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Sugarcane is a perennial grass species mainly for sugar production and one of the significant crops in Costa Rica, where ideal growing conditions support its cultivation. Weed control is a critical aspect of sugarcane farming, traditionally managed through preventive or corrective mechanical and chemical methods. However, these methods can be time-consuming and costly. This study aimed to develop site-specific, variable rate prescription maps for weed control using remote sensing. High-spatial-resolution images (5 cm) and Light Detection And Ranging (LiDAR) were acquired using a Micasense Rededge-P camera and a DJI L1 sensor mounted on a drone. Precise locations of weeds were collected for calibration and validation. Normalized Difference Vegetation Index derived from multispectral images separated vegetation coverage and soil. A deep learning (DL) algorithm further classified vegetation coverage into sugarcane and weeds. The DL model performed well without overfitting. The classification accuracy was 87% compared to validation samples. The density and average heights of weed patches were extracted from the canopy height model (LiDAR). They were used to derive site-specific prescription maps for weed control. This efficient and precise alternative to traditional methods could optimize weed control, reduce herbicide usage and provide more profitable yield. [ABSTRACT FROM AUTHOR]

An orchard sprayer prototype running a variable-rate algorithm to adapt the spray volume to the canopy characteristics (dimensions, shape and leaf density) in real-time was designed and implemented. The developed machine was able to modify the application rate by using an algorithm based on the tree row volume, in combination with a newly coefficient defined as Density Factor (Df). Variations in the canopy characteristics along the row crop were electronically measured using six ultrasonic sensors (three per sprayer side). These differences in foliage structure were used to adjust the flow rate of the nozzles by merging the ultrasonic sensors data and the forward speed information received from the on-board GNSS. A set of motor-valves was used to regulate the final amount of sprayed liquid. Laboratory and field tests using artificial canopy were arranged to calibrate and select the optimal ultrasonic sensor configuration (width beam and signal pre-processing method) that best described the physical canopy properties. Results indicated that the sensor setup with a medium beam width offered the most appropriate characterization of trees in terms of width and Df. The experimental sprayer was also able to calculate the application rate automatically depending on changes on target trees. In general, the motor valves demonstrated adequate capability to supply and control the required liquid pressure at all times, mainly when spraying in a range between 4.0 and 14.0 MPa. Further work is required on the equipment, such as designing field efficiency tests for the sprayer or refining the accuracy of Df. [ABSTRACT FROM AUTHOR]

Preemergence herbicides are traditionally applied uniformly throughout the area; however, weed control may vary due to spatial variability of the soil within the same area. Precision agriculture tools such as variable rate applications of herbicides improve weed control, making it necessary to know the physicochemical characteristics of the soil. The objective of this study was to map the spatial variability of sorption–desorption and agronomic efficiency of indaziflam and metribuzin for weed management in a field of 17.5 ha of Minas Gerais, Brazil. Fifty-five soil samples were collected (0–10 cm depth) and based on their physicochemical characteristics and the sorption and desorption coefficients for indaziflam and metribuzin, determined by a batch equilibrium method, thematic maps were generated for each variable in QGIS (Quantum Geographic Information System). In addition, the bioavailability concentration of each herbicide was determined, and its efficacy was evaluated in controlling Amaranthus hybridus and Eleusine indica in a representative soil sample. The sorption coefficient (Kd(s)) of indaziflam ranged from 6.9 to 40.5 L kg−1, the sorbed percentage (S%) from 61 to 86.6%, the desorbed percentage (D%) from 8.4 to 33.1%, and the total bioavailability (TB%) from 26 to 55.7%. The Kd(s) values of metribuzin ranged from 1.1 to 4.3 L kg−1, S% from 22.8 to 33.9%, D% from 17.2 to 22.0%, and TB% from 79.4 to 96.7%. Organic matter was highly correlated with the TB% of indaziflam (r = –0.8) and metribuzin (r = –0.7). Soil solution equilibrium concentration (Ce) of 1.9 g a.i. ha−1 of indaziflam controlled 80% of A. hybridus and E. indica. The Ce values of metribuzin were 55.2 and 111.2 g a.i. ha−1 to control 80% of A. hybridus and E. indica, respectively. Recommendations of varied doses provided reductions of 17.6% in the total application of indaziflam and 9.8% of metribuzin concerning the highest dose recommended in the field. The characterization of the soil physicochemical characteristics as well as the determination of the total bioavailability of indaziflam and metribuzin, by knowing the sorption–desorption coefficients, allowed for the recommendation of variable and precise doses of herbicides for the efficient management of A. hybridus and E. indica in preemergence, reducing potential negative environmental impacts. [ABSTRACT FROM AUTHOR]

