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Introduction: High annual tomato yields are achieved using high-tech greenhouse production systems. Large greenhouses typically rely only on one central weather station per compartment to steer their internal climate, ignoring possible microclimate conditions within the greenhouse itself. Methods: In this study, we analysed spatial variation in temperature and vapour pressure deficit in a commercial tomato greenhouse setting for three consecutive years. Multiple sensors were placed within the crop canopy, which revealed microclimate gradients. Results and discussion: Different microclimates were present throughout the year, with seasonal (spring - summer - autumn) and diurnal (day - night) variations in temperature (up to 3 °C, daily average) and vapour pressure deficit (up to 0.6 kPa, daily average). The microclimate effects influenced in part the variation in plant and fruit growth rate and fruit yield - maximum recorded difference between two locations with different microclimates was 0.4 cm d-1 for stem growth rate, 0.6 g d-1 for fruit growth rate, 80 g for truss mass at harvest. The local microclimate effect on plant growth was always larger than the bulk climate variation measured by a central sensor, as commonly done in commercial greenhouses. Quality attributes of harvested tomato fruit did not show a significant difference between different microclimate conditions. In conclusion, we showed that even small, naturally occurring, differences in local environment conditions within a greenhouse may influence the rate of plant and fruit growth. These findings could encourage the sector to deploy larger sensor networks for optimal greenhouse climate control. A sensor grid covering the whole area of the greenhouse is a necessity for climate control strategies to mitigate suboptimal conditions. [ABSTRACT FROM AUTHOR]

Modern agriculture is characterized by the use of smart technology and precision agriculture to monitor crops in real time. The technologies enhance total yields by identifying requirements based on environmental conditions. Plant phenotyping is used in solving problems of basic science and allows scientists to characterize crops and select the best genotypes for breeding, hence eliminating manual and laborious methods. Additionally, plant phenotyping is useful in solving problems such as identifying subtle differences or complex quantitative trait locus (QTL) mapping which are impossible to solve using conventional methods. This review article examines the latest developments in image analysis for plant phenotyping using AI, 2D, and 3D image reconstruction techniques by limiting literature from 2020. The article collects data from 84 current studies and showcases novel applications of plant phenotyping in image analysis using various technologies. AI algorithms are showcased in predicting issues expected during the growth cycles of lettuce plants, predicting yields of soybeans in different climates and growth conditions, and identifying high-yielding genotypes to improve yields. The use of high throughput analysis techniques also facilitates monitoring crop canopies for different genotypes, root phenotyping, and late-time harvesting of crops and weeds. The high throughput image analysis methods are also combined with AI to guide phenotyping applications, leading to higher accuracy than cases that consider either method. Finally, 3D reconstruction and a combination with AI are showcased to undertake different operations in applications involving automated robotic harvesting. Future research directions are showcased where the uptake of smartphone-based AI phenotyping and the use of time series and ML methods are recommended. [ABSTRACT FROM AUTHOR]

Simple crop rotations like maize and soyabean grown in succession often coincide with simple weed management tactics, resulting in weed infestations that are highly adapted to the conditions maintained for the selected crops. Amaranthus tuberculatus is a prominent weed species which possesses biological characteristics that allow it to persist in maize–soyabean rotations. Spring wheat and oats emerge earlier than A. tuberculatus and are grown in narrower rows than maize and soyabean, which may increase their competitive capability. A field study which measured morphological and phenological characteristics of three A. tuberculatus cohorts that emerged under fallow, maize, soyabean, spring wheat and oats was conducted to compare their competitive ability. Early emerged A. tuberculatus grew similarly in fallow, maize and soyabeans and grew to heights of >150 cm but was significantly reduced in spring wheat and oats, never exceeding 40 cm. Heights of the last emerged cohorts were more influenced by crop canopies never reaching 15 cm in spring wheat and oats compared to >70 cm in fallow. Early emerged A. tuberculatus canopy diameter and branches plant−1 were reduced by >80% in spring wheat and oats compared to fallow. Flowering was delayed in spring wheat and oats compared to fallow and seed production and biomass were reduced by 99% across cohorts. Growth suppression of A. tuberculatus, based on the findings, can be achieved under dense, competitive crop canopies such as in spring wheat and oats and these crops should be considered as additional crops in maize–soyabean rotations. [ABSTRACT FROM AUTHOR]

Large-span arch greenhouse has gained widespread application in fruit and vegetable production in recent years, due to the convenient facility operation, high land use efficiency, suitability for multiple coverings, and external heat preservation. However, internal turbulence can often occur in the expansion of the span and height of the large-span arch greenhouse. The resulting uniformity of the greenhouse environment can then affect the efficient production of greenhouse crops. In this study, a multi-field coupled computational fluid dynamics (CFD) model was constructed to integrate the facility structure and tomato plant aerodynamics, according to agricultural industry demand. The research object was selected as the 20 m span large-span arch greenhouse in Laiwu district, Jinan City, Shandong province (36.14° N, 117.28° E), China. A systematic investigation was carried out to explore the influence of the height of the arch greenhouse on the spatial and temporal distribution and evolution of the turbulent structure inside the large-span arch greenhouse. An analysis was also made to clarify the relationship between the wind and the temperature distribution, turbulence intensity, summer ventilation, and cooling efficiency, and the height of the arch greenhouse. Seven kinds of large-span arch greenhouse were established with 4.5, 5.0, 5.5, 6.0, 6.5, 7.0 and 7.5 m heights. The results show that there was turbulent behavior in the large-span arch greenhouses, indicating the mostly double vortex in the section. The air-flow entered the greenhouse via the bottom vent on the windward side, then ascended along the lower side of the greenhouse film, and finally descended towards the roof area. There was a tendency for the stabilization in the position of the inflection point, as the ridge height increased beyond 6.0 m. Therefore, the better uniformity of air flow and temperature distribution were, the higher the uniformity of the greenhouse was. The large-span arch greenhouse with a height of 6.0 m shared the highest average flow velocity at the crop canopy, which was in the appropriate range and relatively high. The average temperatures were also relatively low, which were 0.404 ℃ and 0.026 ℃ lower than those of the arch greenhouse with a height of 5.5 and 6.5 m, respectively. The turbulence intensity of the large-span arch greenhouse with a height of 4.5-5.5 m was significantly higher than that with a height of more than 6.0 m. There was a small difference, after the ridge height exceeded 6.0 m. Once the height of the greenhouse was 4.5 m, the ventilation efficiency was 2.24. After that, the efficiency of ventilation decreased with the increase in the height of the greenhouse. There was a relatively high value when the ridge height reached 6.0 and 6.5 m. The ridge height of 7.0 m corresponded to the lowest ventilation efficiency of 1.60. The large span greenhouse with a 4.5 m ridge height exhibited the higher ventilation efficiency, but the lower air change rate. The large span greenhouse with a 7.5 m ridge height shared the lower air change rate and ventilation efficiency. The outstanding ventilation and cooling performance of the large-span arch greenhouse in summer was determined by many factors. In a 20 m long-span arch greenhouse with shoulder, a better structure was achieved with the ridge height of 6.0-6.5 m and the ridge height of 6.0 m, in order to better consider the factors of economy and safety. The finding can provide a strong reference to optimize a similar greenhouse configuration in the construction of the large-span arch greenhouse. [ABSTRACT FROM AUTHOR]

Introduction: Rapid and accurate estimation of leaf area index (LAI) is of great significance for the precision agriculture because LAI is an important parameter to evaluate crop canopy structure and growth status. Methods: In this study, 20 vegetation indices were constructed by using cotton canopy spectra. Then, cotton LAI estimation models were constructed based on multiple machine learning (ML) methods extreme learning machine (ELM), random forest (RF), back propagation (BP), multivariable linear regression (MLR), support vector machine (SVM)], and the optimal modeling strategy (RF) was selected. Finally, the vegetation indices with a high correlation with LAI were fused to construct the VI-fusion RF model, to explore the potential of multivegetation index fusion in the estimation of cotton LAI. Results: The RF model had the highest estimation accuracy among the LAI estimation models, and the estimation accuracy of models constructed by fusing multiple VIs was higher than that of models constructed based on single VIs. Among the multi-VI fusion models, the RF model constructed based on the fusion of seven vegetation indices (MNDSI, SRI, GRVI, REP, CIred-edge, MSR, and NVI) had the highest estimation accuracy, with coefficient of determination (R2), rootmean square error (RMSE), normalized rootmean square error (NRMSE), and mean absolute error (MAE) of 0.90, 0.50, 0.14, and 0.26, respectively. Discussion: Appropriate fusion of vegetation indices can include more spectral features in modeling and significantly improve the cotton LAI estimation accuracy. This study will provide a technical reference for improving the cotton LAI estimation accuracy, and the proposed method has great potential for crop growth monitoring applications. [ABSTRACT FROM AUTHOR]

Background: Pulses play an important role in Indian agriculture and they are the rich sources of protein. Redgram is an essential food legume, mostly cultivated under rainfed condition in India. Due to increasing population the demand of pulses is increased; hence the redgram productivity has to be increased to meet out the requirement. To overcome this problem an investigation was carried out to study the effect of crop geometry, modified canopy architecture and 1% PPFM application on morpho-physiological and biochemical responses of rainfed redgram during north-east monsoon season. Methods: The treatments comprised of varied crop geometries viz, broad casting (farmer’s practice), high density planting at 30 × 30 cm and recommended spacing of 60 × 30 cm; modified canopy architectures viz. nipping of primary branches at 45 and 60 days after the receipt of soaking rain and foliar application of 1% PPFM spray at 50% flowering to avoid the moisture stress. Result: The results revealed that sowing at 60 × 30 cm spacing with nipping at 45th day recorded significantly higher seed yield and protein yield of redgram in rainfed condition. Further, nipping at 45th day showed higher level of relative water content (51.6%) associated with lower level of proline (26.61 mg g-1). [ABSTRACT FROM AUTHOR]

Greenhouse is one kind of protected cultivation facility with plastic film as the covering material and bamboo or steel frame as the supporting structure. Solar radiation is one of the most important influencing factors on the thermal energy environment inside the greenhouses. Only the greenhouse effect without heating can make the internal temperature higher than the outside in the cold regions. The solar radiation received by a greenhouse is strongly related to some parameters, such as geographical location, climatic conditions, orientation, and shape. Therefore, it is very necessary to optimize the shape, orientation, and size of greenhouses, in order to maximize the use of winter solar radiation and then effectively reduce winter heating requirements and operating costs. The optimal shape and orientation of greenhouses can vary slightly in the height of the crop canopy, such as fruit, leafy vegetables, and flowers. However, it is still lacking to consider the canopy height of the crops in the greenhouse structure, according to the capture of solar radiation on the lighting surfaces. In this study, a novel model was developed to optimize the ridge-shape greenhouse using canopy height and constant volume constraints. The amount of solar radiation was also maximized to capture the lighting surface and the effective planting area. A systematic analysis was implemented to explore the influence of the greenhouse height ratio and azimuth angle on solar radiation capture under the constant internal energy demand. The optimal combination of parameters was achieved in the different ridge ratios and regions. More light was harvested and converted at the expense of the greenhouse into heat stored in the greenhouse, thus reducing night-time heating costs. A comparative analysis was also made to verify the calculated and measured values of the solar radiation model. The results show that the solar radiation captured by the greenhouses was positively correlated with the height and ridge ratio. The difference in solar radiation with different ridge ratios decreased with the decrease in height ratio. The amount of solar radiation captured inside the greenhouse increased with the decrease of regional latitude at the same height ratio. Most solar radiation showed a decreasing and then increasing trend with the increase of azimuth angle. The preferred height ratios in different regions were related only to the height of the crop canopy. The higher the latitude under the same conditions was, the greater the ridge ratio and canopy height were. The greater angle of azimuth was preferably obtained by the greenhouse deflected with respect to the north-south direction. This finding can improve the thermal insulation and heat storage with heating cost savings in the greenhouse. A theoretical basis can also offer to advance the overall planning and development of greenhouses. [ABSTRACT FROM AUTHOR]

Precise monitoring of individual crop growth and health status is crucial for precision agriculture practices. However, traditional inspection methods are time-consuming, labor-intensive, prone to human error, and may not provide the comprehensive coverage required for the detailed analysis of crop variability across an entire field. This research addresses the need for efficient and high-resolution crop monitoring by leveraging Unmanned Aerial Vehicle (UAV) imagery and advanced computational techniques. The primary goal was to develop a methodology for the precise identification, extraction, and monitoring of individual corn crops throughout their growth cycle. This involved integrating UAV-derived data with image processing, computational geometry, and machine learning techniques. Bi-weekly UAV imagery was captured at altitudes of 40 m and 70 m from 30 April to 11 August, covering the entire growth cycle of the corn crop from planting to harvest. A time-series Canopy Height Model (CHM) was generated by analyzing the differences between the Digital Terrain Model (DTM) and the Digital Surface Model (DSM) derived from the UAV data. To ensure the accuracy of the elevation data, the DSM was validated against Ground Control Points (GCPs), adhering to standard practices in remote sensing data verification. Local spatial analysis and image processing techniques were employed to determine the local maximum height of each crop. Subsequently, a Voronoi data model was developed to delineate individual crop canopies, successfully identifying 13,000 out of 13,050 corn crops in the study area. To enhance accuracy in canopy size delineation, vegetation indices were incorporated into the Voronoi model segmentation, refining the initial canopy area estimates by eliminating interference from soil and shadows. The proposed methodology enables the precise estimation and monitoring of crop canopy size, height, biomass reduction, lodging, and stunted growth over time by incorporating advanced image processing techniques and integrating metrics for quantitative assessment of fields. Additionally, machine learning models were employed to determine relationships between the canopy sizes, crop height, and normalized difference vegetation index, with Polynomial Regression recording an R-squared of 11% compared to other models. This work contributes to the scientific community by demonstrating the potential of integrating UAV technology, computational geometry, and machine learning for accurate and efficient crop monitoring at the individual plant level. [ABSTRACT FROM AUTHOR]

The aim of this study was to overcome the impact of vibration generated by agricultural machinery on the monitoring accuracy and performance of vehicle-mounted crop growth monitoring systems during field operation. This paper developed a vehicle-mounted crop growth monitoring system with vibration damping capability to achieve this goal. The system consists of a multispectral crop growth sensor, signal conditioning module, and truss-type sensor bracket with self-vibration damping capability. The commercial finite element analysis software ABAQUS 6.10 was used to conduct modal and dynamic simulation analyses of the sensor bracket, which indicate that the truss-type sensor bracket can damp vibrations effectively. The p-values (least significant differences) of crop canopy DNRE (red edge normalized difference vegetation index) under different operating speeds (1.5, 3, and 4.5 km/h) are 0.454, 0.703, 0.81, and 0.838, respectively, for four different crop growth stages. In a comparative experiment between the proposed monitoring system and two similar vehicle-mounted sensors (CropSpec and GreenSeeker RT 200) for measuring agronomic parameters at different stages of crop growth, the proposed monitoring system yielded R2 values of 0.8757, 0.7194, and 0.795, respectively, and RMSE values of 0.7157, 2.2341, and 2.0952, respectively, in the tillering stage, jointing stage, and tillering and jointing stage, outperforming the other two sensors. [ABSTRACT FROM AUTHOR]

Computational fluid dynamics (CFD) simulations have been extensively used in designing air distribution systems for controlled environment agriculture (CEA). In recent years, more application studies using CFD simulations can be found for vertical farms due to the increasing interest in indoor vertical farming systems. However, it is well-known that CFD simulations are sensitive to many computational parameters and settings. The requirement of a crop response model in the CFD simulation for a vertical farm makes it even more complicated. Despite increased interest, guidelines for CFD simulations in vertical farms are scarce based on a literature study. Therefore, a systematic sensitivity analysis is conducted for a small generic multi-layer vertical farm with sole source lighting, which was the object of study in the literature before. The impact of a wide range of computational and physical parameters is investigated, including grid resolution, turbulence model, turbulence intensity, discretisation scheme, drag coefficient of the crops and computational time. The analysis shows that in this case (inlet Re = 46,923, Ar = 0.078, cultivated with lettuce), the RNG k-ε turbulence model outperforms other commonly used two-equation turbulence models. Compared to the experimental results from the literature, the simulation results from the first-order upwind scheme show large discrepancies, especially on the coarse grid. Although the influence of drag coefficient on the airflow inside the crop canopy is pronounced, little difference is observed in the air distributions in the vertical farm away from the crops. • Literature review on computational settings in CFD simulations of indoor farming. • Validation of RANS CFD simulations including a crop response model. • RNG k-ε turbulence model outperforms the other two-equation turbulence models. • First-order upwind discretisation scheme shows large discrepancies. • Influence of the momentum sink of lettuce canopy on the air distributions is limited. [ABSTRACT FROM AUTHOR]

Crop canopy temperature has proven beneficial for qualitative and quantitative assessment of plants' biotic and abiotic stresses. In this two-year study, water stress identification in okra crops was evaluated using thermal–RGB imaging and AI approaches. Experimental trials were developed for two irrigation types, sprinkler and flood, and four deficit treatment levels (100, 50, 75, and 25% crop evapotranspiration), replicated thrice. A total of 3200 thermal and RGB images acquired from different crop stages were processed using convolutional neural network architecture-based deep learning models (1) ResNet-50 and (2) MobileNetV2. On evaluation, the accuracy of water stress identification was higher with thermal imagery inputs (87.9% and 84.3%) compared to RGB imagery (78.6% and 74.1%) with ResNet-50 and MobileNetV2 models, respectively. In addition, irrigation treatment and levels had significant impact on yield and crop water use efficiency; the maximum yield of 10,666 kg ha−1 and crop water use efficiency of 1.16 kg m−3 was recorded for flood irrigation, while 9876 kg ha−1 and 1.24 kg m−3 were observed for sprinkler irrigation at 100% irrigation level. Developments and observations from this study not only suggest applications of thermal–RGB imagery with AI for water stress quantification but also developing and deploying automated irrigation systems for higher crop water use efficiency. [ABSTRACT FROM AUTHOR]

Most of the current crop row detection algorithms focus on extracting crop canopy rows as location information. However, for some high-pole crops, due to the transverse deviation of the position of the canopy and roots, the agricultural machinery can easily cause the wheel to crush the crop when it is automatically driven. In fact, it is more accurate to use the crop root row as the feature for its location calibration, so a method of crop root row detection is proposed in this paper. Firstly, the ROI (region of interest) of the crop canopy is extracted by a semantic segmentation algorithm, then crop canopy row detection lines are extracted by the horizontal strip division and the midpoint clustering method within the ROI. Next, the Crop Root Representation Learning Model learns the Representation of the crop canopy row and crop root row to obtain the Alignment Equation. Finally, the crop canopy row detection lines are modified according to the Alignment Equation parameters to obtain crop root row detection lines. The average processing time of a single frame image (960 × 540 pix) is 30.49 ms, and the accuracy is 97.1%. The research has important guiding significance for the intelligent navigation, tilling, and fertilization operation of agricultural machinery. [ABSTRACT FROM AUTHOR]