Sugarcane is a perennial grass species mainly for sugar production and one of the significant crops in Costa Rica, where ideal growing conditions support its cultivation. Weed control is a critical aspect of sugarcane farming, traditionally managed through preventive or corrective mechanical and chemical methods. However, these methods can be time-consuming and costly. This study aimed to develop site-specific, variable rate prescription maps for weed control using remote sensing. High-spatial-resolution images (5 cm) and Light Detection And Ranging (LiDAR) were acquired using a Micasense Rededge-P camera and a DJI L1 sensor mounted on a drone. Precise locations of weeds were collected for calibration and validation. Normalized Difference Vegetation Index derived from multispectral images separated vegetation coverage and soil. A deep learning (DL) algorithm further classified vegetation coverage into sugarcane and weeds. The DL model performed well without overfitting. The classification accuracy was 87% compared to validation samples. The density and average heights of weed patches were extracted from the canopy height model (LiDAR). They were used to derive site-specific prescription maps for weed control. This efficient and precise alternative to traditional methods could optimize weed control, reduce herbicide usage and provide more profitable yield.

Managing N supply to corn (Zea mays L.) properly is a major challenge, with consequences of mismanagement ranging from N-deficient, low yielding crops to pollution of surface and groundwater. For much of the 20th and 21st centuries, N fertilizer recommendations for corn have been calculated by multiplying the yield goal by a constant equal to the amount of N needed per mass of corn grain. While this approach is often sufficient to support the growing crop without tremendous environmental consequences, it does not explicitly consider N mineralized from soil organic matter (OM) or the dynamics of N mineralization or immobilization caused by previous cover crop residues. While typical N management practices used by farmers have led to relatively low N use efficiency, precision agriculture technologies developed in the late 20th century held the promise of improving N management by considering spatial variability in crop fields and adjusting fertilizer rates accordingly. To date, however, few farms use precision agriculture technologies for managing N. In this experiment, we tested a new N recommendation system based on a biogeochemical model, the soil and cover crop available nitrogen (SCAN) model, which explicitly considers OM and cover crop residue pools of N. We coupled this new N recommendation system with variable rate precision agriculture technology to generate spatially explicit N recommendations in corn fields. We hypothesized that this approach to N management would: (i) reduce the amount of N recommended for a corn crop compared to a yield goal approach while, (ii) maintaining corn yield at a level similar to that in a traditional yield goal approach. We found that the SCAN model reduced N rates at four of the six sites compared to the yield goal approach but recommended greater N application at two of the six sites. At one of the sites where the SCAN model recommended higher N rates, we also observed increased grain yields. However, when the SCAN model recommended lower N rates than the yield goal approach, we typically observed reduced yields. Additional research is needed to identify the proper agronomic nitrogen efficiency to use in the SCAN model.

【Objective】 This paper is to investigate experimentally the static spray characteristics of variable spray systems of centrifugal nozzles. 【Method】 A centrifugal nozzle variable spray test system was constructed by using a diaphragm pump, a high-precision turbine flowmeter, a solenoid valve and a centrifugal nozzle. Its static spray characteristics were studied for the nozzle flow rate in the range from 1.5 L/min to 13 000 r/min. For each nozzle flow rate, we measured the water droplet sizes and deposition distribution of the droplets using water-sensitive paper and droplet collection devices, respectively. 【Result】 With the increase in centrifugal nozzle flow rate, the size of droplets decreased. The deposition amount of fog droplets in the 0.5 m proximal to nozzle decreased first and then increased showing a double-peak patten, with the deposition amount increased in the second peak more than in the first peak. With the increase in the centrifugal nozzle flow rate, the peak value on the left side of the distribution center of the droplet deposition decreased, and the peak value on the left side skewed towards the droplet distribution center. The peak value on the right side remained unchanged, while the peak value on the right side moves away from the droplet distribution center. 【Conclusion】 The rotating speed of the centrifugal nozzle has a significant influence on sizes and deposition distribution of the water droplets. Reasonably controlling the droplet size to improve the uniformity of the droplet deposition distribution is a key to improving pesticide utilization and reducing its leaching to the environment.