Accurate, non-destructive and cost-effective estimation of crop canopy Soil Plant Analysis De-velopment(SPAD) is crucial for precision agriculture and cultivation management. Unmanned aerial vehicle (UAV) platforms have shown tremendous potential in predicting crop canopy SPAD. This was because they can rapidly and accurately acquire remote sensing spectral data of the crop canopy in real-time. In this study, a UAV equipped with a five-channel multispectral camera (Blue, Green, Red, Red_edge, Nir) was used to acquire multispectral images of sugar beets. These images were then combined with five machine learning models, namely K-Nearest Neighbor, Lasso, Random Forest, RidgeCV and Support Vector Machine (SVM), as well as ground measurement data to predict the canopy SPAD of sugar beets. The results showed that under both normal irrigation and drought stress conditions, the SPAD values in the normal ir-rigation treatment were higher than those in the water-limited treatment. Multiple vegetation indices showed a significant correlation with SPAD, with the highest correlation coefficient reaching 0.60. Among the SPAD prediction models, different models showed high estimation accuracy under both normal irrigation and water-limited conditions. The SVM model demon-strated a good performance with a correlation coefficient (R2) of 0.635, root mean square error (Rmse) of 2.13, and relative error (Re) of 0.80% for the prediction and testing values under normal irrigation. Similarly, for the prediction and testing values under drought stress, the SVM model exhibited a correlation coefficient (R2) of 0.609, root mean square error (Rmse) of 2.71, and rela-tive error (Re) of 0.10%. Overall, the SVM model showed good accuracy and stability in the pre-diction model, greatly facilitating high-throughput phenotyping research of sugar beet canopy SPAD. [ABSTRACT FROM AUTHOR]

Canopy height serves as an important dynamic indicator of crop growth in the decision-making process of field management. Compared with other commonly used canopy height measurement techniques, ultrasonic sensors are inexpensive and can be exposed in fields for long periods of time to obtain easy-to-process data. However, the acoustic wave characteristics and crop canopy structure affect the measurement accuracy. To improve the ultrasonic sensor measurement accuracy, a four-year (2018-2021) field experiment was conducted on maize and wheat, and a measurement platform was developed. A series of single-factor experiments were conducted toinvestigate the significant factors affectingmeasurements, including the observation angle (0-60°), observation height (0.5-2.5 m), observation period (8:00 -18:00), platform moving speed with respect to the crop (0-2.0 m min-1), planting density (0.2-1 time of standard planting density), and growth stage (maize from three -leaf to harvest period and wheat from regreening to maturity period). The results indicated that both the observation angle and planting density significantly affected the results of ultrasonic measurements (p-value< 0.05), whereas the effects of other factors on measurement accuracy were negligible (p-value > 0.05). Moreover, a double-input factor calibrationmodelwasconstructed to assess canopy heightunder different years by utilizing the normalized difference vegetation index and ultrasonic measurements. The model was developed by employing the least-squares method, and ultrasonic measurement accuracy was significantly improved when integrating themeasured value of canopy heights and the normalized difference vegetation index (NDVI). The maize measurement accuracy had a root mean squared error (RMSE) ranging from 81.4 mm to 93.6 mm, while the wheat measurement accuracy had an RMSE from 37.1mmto 47.2mm. The research results effectively combine stable and low-cost commercial sensors with ground-based agricultural machinery platforms, enabling efficient and non-destructive acquisition of crop height information. [ABSTRACT FROM AUTHOR]

In recent years, the utilization of machine learning algorithms and advancements in unmanned aerial vehicle (UAV) technology have caused significant shifts in remote sensing practices. In particular, the integration of machine learning with physical models and their application in UAV–satellite data fusion have emerged as two prominent approaches for the estimation of vegetation biochemistry. This study evaluates the performance of five machine learning regression algorithms (MLRAs) for the mapping of crop canopy chlorophyll at the Kellogg Biological Station (KBS) in Michigan, USA, across three scenarios: (1) application to Landsat 7, RapidEye, and PlanetScope satellite images; (2) application to UAV–satellite data fusion; and (3) integration with the PROSAIL radiative transfer model (hybrid methods PROSAIL + MLRAs). The results indicate that the majority of the five MLRAs utilized in UAV–satellite data fusion perform better than the five PROSAIL + MLRAs. The general trend suggests that the integration of satellite data with UAV-derived information, including the normalized difference red-edge index (NDRE), canopy height model, and leaf area index (LAI), significantly enhances the performance of MLRAs. The UAV–RapidEye dataset exhibits the highest coefficient of determination (R2) and the lowest root mean square errors (RMSE) when employing kernel ridge regression (KRR) and Gaussian process regression (GPR) (R2 = 0.89 and 0.89 and RMSE = 8.99 µg/cm2 and 9.65 µg/cm2, respectively). Similar performance is observed for the UAV–Landsat and UAV–PlanetScope datasets (R2 = 0.86 and 0.87 for KRR, respectively). For the hybrid models, the maximum performance is attained with the Landsat data using KRR and GPR (R2 = 0.77 and 0.51 and RMSE = 33.10 µg/cm2 and 42.91 µg/cm2, respectively), followed by R2 = 0.75 and RMSE = 39.78 µg/cm2 for the PlanetScope data upon integrating partial least squares regression (PLSR) into the hybrid model. Across all hybrid models, the RapidEye data yield the most stable performance, with the R2 ranging from 0.45 to 0.71 and RMSE ranging from 19.16 µg/cm2 to 33.07 µg/cm2. The study highlights the importance of synergizing UAV and satellite data, which enables the effective monitoring of canopy chlorophyll in small agricultural lands. [ABSTRACT FROM AUTHOR]

Studying the effect of variable fertilization during the elongation stage on winter wheat production in the rice-wheat rotation area is critical for evaluating the application effect and economic benefits of variable fertilization technology. The variable fertilization experiment of wheat was carried out in Jiangsu province by using the self-developed fertilizer applicator. Three fertilization methods were used to conduct a comparative analysis of the fertilization amount, population structure, and yield of winter wheat during the elongation stage. On this basis, the economic feasibility of variable fertilization during the elongation stage was evaluated. The experimental results showed that the control accuracy of variable-rate fertilization with fertilization equipment was greater than 95%. After variable fertilization, the coefficient of variation of Normalized Difference Vegetation Index (NDVI) values in the winter wheat crop canopy spectral data remained between 0.076 and 0.125, and the Christensen uniformity coefficient remained between 0.901 and 0.940. Compared with the traditional empirical balance method for quantitative fertilization of plots, the real-time variable fertilization plot used 13.6 kg/ha less fertilizer during the elongation stage. The findings validate that implementing variable fertilization can help reduce nitrogen fertilizer input, improve nitrogen fertilizer utilization efficiency, reduce environmental pollution, and enhance the sustainability of agricultural production. [ABSTRACT FROM AUTHOR]

The application of diffusive and reflective polyethylene (PE) films as greenhouse coverings in arid climates presents an opportunity to improve the microclimate of the greenhouse and achieve consistent light distribution within the crop canopy. Nevertheless, there is still a lack of understanding regarding the properties of these covers and their impact on the microclimate and the growth parameters of crops. This study aimed to assess the impact of different covers on the diffusion of beam radiation during transmission, microclimatic parameters, and growth parameters of cucumbers in each of the greenhouses they covered. In the study, three PE covers were evaluated: a reflective cover (RC), a diffusive film (DC), and a locally produced cover (LPC) as the control treatment. The covers were installed on three identical, single-span, evaporatively cooled greenhouses named GH1/LPC, GH2/RC, and GH3/DC, which were utilized for cultivating cucumber crops. The results indicated that the diffusive nature of the tested films increased the ratio of diffuse to global solar radiation (D/G) from 0.22 outside the greenhouses to 0.49, 0.42, and 0.41 inside GH1/LPC, GH3/DC, and GH2/RC, respectively. Similarly, the ratio of diffuse to direct beam radiation (D/B) showed an increase, with values of 0.95, 0.70, and 0.68 inside GH1/LPC, GH3/DC, and GH2/RC, respectively, compared to the outside value of 0.28. The DC used in GH3 showed a favorable microclimate by reducing the air temperature and improving the relative humidity. Accordingly, the vegetative growth of the cucumbers was significantly improved in GH3/DC, reflected in increases in their biomass, followed by GH2/RC and GH1/LPC. The highest crop yield (p ≤ 0.05) of 12.3 kg/m2 was achieved in GH3/DC, followed by 10.2 kg/m2 in GH2/RC and 10.1 kg/m2 in GH1/LPC. Interestingly, the LPC not only stood out as a low-cost option but also displayed excellent diffusive–radiative properties, and demonstrated reasonable growth development and productivity for the cucumber crops. Consequently, the LPC emerges as a practical and cost-effective greenhouse covering material for crop production in arid climates. [ABSTRACT FROM AUTHOR]

To effectively manage crop production in a greenhouse, it is essential to understand the natural light environment and physiological responses of the plants to light. This study investigated the dynamics of photosynthetic photon flux densities (PPFD) and light quality within the canopies of greenhouse-grown eggplant (Solanum melongena) and the photosynthetic capacities of leaves at different locations within the canopies. The light environment was quantified at 0.2-m intervals within (intracanopy) and adjacent to (extra-canopy) the crop canopy on both sunny and cloudy days within a commercial greenhouse located in Leamington, Ontario, Canada. Our results indicated a linear decline in extra-canopy PPFD on both sunny and cloudy days, but an exponential decrease in intra-canopy PPFD. The intra-canopy PPFD decreased by 91% and 76% between 0 m and 0.4 m from the canopy apex on sunny and cloudy days, respectively. The lower canopy (0.6–1.2 m) light spectrum consisted largely of far-red light, equal amounts of red light and green light, with a lower percentage of blue light. Parameters derived from leaf-level light response curves indicated that the light-saturated net carbon exchange rate, light saturation point, and light compensation point decreased as the distance from canopy apex increased, whereas quantum yield was unaffected. Thus, leaves in the lower canopy were less efficient at using high PPFD, but they displayed no deterioration of photosynthetic machinery. Based solely on photosynthetic capabilities, leaves between 0 and 1.0 m from the canopy apex should not be removed to decrease the total plant sink strength. [ABSTRACT FROM AUTHOR]

The New Zealand bottlebrush, Metrosideros excelsa (Myrtaceae), was introduced into South Africa in the 1940s as an ornamental plant and subsequently naturalized in coastal parts of the Cape Floristic Region. Knowledge of M. excelsa's propagule pressure and response to fire in South Africa can inform evaluation of the species' invasion potential and management. We assessed M. excelsa's canopy and soil seed banks in burnt and unburnt stands; and post‐fire regeneration via reseeding and resprouting in relation to fire severity and tree size. Soil seed banks were assessed using an emergence technique, but no seedlings emerged from soil collected under burnt or unburnt plants suggesting that the species does not maintain a viable soil‐stored seed bank or germination triggers were not met (though unlikely). The annual seed crop in the canopies of unburnt trees was approximately 0.5–23 million seeds per tree. Viability, assessed through tetrazolium stain testing, of canopy‐borne seeds on unburnt trees was 18%, whereas a canopy fire, ranging in severity from low to extreme, killed all canopy‐borne seeds. Fifteen months post‐fire, the seedling to pre‐fire (live) tree ratio was 0.01, whereas 43% of burnt trees survived via basal resprouting. Fire severity had a non‐linear effect on tree survival (survival was highest after medium and high fire severity and lowest after low and extreme fire severity), while larger trees were more likely to survive fire. These results suggest that even low‐severity (safe) burning may be a useful control measure as it kills canopy‐borne seeds and causes substantial mortality, particularly of smaller trees. An opportunistic evaluation of an uncontrolled (no comparison with untreated individuals) foliar herbicide application to post‐fire resprouting individuals also showed considerable (91%) mortality. However, rigorous herbicide trials (also with herbicide in conjunction with burning) are required to inform the management of M. excelsa. [ABSTRACT FROM AUTHOR]

Mulch regulates soil hydrothermal regime while suppressing weed growth, effects of which may depend on mulch material and/or similar to that of closed crop canopy. Combined use of organic or inorganic material as surface mulch and appropriate plant spacing may produce synergistic agronomic effects in water-stressed environments. This study evaluated the sensitivity of rainfed okra to mulch-spacing variants in droughty derived-savannah soils. With dry-grass mulch (DGM) and sawdust mulch (SDM) representing organic mulch material and black polythene (BPM) as inorganic/plastic, treatments were factorial combinations of surface-applied DGM, SDM and BPM with close (60 cm × 30 cm), intermediate (60 cm × 45 cm) and wide (60 cm × 60 cm) spacings. Crop growth/performance and fruit yield data were collected. Mulch material affected okra growth (with overall better results in BPM than SDM), but not flowering/fruiting and fruit yield. For plant spacing, sum of weights of pods and fruit yield were higher in close than intermediate and wide spacings, with fruit yields as 2.42, 1.21 and 0.76 t ha-1, respectively. Mulch-spacing interaction effects showed that BPM with close spacing gave the highest values for stem girth 8 weeks after sowing (SDM treatments gave the lowest), sum of weights of pods and fruit yield (2.93 t ha-1). Fruit yield, however, depended not on stem girth but on certain okra yield attributes. The BPM or, where not feasible, either of DGM and SDM, could be effectively combined with close spacing to improve okra productivity in droughty soils of the derived savannah. [ABSTRACT FROM AUTHOR]

Aphid infestations are one of the primary causes of extensive damage to wheat and sorghum fields and are one of the most common vectors for plant viruses, resulting in significant agricultural yield losses. To address this problem, farmers often employ the inefficient use of harmful chemical pesticides that have negative health and environmental impacts. As a result, a large amount of pesticide is wasted on areas without significant pest infestation. This brings to attention the urgent need for an intelligent autonomous system that can locate and spray sufficiently large infestations selectively within the complex crop canopies. We have developed a large multi-scale dataset for aphid cluster detection and segmentation, collected from actual sorghum fields and meticulously annotated to include clusters of aphids. Our dataset comprises a total of 54,742 image patches, showcasing a variety of viewpoints, diverse lighting conditions, and multiple scales, highlighting its effectiveness for real-world applications. In this study, we trained and evaluated four real-time semantic segmentation models and three object detection models specifically for aphid cluster segmentation and detection. Considering the balance between accuracy and efficiency, Fast-SCNN delivered the most effective segmentation results, achieving 80.46% mean precision, 81.21% mean recall, and 91.66 frames per second (FPS). For object detection, RT-DETR exhibited the best overall performance with a 61.63% mean average precision (mAP), 92.6% mean recall, and 72.55 on an NVIDIA V100 GPU. Our experiments further indicate that aphid cluster segmentation is more suitable for assessing aphid infestations than using detection models. [ABSTRACT FROM AUTHOR]

Accurate retrieval of canopy nutrient content has been made possible using visible-to-shortwave infrared (VSWIR) imaging spectroscopy. While this strategy has often been tested on closed green plant canopies, little is known about how nutrient content estimates perform when applied to pixels not dominated by photosynthetic vegetation (PV). In such cases, contributions of bare soil (BS) and non-photosynthetic vegetation (NPV), may significantly and nonlinearly reduce the spectral features relied upon for nutrient content retrieval. We attempted to define the loss of prediction accuracy under reduced PV fractional cover levels. To do so, we utilized VSWIR imaging spectroscopy data from the Global Airborne Observatory (GAO) and a large collection of lab-calibrated field samples of nitrogen (N) content collected across numerous crop species grown in several farming regions of the United States. Fractional cover values of PV, NPV, and BS were estimated from the GAO data using the Automated Monte Carlo Unmixing algorithm (AutoMCU). Errors in prediction from a partial least squares N model applied to the spectral data were examined in relation to the fractional cover of the unmixed components. We found that the most important factor in the accuracy of the partial least squares regression (PLSR) model is the fraction of photosynthetic vegetation (PV) cover, with pixels greater than 60% cover performing at the optimal level, where the coefficient of determination ( R 2 ) peaks to 0.66 for PV fractions of more than 60% and bare soil (BS) fractions of less than 20%. Our findings guide future spaceborne imaging spectroscopy missions as applied to agricultural cropland N monitoring. [ABSTRACT FROM AUTHOR]

Electrostatic spraying technology can improve the efficiency of pesticide deposition on the surface of leaves and reduce the environmental pollution caused by pesticide drift, which has an important prospect in agricultural pesticide application. To improve the deposition and penetration of droplets in the crop canopy, we designed and optimized an air-assisted electrostatic nozzle and conducted the spraying performance experiment. Parameters, such as charge-to-mass ratio (CMR) and particle size, were tested and analyzed to obtain the suitable operating parameters of nozzle. The results proved that the improved air-assisted electrostatic nozzle has good atomization and chargeability. There is a good charging effect with a charging voltage of 3,000-5,000 V, the CMR increased 127.8% from 0.86 to 1.97 mC/kg as the charge voltage increases from 1,000 to 4,000 V, at an air pressure of 1.0 bar and liquid flow rate of 200 ml/min. Furthermore, we designed a multi-factor orthogonal experiment, which was conducted using a four-factor, three-level design to investigate the effects of operational parameters and canopy characteristics on droplet deposition and penetration. Analysis of variance (ANOVA) and F-test were performed on the experiment results. The results showed that the factor effect on droplet penetration, in descending order, was as follows: spray distance, leaf area index, air pressure, and air pressure × spray distance. The factor effect on abaxial leaf deposition, in descending order, was as follows: air pressure, spray distance, air pressure × charge voltage, spray distance × charge voltage, and charge voltage. For optimal droplet penetration and abaxial leaf deposition, option A3B1D2 (air pressure 1.5 bar, spray distance 0.2 m, charge voltage 2,500 V) is recommend. The spray nozzle atomization performance and deposition regulation were studied by experimental methods to determine the optimal values of operating parameters to provide a reference for electrostatic spray system development. [ABSTRACT FROM AUTHOR]

To enhance the utilization of solar energy in Chinese solar greenhouses (CSGs), a new method for optimizing the internal lighting environment of CSGs using reflective films is proposed. The influence of different positions and angles of reflecting film on solar radiation in greenhouses was studied, using the solar radiation on the inside surface of the CSG as an evaluation index. According to the findings, total solar radiation increased by 5.33% when the reflective film was positioned on the north roof at an angle of 0°. The light interception on the north wall decreased from 7.91% to 10.54% when the angle was raised from 15° to 25°. The crop canopy was not significantly affected by the reflective film's various placements and angles, and the benefits of additional light were insufficient to compensate for the drawbacks of crop shading. This result provides a theoretical basis for the application of reflective films in relevant agricultural facilities. Reasonable installation of reflective film in the greenhouse can increase the light interception of plants inside the greenhouse and further increase the income of farmers. [ABSTRACT FROM AUTHOR]

When the model is applied to a new area, the first thing to consider is the problem of localization correction, and parameter sensitivity analysis is an important part of it. In this study, the AquaCrop model was used to simulate the growth process of tea trees, and the parameter sensitivity of the model was tested by the method of EFAST. The results show that the crop parameters with high sensitivity to AGB are mainly KCB, wp, Tbase, den, and Hi; the crop parameters with high sensitivity to CC are mainly CGC, den, Tbase, mcc, wp, and KCB; the crop parameters with high sensitivity to Y are mainly The main crop parameters are Hi, den, wp, KCB, CGC, and Tbase. Overall, the AquaCrop model is mainly affected by the crop canopy coefficient, phenological development coefficient, biomass coefficient, and yield coefficient. Considering that sensitive parameters greatly influence the output, the sensitive parameters should be calibrated first, and the insensitive parameters can be set to fixed values to reduce the workload. More attention should be paid to the interaction between parameters when studying crop models. This helps to correctly understand the structure of the crop model and the role of each parameter, achieve an accurate simulation of the growth process, and improve the simulation accuracy. [ABSTRACT FROM AUTHOR]