Recently, different methods have been developed in order to increase the application efficiency of agricultural sprayer systems. The dynamic control of flow and / or pressure is performed by the proportional control of pumps or valves. The aim of the current study was to minimize the application rate error of field sprayers using the pump motor variable speed drive (VSD) of a centrifugal pump system. The flow rate and pressure control of the pump system was achieved using a Proportional Integral Derivative (PID) controller. During the tests, in the first stage, the flow rate-speed ratio was kept at a constant value to apply a constant application rate with varying ground speeds. In the second stage, the control of the system pressure was accomplished using the manually applied disturbing effects on the system. In the final stage, the flow rate of a spray nozzle was controlled by Pulse Width Modulation (PWM) depending on the speed at different constant pressure values. In flow control test, absulute percentage error of application rate in only six out of 150 measurements were higher than 10% which is acceptable application rate error at varying ground speeds from 6 to 10 km h-1. In pressure control tests, it was observed that the controller moved the system pressure back to the set value within 2-3 seconds after a disturbance applied. The system was able to control the nozzle flow rates between 50% and 100% by changing the duty cycle of the signal applied to the solenoid valve at the nozzle inlet during the PWM spray tests. Further investigation is required to test the observation in the current experiment on field sprayers to improve the efficiency of constant and variable rate or PWM applications. [ABSTRACT FROM AUTHOR]

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The airflow discharged from orchard airblast sprayers is a primary component for successfully carrying spray droplets to the target trees. Because of the variation in orchard tree canopies, control of the airflow to minimize off-target loss during spray application is essential. An automatic airflow control system for precision sprayers was developed to maximize spray droplet coverage on targets and minimize off-target loss while considering the tree canopy densities. The primary component of the system was an iris damper, which was designed as a retrofit attachment on the fan inlet of a threepoint airblast intelligent sprayer. A 3D light detection and ranging (LiDAR) sensor was installed at the top of the sprayer to acquire the tree canopy data. A motor was employed to control the damper opening with a micro-controller. To develop the models required for automatic airflow control, field experiments were conducted at three canopy density orchards with different cultivars (GoldRush, Gala, and Fuji). A total of 15 trees (five trees from each cultivar) were randomly selected, and five different damper openings (openings 1, 2, 3, 4, and 5) were tested for each tree. Opening 1 represented the same air inlet as a traditional precision airblast sprayer, while openings 2, 3, 4, and 5 were the sequentially reduced air inlets of the sprayer. A canopy density measurement algorithm was scripted to measure the canopy point density of individual trees. Three models were built to show relationships between (1) tree canopy point densities and airflows; (2) canopy densities and damper openings; and (3) damper opening and motor steps. The combination of the two models (2 & 3) was used to assess the amount of airflow required for a specific canopy density. Field validations for medium and high-density trees showed that the system achieved adequate spray penetration at the top, middle, bottom, back-left, and back-right positions of the tree sections and reduced off-target loss at the ground and edge of next row sections using openings 4 and 2, respectively. However, the mechanical motion of the damper required 3 s to move from minimum to maximum opening, so the average canopy density was recommended to control the airflow. The overall results suggested that the automatic airflow control system could reduce spray drift and off-target losses and improve spray application efficiency in orchards. [ABSTRACT FROM AUTHOR]

Most nitrogen (N) lost to the environment from grazed grassland is produced as a result of N excreted by livestock, released in the form of nitrous oxide (N2O) emissions, nitrate leaching and ammonia volatilisation. In addition to the N fertiliser applied, excreta deposited by grazing livestock constitute a heterogeneous excess of N, creating spatial hotspots of N losses. This study presents a yearlong N2O emissions map from a typical intensively managed temperate grassland, grazed periodically by a dairy herd. The excreta deposition mapping was undertaken using high-resolution RGB images captured with a remotely piloted aircraft system combined with N2O emissions measurements using closed statics chambers. The annual N2O emissions were estimated to be 3.36 ± 0.30 kg N2O–N ha−1 after a total N applied from fertiliser and excreta of 608 ± 40 kg N ha−1 yr−1. Emissions of N2O were 1.9, 3.6 and 4.4 times lower than that estimated using the default IPCC 2019, 2006 or country-specific emission factors, respectively. The spatial distribution and size of excreta deposits was non-uniform, and in each grazing period, an average of 15.1% of the field was covered by urine patches and 1.0% by dung deposits. Some areas of the field repeatedly received urine deposits, accounting for an estimated total of 2410 kg N ha−1. The method reported in this study can provide better estimates of how management practices can mitigate N2O emissions, to develop more efficient selective approaches to fertiliser application, targeted nitrification inhibitor application and improvements in the current N2O inventory estimation. [ABSTRACT FROM AUTHOR]