Crop leaf chlorophyll content (LCC) and fractional vegetation cover (FVC) are crucial indicators for assessing crop health, growth development, and maturity. In contrast to the traditional manual collection of crop trait parameters, unmanned aerial vehicle (UAV) technology rapidly generates LCC and FVC maps for breeding materials, facilitating prompt assessments of maturity information. This study addresses the following research questions: (1) Can image features based on pretrained deep learning networks and ensemble learning enhance the estimation of remote sensing LCC and FVC? (2) Can the proposed adaptive normal maturity detection (ANMD) algorithm effectively monitor maize maturity based on LCC and FVC maps? We conducted the following tasks: (1) Seven phases (tassel initiation to maturity) of maize canopy orthoimages and corresponding ground-truth data for LCC and six phases of FVC using UAVs were collected. (2) Three features, namely vegetation indices (VI), texture features (TF) based on Gray Level Co-occurrence Matrix, and deep features (DF), were evaluated for LCC and FVC estimation. Moreover, the potential of four single-machine learning models and three ensemble models for LCC and FVC estimation was evaluated. (3) The estimated LCC and FVC were combined with the proposed ANMD to monitor maize maturity. The research findings indicate that (1) image features extracted from pretrained deep learning networks more accurately describe crop canopy structure information, effectively eliminating saturation effects and enhancing LCC and FVC estimation accuracy. (2) Ensemble models outperform single-machine learning models in estimating LCC and FVC, providing greater precision. Remarkably, the stacking + DF strategy achieved optimal performance in estimating LCC (coefficient of determination (R2): 0.930; root mean square error (RMSE): 3.974; average absolute error (MAE): 3.096); and FVC (R2: 0.716; RMSE: 0.057; and MAE: 0.044). (3) The proposed ANMD algorithm combined with LCC and FVC maps can be used to effectively monitor maize maturity. Establishing the maturity threshold for LCC based on the wax ripening period (P5) and successfully applying it to the wax ripening-mature period (P5–P7) achieved high monitoring accuracy (overall accuracy (OA): 0.9625–0.9875; user's accuracy: 0.9583–0.9933; and producer's accuracy: 0.9634–1). Similarly, utilizing the ANMD algorithm with FVC also attained elevated monitoring accuracy during P5–P7 (OA: 0.9125–0.9750; UA: 0.878–0.9778; and PA: 0.9362–0.9934). This study offers robust insights for future agricultural production and breeding, offering valuable insights for the further exploration of crop monitoring technologies and methodologies. [ABSTRACT FROM AUTHOR]

The self-propelled sprinkler irrigation machine is an extended irrigation system that has reduced its energy dependency in the last twenty years by replacing impact sprinklers with low-pressure emitters. The reduction in pressure operation changes the characteristics of the drop population and its impact on the soil. The crop canopy can also modify the energy reaching the soil. This study aims to contribute to the understanding of water partitioning, drop characteristics and energy impacting the soil in a growing maize canopy irrigated with a sprinkler machine. The results may provide valuable insights for optimizing irrigation practices and managing potential soil erosion risks in sprinkler irrigated maize fields. Measurements were performed in a field experiment, above and below the canopy at different maize heights. All measurements were taken at two points with two nozzle sizes (4.58 and 6.75 mm). Water partitioning and drop characteristics change as the canopy grows. The amount of throughfall decreases as the maize grows. An increase in the proportion of the smallest (< 1 mm) and largest (3–4 mm) drop diameters was measured as one moved deeper into the canopy. For a completely developed canopy, a reduction in specific power reaching the soil was observed for the 6.75 mm nozzle, but both a decrease and an increase were measured for the 4.58 mm nozzle, depending on the measurement location. The highest percentage of stem flow associated with the largest nozzle resulted in a greater protective effect of the canopy compared to the smallest nozzle. The balance between throughfall reduction, dripping, and splashing determines the ability of the canopy to mitigate soil impact energy. This study advances previous works on sprinkler irrigation by adding the variation of drop population characteristics not only as a function of canopy height but also as a function of irrigation time. In addition, this study incorporates the characterization of water application with irrigation machine emitters, which produce different drop populations than sprinkler solid-sets, allowing for a comparison between both systems. [ABSTRACT FROM AUTHOR]

Crop canopy optical reflectance and ultrasonic sensors provide a means of estimating the spatial variability of biomass and nitrogen uptake by sugarcane during the in-season period. The objective of this paper is to assess the crop canopy reflectance and ultrasonic crop height for predicting sugarcane biomass and N-uptake until the later fertilization stages. An ultrasonic sensor was deployed to measure canopy height, which were combined with optical reflectance sensor to characterize the spatial variability of the crop growth in four commercial fields in southeast Brazil during three different growing stages for dry and wet seasons. Ten sampling location points in each field were defined to determine plant biomass and N-uptake through traditional measurements. The points in each field were used to relate the actual biomass and N-uptake with the sensor data and compare them using the coefficient of determination and standard errors; this defined the best approach in each situation according to the multivariable statistics. It was found that both sensor systems enable to correlate its data with sugarcane biomass and N-uptake. Canopy reflectance sensor produced a better assessment of crop growth at the earlier growth stage whereas the ultrasonic sensor resulted in more accurate predictions at the later growing stages. It was proven that canopy height is season dependent whereas the reflectance data is growth stage dependent. The integration of both sensing systems improved the predictions of sugarcane biomass and N-uptake. It could be an alternative to guide local interventions by sugarcane industry during the growing season. [ABSTRACT FROM AUTHOR]

Sustainable agricultural growth and management reduces over-utilization of farm resources and squeezes risk of negative impacts on environment. Monitoring continuous crop growth and health under various conditions at different spatio-temporal resolutions is a key to assess yield stability, crop diversity, adaptability, mitigation for stress and response. The quantification of crop biophysical and biochemical variables spatially from remotely sensed data with help of spectroscopic methods provide a reliable discerning information in the context of crop foliar condition like leaf greenness, senescence, canopy density, crop growth, stress and eco-physiological processes. Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) airborne hyperspectral data offers high spatial and spectral resolution giving a unique advantage and opportunity to test retrievals of crop biophysical (BP)-biochemical (BC) variables under varying conditions over different types of crops. A hybrid inversion of leaf-canopy Radiative Transfer model – PROSAIL-D complemented with use of data driven nonlinear non-parametric methods which offer simplicity, fastness, reliability and competency is a powerful method to retrieve crop biophysical and biochemical variables. The Hyperspectral band (feature) selection is a computationally cost efficient method to overcome data redundancy in high dimensional correlated input spectral bands. The determination of optimum subset or combination of hyperspectral bands specific to retrieval of vegetation properties (including canopy effects) determined using feature selection algorithm for regression-based retrievals are active research topic unlike classification problems in which it is more common. A band selection algorithm based on Gaussian Processes Regression was used to choose most sensitive bands from in-situ biophysical-biochemical measurements and crop spectral signatures collected for analysis in two different agricultural regions: Raichur (Karnataka) and Anand (Gujarat) districts of India representing diverse landscapes, heterogeneous crop canopies and agro climatic settings. The retrieval algorithm for AVIRIS-NG image employed a decision tree ensemble algorithm Canonical Correlation Forests using the optimum subset of bands for retrieval of targeted crop variable. Validation of retrieved crop variables were done using in-situ ground observations collected over heterogeneous diverse crop landscape. The results showed chlorophyll (RMSE = 6.61 µg cm−2), equivalent water thickness (RMSE = 0.002 cm), leaf area index (RMSE = 0.35 m2/m−2) and dry matter (RMSE = 0.003 g cm−2), carotenoid (RMSE = 14.3 µg cm−2), anthocyanin (RMSE = 12.92 µg cm−2) retrieved with better accuracies. The results obtained in context of feature (band) selection approach for Radiative Transfer model inversion show overlapping of sensitive spectral bands of chlorophyll-ab, carotenoid and anthocyanin especially between narrow spectral range 480 to 560 nm as predominant reason for decreased accuracies of anthocyanin and carotenoid compared to other variable. This poses a limitation as well as opportunity for further research in signal separation in context of feature selection approach especially in context of broader spectral bandwidths. It also sets ground for further development of feature selection algorithms that use hybrid regression methods customized for crop specific traits. [ABSTRACT FROM AUTHOR]

Canopy interception affects the effective rainfall for plant growth. Extensive studies of canopy interception have focused on trees, but few on crops, due to the longer canopy duration of trees. However, overlooking the canopy rainfall interception results in an overestimate of effective water for crop growth and development. It is still unclear how crop canopy interception is influenced. In this study we examined the effect of leaf area index (LAI) and rainfall characteristics on soybean canopy interception. The results showed that the LAI, rainfall intensity and rainfall duration were the most relevant factors affecting canopy interception. The relationship between canopy interception and LAI was expressed by a linear function, as well as the relationship between canopy interception and rainfall amount. We proposed a canopy interception model versus LAI and rainfall characteristics to simulated the water loss by canopy interception. The results indicated that canopy interception loss increased with bigger LAI and decreased with rainfall amount increasing, indicating that canopy interception can't be ignored in the crop production, especially with small LAI and high precipitation. [ABSTRACT FROM AUTHOR]

Real-time monitoring of canopy chlorophyll content is significant in understanding crop growth status and guiding precision agricultural management. Remote sensing methods have demonstrated great potential in this regard. However, the spatiotemporal heterogeneity of chlorophyll content within crop canopies poses challenges to the accuracy and stability of remote sensing estimation models. Hence, this study aimed to develop a novel method for estimating canopy chlorophyll content (represented by SPAD values) in maize (Zea mays L.) canopies. Firstly, we investigated the spatiotemporal distribution patterns of maize canopy SPAD values under varying nitrogen application rates and different growth stages. The results revealed a non-uniform, "bell-shaped" curve distribution of maize canopy SPAD values in the vertical direction. Nitrogen application significantly influenced the distribution structure of SPAD values within the canopy. Secondly, we achieved satisfactory results by fitting the Lorentz peak distribution function to the SPAD values of different leaf positions in maize. The fitting performance, evaluated using R² and RMSE, ranged from 0.69 to 0.98 and 0.45 to 3.59, respectively, for the year 2021, and from 0.69 to 0.77 and 2.38 to 6.51, respectively, for the year 2022.Finally, based on the correlation between canopy SPAD values and vegetation indices (VIs) at different growth stages, we identified the sensitive leaf positions for the selected CCCI (Canopy Chlorophyll Index) in each growth stage. The 6th (r = 0.662), 4th (r = 0.816), 12th (r = 0.722), and 12th (r = 0.874) leaf positions exhibited the highest correlations. Compared to the estimation model using canopy wide SPAD values, the model based on sensitive leaf positions showed improved accuracy, with increases of 34%, 3%, 20%, and 3% for each growth stage, respectively. In conclusion, the findings of this study contribute to the enhancement of chlorophyll content estimation models in crop canopies and provide valuable insights for the integration of crop growth models with remote sensing methods. [ABSTRACT FROM AUTHOR]

The widely used SAIL (Scattering by Arbitrarily Inclined Leaves) radiative transfer model (RTM) is designed for canopies that can be considered as homogeneous turbid media and thus should be inadequate for row canopies. However, numerous studies have employed the SAIL model for row crops (e.g., wheat and maize) to simulate canopy reflectance or retrieve vegetation properties with satisfactory accuracy. One crucial reason may be that under certain conditions, a row crop canopy can be considered as a turbid medium, fulfilling the assumption of the SAIL model. Yet, a comprehensive analysis about the performance of SAIL in row canopies under various conditions is currently absent. In this study, we employed field datasets of wheat canopies and synthetic datasets of wheat and maize canopies to explore the impacts of the vegetation cover fraction (fCover), solar angle and soil background on the performance of SAIL in row crops. In the numerical experiments, the LESS 3D RTM was used as a reference to evaluate the performance of SAIL for various scenarios. The results show that the fCover is the most significant factor, and the row canopy with a high fCover has a low soil background influence. For a non-black soil background, both the field measurement and simulation datasets showed that the SAIL model accuracy initially decreased, and then increased with an increasing fCover, with the most significant errors occurring when the fCover was between about 0.4 and 0.7. As for the solar angles, the accuracy of synthetic wheat canopy will be higher with a larger SZA (solar zenith angle), but that of a synthetic maize canopy is little affected by the SZA. The accuracy of the SAA (solar azimuth angle) in an across-row direction is always higher than that in an along-row direction. Additionally, when the SZA ranges from 65° to 75° and the fCover of wheat canopies are greater than 0.6, SAIL can simulate the canopy reflectance with satisfactory accuracy (rRMSE < 10%); the same accuracy can be achieved in maize canopies as long as the fCover is greater than 0.8. These findings provide insight into the applicability of SAIL in row crops and support the use of SAIL in row canopies under certain conditions (with rRMSE < 10%). [ABSTRACT FROM AUTHOR]

Accurate quantification of damage associated with root lodging events can help producers assess damage, predict potential yield losses, and help understand potential issues with grain quality that may arise post‐harvest (i.e., kernel weight reductions, premature germination on the ear, or vivipary). The objective of this research was to utilize imagery from an uncrewed aerial vehicle (UAV) to accurately quantify crop canopy height, grain yield, and identify trends in imagery data associated with grain quality after root lodging was imposed at multiple growth stages. Simulated corn (Zea mays L.) root lodging experiments were conducted in 2018 and 2019 with lodging treatments applied at two vegetative or two reproductive growth stages (V10, V14, VT/R1, and R3). At dough stage (R4), visible‐color and multispectral images were collected from each trial. Bare fields were also flown in February to obtain baseline elevation data. Imagery data were used to develop digital surface model (DSM) images and used to calculate indices of normalized difference red edge (NDRE) and normalized difference vegetation index (NDVI). Individual datapoints within each experimental plot were extracted from the imagery files and were compared to ground‐truth measurements. The DSM height values were similar to actual measured heights for most lodging treatments (Adj. R2 =.957). Both NDRE and NDVI exhibited linear trends with height and quality parameters (Adj. R2 =.25–.54), though yield patterns were best described using a quadratic model (Adj. R2 =.42–.60). These procedures hold utility in accurately quantifying canopy height following a root lodging event and hold promise in helping consultants identify yield and grain quality reductions associated with root lodging. Core Ideas: Digital surface model image data at dough stage correlated strongly with actual height data (Adj. R2 =.957).Indices using red or red edge reflectance at dough stage were moderate predictors of yield (Adj. R2 =.42–.60).Prediction of grain quality after root lodging was lower, but trends were evident (Adj. R2 =.25–.50). [ABSTRACT FROM AUTHOR]

Plant protection unmanned aerial vehicles (UAVs), which are highly adapted to terrain and capable of efficient low-altitude spraying, will be extensively used in agricultural production. In this paper, single or several independent factors influencing the deposition characteristics of droplets sprayed by plant protection UAVs, as well as the experimental methods and related mathematical analysis models used to study droplet deposition and drift, are systematically investigated. A research method based on farmland environmental factors is proposed to simulate the deposition and drift characteristics of spray droplets. Moreover, the impacts of multiple factors on the droplet deposition characteristics are further studied by using an indoor simulation test system for the spraying flow field of plant protection UAVs to simulate the plant protection UAVs spraying flow field, temperature, humidity and natural wind. By integrating the operation parameters, environmental conditions, crop canopy characteristics and rotor airflow, the main effects and interactive effects of the factors influencing the deposition of spray droplets can be explored. A mathematical model that can reflect the internal relations of multiple factors and evaluate and analyze the droplet deposition characteristics is established. A scientific and effective method for determining the optimal spray droplet deposition is also proposed. In addition, this research method can provide a necessary scientific basis for the formulation of operating standards for plant protection UAVs, inspection and evaluation of operating tools at the same scale, and the improvement and upgrading of spraying systems. [ABSTRACT FROM AUTHOR]

Several vital processes in crop canopies and their microclimate, such as canopy photosynthesis and transpiration, are affected by fluctuations in irradiance, here called lightflecks. These lightflecks occur both in the field and inside greenhouses, although their dynamics may be different, due to an absence of wind in the greenhouse. When modelling plant and climate processes, these dynamic conditions are often ignored. The current study reports (1) high frequency (i.e. 1s interval) observations of lightflecks inside a greenhouse during several months and (2) a quantification of the effect of these lightflecks on leaf-level photosynthesis, transpiration and energy balance in rose by comparing results from a dynamic and a steady-state biophysical model. We found that lightflecks in the greenhouse were short, typically < 5s. Increasing complexity in irradiance fluctuation patterns (i.e. from very artificial to measured time series) reduced the difference between dynamic and steady-state simulations, e.g. for photosynthesis this difference decreased from 50% to 5%; for water-use efficiency the difference decreased from 70% to 5%. The relative contribution of the increase in light sum and fewer delayed responses by compensating drops in light level through additional light supply on plant processes was different. For example, photosynthesis was equally affected by both the increase in light sum and fewer delayed responses (i.e. 19% and 15% respectively), whereas water-use efficiency was mostly a result of less delayed plant responses (i.e. 9% compared to 1%). Our results demonstrate that lightflecks occur frequently in the greenhouse, and have strong effects on crop photosynthesis, water-use efficiency, and microclimate conditions. • High frequency radiation measurements reveal rapid light fluctuations in greenhouses. • Most lightflecks in the greenhouse are shorter than 5 s. • Using artificial lightflecks might overestimate dynamic plant response consequences. • Compensating light drops through supplementary light increases water-use efficiency. [ABSTRACT FROM AUTHOR]

Crop yield prediction before harvest is a key issue in managing agricultural policies and making the best decisions for the future. Using remote sensing techniques in yield estimation studies is one of the important steps for many countries to reach their agricultural targets. However, crop yield estimates rely on labor-intensive surveys in Ethiopia. To solve this, we used Sentinel-2, crop canopy analyzer, and ground-truthing data to estimate grain yield (GY) and aboveground biomass (AGB) of two major crops, teff and finger millet, in 2020 and 2021 in Ethiopia's Aba Gerima catchment. We performed a supervised classification of October Sentinel-2 images at the tillering stage. Among vegetation indices and leaf area index (LAI) used to predict teff and finger millet GY and AGB, the enhanced vegetation index (EVI) and normalized-difference VI (NDVI) provided the best fit to the data. NDVI and EVI most influenced teff AGB (R2 = 0.87; RMSE = 0.50 ton/ha) and GY (R2 = 0.84; RMSE = 0.14 ton/ha), and NDVI most influenced finger millet AGB (R2 = 0.87; RMSE = 0.98 ton/ha) and GY (R2 = 0.87; RMSE = 0.22 ton/ha). We found a close association between GY and AGB and the satellite EVI and NDVI. This demonstrates that satellite images can be employed in yield prediction studies. Our results show that satellite and crop canopy analyzer-based monitoring can facilitate the management of teff and finger millet to achieve high yields and more sustainable food production and environmental quality in the area. The results could be reproducible under similar study catchment conditions and boost crop yield. Extrapolation of the models to other areas requires local validation. To improve crop monitoring for farmers and reduce expenses, we suggest integrating time series Sentinel-2 images along with LAI obtained from crop canopy analyzers collected during the cropping season. [ABSTRACT FROM AUTHOR]

To obtain a more consistent droplet distribution and reduce spray drift, it is necessary to keep the entire spray boom parallel to the crop canopy or ground and maintain a certain distance from the spray nozzles to the crop canopy or ground. A high-performance boom active control system was developed for boom trapezoid suspension. The hydraulic system and hardware circuit of the boom control system were designed based on analyzing the configuration of active trapezoid suspension. The mathematical models of valve-controlled hydraulic cylinders and active boom suspensions were developed. Step response and frequency domain response analysis of passive suspension were conducted by Simulink simulations, and then key parameters of the boom suspension and hydraulic system were determined. A feedforward proportion integration differentiation (FPID) control algorithm was proposed to improve the tracking performance. The designed control system was assembled on a 24 m boom with trapezoid suspension. The response characteristic of the active boom control system was tested by the step signal and the sinusoidal signal from a six-degree-of-freedom hydraulic motion platform. Firstly, the tracking performance of the active balance control system for the PID (proportion integration differentiation) and FPID control algorithms was compared for a given 0.2 Hz sine signal. Then, for the ground-following control system, the response characteristics in challenging terrain and tracking performance in less challenging terrain were tested. Field experiment results indicate that the maximum rolling angle of the chassis was 3.896 ° while the maximum inclination angle of the boom was 0.453 ° . The results show that the designed boom adjustment and control system can effectively adjust the boom motion in real time and meet the requirements of field operation. [ABSTRACT FROM AUTHOR]

Winter wheat is a major food source for the inhabitants of North China. However, its yield is affected by drought stress during the growing period. Hence, it is necessary to develop drought-resistant winter wheat varieties. For breeding researchers, yield measurement, a crucial breeding indication, is costly, labor-intensive, and time-consuming. Therefore, in order to breed a drought-resistant variety of winter wheat in a short time, field plot scale crop yield estimation is essential. Unmanned aerial vehicles (UAVs) have developed into a reliable method for gathering crop canopy information in a non-destructive and time-efficient manner in recent years. This study aimed to evaluate strategies for estimating crop yield using multispectral (MS) and hyperspectral (HS) imagery derived from a UAV in single and multiple growth stages of winter wheat. To accomplish our objective, we constructed a simple linear regression model based on the single growth stages of booting, heading, flowering, filling, and maturation and a multiple regression model that combined these five growth stages to estimate winter wheat yield using 36 vegetation indices (VIs) calculated from UAV-based MS and HS imagery, respectively. After comparing these regression models, we came to the following conclusions: (1) the flowering stage of winter wheat showed the highest correlation with crop yield for both MS and HS imagery; (2) the VIs derived from the HS imagery performed better in terms of estimation accuracy than the VIs from the MS imagery; (3) the regression model that combined the information of five growth stages presented better accuracy than the one that considered the growth stages individually. The best estimation regression model for winter wheat yield in this study was the multiple linear regression model constructed by the VI of ' b 1 − b 2 / b 3 − b 4 ' derived from HS imagery, incorporating the five growth stages of booting, heading, flowering, filling, and maturation with r of 0.84 and RMSE of 0.69 t/ha. The corresponding central wavelengths were 782 nm, 874 nm, 762 nm, and 890 nm, respectively. Our study indicates that the multiple temporal VIs derived from UAV-based HS imagery are effective tools for breeding researchers to estimate winter wheat yield on a field plot scale. [ABSTRACT FROM AUTHOR]

In order to clarify the field sampling methods suitable for pesticide residue testing under the application mode of unmanned aerial spray system (UAS) for plant protection, this study explored the four sampling methods, Z-shaped method, checkerboard method, diagonal method, and '米'-shaped method, under optimal application conditions, and the density distribution of droplets under different sampling points, and evaluated the uncertainty introduced by the sampling methods. The results show that the optimal operating parameters of T30 plant protection UAS were: flight speed of 3 m/s, flight altitude of 2 m from crop canopy, and working distance of 5 m. The spray deposition content at the periphery of the spray operation area of the plant protection UAS has a great influence on the change of the average spray deposition content in the region, and the residual samples should be collected as much as possible to avoid collecting from the periphery of the application operation area. In order to increase the uniformity and representativeness of field sample collection in the application mode of plant protection UAS, it is recommended that the number of sampling points should be set at 18 or more, and the diagonal method, checkerboard method or Z-shaped method can be selected. [ABSTRACT FROM AUTHOR]

An on-the-go infield machine vision system was developed to measure internode length (i.e., the distance between main stem nodes) of cotton (Gossypium hirsutum L.) plants, which is a significant indicator of past water stress. An infield vehicle was developed to convey the machine vision system automatically across the crop canopy. This paper presents an evaluation of the devised system under a range of operational and field conditions. On average, the vision system's internode length detection rate ranged from 12 measurements per 100 plants for compact plants when the sun was directly overhead with respect to the camera, to 64 measurements per 100 plants for vigorous plants when the sunlight was perpendicular to the camera view angle. Imaging at night time using 850 nm near-infrared illumination resulted in internode length detection rates not significantly different from the most reliable daylight conditions. Both across-row and along-row operations of the system were evaluated, with the system yielding internode length measurements for groundspeeds up to 0.20 m/s for along-row operation. Visual occlusion of the main stem nodes by foliage and variations in natural lighting conditions were observed to be the principal reasons for internode lengths not being detected successfully for every plant. However, from the success rates observed, it is concluded that the system has the practical capability to map internode length across a field and hence, identify spatial trends in internode length from which trends in plant water stress can be inferred. [ABSTRACT FROM AUTHOR]

The objective of this study was to review representative publications for improved knowledge of spraying dense field crop canopies to augment the current understanding of the interaction between target foliage characteristics, spray practices, and the environment. Emphasis was placed on measured deep-canopy spray deposits made by full-scale sprayers and studies of airflow within and around crop canopies. Airflow could act as a spray droplet carrier and/or indicator of the internal canopy structure that restricts droplet penetration. High variation in natural airflows was noted in several studies. Crop canopy descriptions for spray studies were generally limited to overall canopy/row dimensions, descriptions of individual plant structures, leaf shapes, and leaf area index. Few studies evaluated the internal canopy "openness" with characteristic shape and size of internal volumes that would accommodate spray droplet trajectories. There have been significant increases in available spray tip designs with multiple orifices, discharge configurations, and droplet sizes that offered the applicator many choices. Advanced sprayer technologies ranging from nozzle control to sensor navigation are available, provided that suitable algorithms can be developed in a timely manner that pertain to a wide variety of spray and crop conditions. The air-assist technique provides a dynamic alternative to traditional over-the-top sprays for increasing spray penetration and deposit, advocating that the specifics of air discharge, spray droplet sizes, and canopy structure can be integrated. The complexity of the spraying process needs an extensive collaborative effort of many stakeholders to develop solutions for sprays to penetrate foliage that is subject to diseases and pests. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate the capabilities of Pleiades-1a imagery for very high resolution (VHR) crop yield estimation by utilizing the predictor variables from the horizontal-spectral information, through Normalized Difference Vegetation Indices (NDVI), and the vertical-volumetric crop characteristics, through the derivation of Crop Canopy Models (CCMs), from the stereo imaging capacity of the satellite. CCMs captured by Unmanned Aerial Vehicles are widely used in precision farming applications, but they are not suitable for the mapping of large or inaccessible areas. We further explore the spatiotemporal relationship of the CCMs and the NDVI for five observation dates during the growing season for eight selected crop fields in Germany with harvester-measured ground truth crop yield. Moreover, we explore different CCM normalization methods, as well as linear and non-linear regression algorithms, for the crop yield estimation. Overall, using the Extremely Randomized Trees regression, the combination of CCMs and NDVI achieves an R2 coefficient of determination of 0.92. [ABSTRACT FROM AUTHOR]

Air conditions are usually used to control the environmental parameters in plant factories with artificial light. Among them, the air velocity is generally lower than the optimum range of 0.1-1 m/s that is required for plant growth. The occurrence of tipburn behavior is often observed on the inner and newly developed leaves of lettuce plants, especially during the last days of transplanting. It is very necessary to properly design the airflow in the indoor cultivation systems, thus promoting the growth for less occurrence of tipburn in the lettuce plants. The perforated air tubes can be used to improve the airflow. However, this ventilation scheme is required by the installation of pipelines in the plant factory, inevitably leading to an increase in the installation process and the complexity of the project. In this study, a kind of air-conducting cultivation tank was designed to integrate with the cultivation bed (CBT). The airflow disturbance increased inside the crop canopy in the multi-layer cultivation mode of the plant factory, indicating the simple construction of the ventilation pipe. A model of CBT was also constructed using three-dimensional Computational Fluid Dynamic software (CFD). The airflow velocity was simulated and measured inside the plant canopy with the inlet velocity of 5 m/s. The root mean square error was 0.22 m/s between the calculated and measured airflow velocity. There was a consistent distribution of the simulated and measured geometric center section in the cultivation area, indicating the accurate simulation of the airflow velocity. The validated model was used to simulate the effects of different air inlet velocities on the airflow direction and distribution inside the crop canopy. The simulation results showed that the airflow entered the crop canopy area via the holes of the cultivation pipe, thus forming a regular airflow beam profile. There was a larger airflow area of high velocity in the crop canopy with the increase of the inlet airflow velocity. Once the inlet velocity was 6 m/s, the percentage of volumes was 56.3% inside the plant canopy with the air velocity between 0.1 and 1 m/s, and the volume-weighted average air velocity was 0.15 m/s in the canopy interior. Correspondingly, 6 m/s was selected as the entrance speed of CBT, where the mature lettuce was used as the test material. The microenvironment was compared in the inner part of the lettuce canopy under CBT and traditional ventilation control mode (TVC) under the same environmental conditions. A systematic investigation was carried out to test the effect of ventilation and temperature regulation on the air flow velocity. The results showed that the air temperature was 22.4 ℃ inside the canopy under CBT treatment during the light period, which was lower than the TVC treatment of 23.7 ℃. There was a relatively less significant difference in air temperature in the dark period. The average air relative humidity in the light and dark periods inside the canopy under the CBT treatment was 65.8% and 71.6%, respectively, which were 5.6% and 8.7% lower than those under the TVC treatment. As such, the CBT greatly contributed to the regulation and control of various micro-environmental parameters inside the crop canopy, compared with the traditional ventilation mode. The finding can provide the promotion value to reduce the requirements of environmental control for the high efficiency of air conditioning temperature. [ABSTRACT FROM AUTHOR]

Background: Large-scale data on the photosynthetic characteristics of whole crop canopy is crucial for improving yield. However, current data collection methods remain challenging, and the time constraints associated with photosynthetic data collection further complicate matters. Developing a practical yet easy-to-use tool for collecting whole-canopy data is essential to address these challenges. Furthermore, it is necessary to obtain instantaneous measurements of photosynthetic rate over a wide range of CO2 concentrations under an unsteady state to enable faster data collection and obtain reliable biochemical limits of carbon assimilation. This study developed a semi-open chamber system with steady and unsteady state measurement techniques to collect biochemical photosynthetic data from an entire cucumber canopy, emphasizing the correction procedures for CO2 concentration of unsteady state measurements applicable regardless of chamber scale. Results: After constructing a semi-open chamber system, we described how to correct measurement errors according to chamber volume. In order to assess the accuracy of the newly developed system, an analysis was conducted to determine the overall measurement error resulting from variations in the reference, sample CO2 concentration, and leakage flow rate. The total measurement error was accurate to no more than 10%. Furthermore, the difference between the photosynthetic rate of the single leaf and that of the whole-canopy was not significant in Rubisco activity-limited carboxylation range. In addition, the Farquhar–von Caemmerer–Berry (FvCB) model parameters and the photosynthetic rate estimation values were compared to evaluate the steady- and unsteady state measurement methods between the cucumber seedlings' single-leaf and whole-canopy. The average root mean square error of the FvCB model in the steady (standard A-Ci response) and unsteady states (800 to 400 ramp) of the chambers was 1.4 and 2.3, respectively. Results show that the developed system is suitable for measuring the gas exchange rate of the cucumber canopy. Conclusions: We demonstrate the correction method for measurement errors to enable the gas exchange rate of the whole-canopy even in an unsteady state. The correction method of the measurement system of the gas exchange rate for the whole- canopy can be applied regardless of the volume of the chamber, and it can be applied simply to other chamber systems. In addition, an unsteady state measurement method for fast data collection was also applicable. However, it was deemed necessary to identify a more optimal measurement range by conducting measurements across a broader range of values. [ABSTRACT FROM AUTHOR]

The fraction of absorbed photosynthetically active radiation (FPAR), which represents the capability of vegetation-absorbed solar radiation to accumulate organic matter, is a crucial indicator of photosynthesis and vegetation growth status. Although a simplified semi-empirical FPAR estimation model was easily obtained using vegetation indices (VIs), the sensitivity and robustness of VIs and the optimal inversion method need to be further evaluated and developed for canola FPAR retrieval. The objective of this study was to identify the robust hybrid inversion model for estimating the winter canola FPAR. A field experiment with different sow dates and densities was conducted over two growing seasons to obtain canola FPARs. Moreover, 29 VIs, two machine learning algorithms and the PROSAIL model were incorporated to establish the FPAR inversion model. The results indicate that the OSAVI, WDRVI and mSR had better capability for revealing the variations of the FPAR. Three parameters of leaf area index (LAI), solar zenith angle (SZA) and average leaf inclination angle (ALA) accounted for over 95% of the total variance in the FPARs and OSAVI exhibited a greater resistance to changes in the leaf and canopy parameters of interest. The hybrid inversion model with an artificial neural network (ANN-VIs) performed the best for both datasets. The optimal hybrid inversion model of ANN-OSAVI achieved the highest performance for canola FPAR retrieval, with R2 and RMSE values of 0.65 and 0.051, respectively. Finally, the work highlights the usefulness of the radiation transfer model (RTM) in quantifying the crop canopy FPAR and demonstrates the potential of hybrid model methods for retrieving the canola FPAR at each growth stage. [ABSTRACT FROM AUTHOR]

With the aim of solving the problems of high labor intensity, low operational efficiency, and poor deposition distribution uniformity in the mango canopy associated with traditional plant protection devices, an air-ground co-operation stereoscopic plant protection system consisting of an orchard caterpillar mist sprayer and a six-rotor plant protection UAV was designed to jointly undertake plant protection operations in mango orchards. We tested the spraying performance of the system on mango trees, compared with the single-machine operation, the air–ground co-operation system could significantly increase the droplet coverage on the upperside of mango leaves in each part of the canopy (a 14.7% increase for the mist sprayer and 12.9% for the UAV). This increased the active component deposition distribution uniformity in the mango canopy but could not significantly improve the deposition and coverage of droplets on the underside of leaves compared with the mist sprayer and plant protection UAV. Due to the characteristics of the mango canopy such as large leaf length and thickness and complex leaf inclination distribution, this led to poor deposition distribution uniformity of the two spray units, and the overall CV was over 150%. The pesticide active ingredients were almost uniformly distributed in the vertical direction when the application ratios (ground implements/plant protection drones) were 8/2 and 7/3, offering a promising protocol for reduced pesticide application in mango orchards. This study presents promising data that support the innovative integration of drones into crop protection programs for large canopy crops (e.g., mango) and provides guidance for the ACSPPS system in reduction and precision application research. [ABSTRACT FROM AUTHOR]

In semi‐arid cropping regions where rainfall is variable, stored soil water is important for reducing the impacts of dry periods; however, in‐season evaporation limits yield potential. We tested the productivity benefits of protecting wheat and lentil crops from evaporation using inter‐row polymer cover, when grown in southern Australia. For wheat, white polyvinyl chloride (PVCw) increased yield by 50% (561 kg/ha) and 11% (408 kg/ha) for decile 2 (2018) and 3 (2019) seasonal rainfall respectively; and for lentil, yield increases were 20% (443 kg/ha) in 2019. A sprayable alginate polymer increased wheat yield by 8% (310 kg/ha). Total water use for wheat in 2018 was reduced (17 mm) for PVCw and water use efficiency (WUE) significantly increased from 7 to 11 kg/ha/mm, whereas in 2019, WU was greater (47 mm) for PVCw, but WUE was equivalent to the control (~15 kg/ha/mm). Colour of the PVC modified the light available to the crop and canopy temperature, where a highly reflective inter‐row cover (albedo 0.44) increased wheat yield (~487 kg/ha). For the future, formulations of highly reflective polymers applied in‐season within dryland cropping enterprises are likely to provide production advantages, although logistical viability of application and potential environmental implications may define impediments to adoption. [ABSTRACT FROM AUTHOR]

Summary: A detailed field experiment was conducted in Northern India to investigate the effects of (1) sowing dates, (2) intercropping, and (3) inter-crop orientation, on the severity of White Rust of mustard (Brassica juncea L.) caused by Albugo candida. Field plots (4 m by 3 m) with three sowing dates at ten-day intervals and with four mustard-wheat intercropping treatments were established at the Experimental Farm of GB Pant University of Agriculture & Technology, Pantnagar, India. The four intercropping treatments were: (i) Sole mustard; (ii) 2 wheat rows with 1 mustard row (2v1); (iii) 5 wheat rows with 1 mustard row (5v1); and (iv) 8 wheat rows with 1 mustard row (8v1). Assessments of White Rust severity made at different layers in the crop canopy indicated in all four treatments a vertical gradient of increasing disease from the top to the lower layers of the crop canopy. Results further indicated that both sowing date and intercropping had significant (P <0.05) effects on disease severity. White Rust severity was highest in the Sole mustard stands, followed in decreasing order by 2v1, 5v1 and 8v1. White Rust severity also decreased in delayed sowing dates, i.e., severity was much higher in the early (1st week of November) than in the late (third week of November) crop establishment dates. White Rust severity was higher in plots with an East-West orientation compared to a North-South orientation, presumably reflecting the more conducive microclimate (e.g., leaf wetness duration, shade) conditions in the East-West orientation. This work is indicative of the value of intercropping mustard to control White Rust in the field. [ABSTRACT FROM AUTHOR]

Above-ground biomass (AGB) serves as an indicator of crop growth status, and acquiring timely AGB information is crucial for estimating crop yield and determining appropriate water and fertilizer inputs. Unmanned Aerial Vehicles (UAVs) equipped with RGB cameras offer an affordable and practical solution for efficiently obtaining crop AGB. However, traditional vegetation indices (VIs) alone are insufficient in capturing crop canopy structure, leading to poor estimation accuracy. Moreover, different flight heights and machine learning algorithms can impact estimation accuracy. Therefore, this study aims to enhance wheat AGB estimation accuracy by combining VIs, crop height, and texture features while investigating the influence of flight height and machine learning algorithms on estimation. During the heading and grain-filling stages of wheat, wheat AGB data and UAV RGB images were collected at flight heights of 30 m, 60 m, and 90 m. Machine learning algorithms, including Random Forest Regression (RFR), Gradient Boosting Regression Trees (GBRT), Ridge Regression (RR), Least Absolute Shrinkage and Selection Operator (Lasso) and Support Vector Regression (SVR), were utilized to construct wheat AGB estimation models. The research findings are as follows: (1) Estimation accuracy using VIs alone is relatively low, with R2 values ranging from 0.519 to 0.695. However, combining VIs with crop height and texture features improves estimation accuracy, with R2 values reaching 0.845 to 0.852. (2) Estimation accuracy gradually decreases with increasing flight height, resulting in R2 values of 0.519–0.852, 0.438–0.837, and 0.445–0.827 for flight heights of 30 m, 60 m, and 90 m, respectively. (3) The choice of machine learning algorithm significantly influences estimation accuracy, with RFR outperforming other machine learnings. In conclusion, UAV RGB images contain valuable crop canopy information, and effectively utilizing this information in conjunction with machine learning algorithms enables accurate wheat AGB estimation, providing a new approach for precision agriculture management using UAV remote sensing technology. [ABSTRACT FROM AUTHOR]