Wide assimilation of precision agriculture among farmers is currently dependent on the ability to demonstrate its efficiency at the field-scale. Yet, most experiments that compare variable-rate vs uniform application (VRA and UA) are performed in strips, concentrated in a small portion of the field with limited extrapolation to the field scale. A spatiotemporal normalized ratio (STNR) methodology is proposed to evaluate the impact of VRA compared with UA for on-farm trials at the field scale. It incorporates a base year in which the whole plot is managed with UA and consecutive years in which half of the plot is managed with UA and the other half is managed with VRA. Additionally, a novel normalized relative comparison index (NRCI) is presented where the ratios of VRA/UA sub-plots are compared between a base year and a consecutive year, for any measured parameter. The NRCI determines the impact of VRA on variability using statistical measures of dispersion (variability measures) and on performance with statistical measures of central tendency (performance measures). Variability measures with NRCI values lower or higher than 1 indicate VRA management decreased or increased variability. Performance measures with NRCI lower or higher than 1 indicate subplot impairment or improvement, respectively due to VRA management. The methodology was demonstrated on a commercial drip irrigated peach orchard and a wine grape vineyard. NRCI results showed that VRA drip irrigation reduced water status in-field variability but did not necessarily increase yield. The benefits and limitations of the proposed design are discussed. [ABSTRACT FROM AUTHOR]

The standard practice among rice farmers in Malaysia is to apply fertilizer using a single application rate for the whole field. However, fertility conditions vary across the field. The excess use of fertilizer leads to increased input cost and can be damaging to the environment. The focus of this research was to develop a method to apply fertilizer on-the-go while sensing the crop nutrient status of rice plants. A machine learning approach was used to develop a crop nitrogen status prediction model. The model used spectral data from an active canopy reflectance sensor and several vegetation indices as inputs. The model was then incorporated into an on-the-go variable rate fertilizer application system. System performance was then evaluated in the field. The results from this work showed that the model had and accuracy of 83% in classifying the nitrogen status of the rice plants. The results also showed that our method was able to save up to 20% fertilizer use while maintaining yield. These findings are important for large estate farmers who are looking to increase productivity and efficiency.

The presented research deals with the variable application of N fertilizers (VRA NF) in the stand of winter wheat and with possibilities for evaluating the effect of such application on grain yield. The effect of VRA NF was assessed in close cooperation between Spearhead Czech Ltd. and Mendel University in Brno within an operational experiment. In 2018 and 2019, three experimental plots were always chosen on which VRA NF was implemented by conventional technique according to application maps. Each application map included control strips with the uniform NF application. The application maps were prepared based on the spectral analysis of satellite images. The individual plots were divided into three zones: Zone 1 with the lowest yield potential, Zone 2 with the medium yield potential, Zone 3 with the highest yield potential. The highest dose of N was at all times applied in Zone 3, and conversely the lowest dose was applied in Zone 1. In 2018 and 2019, the experimental fields were harvested by harvester New Holland CX 8080 which was equipped with the technology for the monitoring of grain yields. The main goal of data processing was to remove error data at first and then to re-calibrate them using the information about the weight of harvested grain. The expected benefit, i.e. a yield increase by min. 3% was found only in 2018, when the benefit was about 5%. In the following year, the difference between the conventional application of N and VRA technologies was minimal. However, this condition was probably caused by drought which negatively affected all stands. In particular, measured values of grain yield do not indicate the negative effect of VRA NF on grain yield. This research further showed the applicability of yield data acquired by harvest technology; however, the elimination of error data is necessary as well as their re-calibration according to total yield ascertained by weighing the total production of grain from a specific plot.

Varying the rate of application of agronomic inputs generates many positive economic and environmental impacts. Increasingly, technologies that enable variable rate application are becoming a distinctive feature of precision agriculture. Nonetheless, a prerequisite, and crucial challenge, remains the optimal and operational designation of distinct application zones for differing agronomic operations. Core to this challenge is the conflation and fusion of diverse data sources ranging from satellite imagery to real-time in-situ data from farms. At present, zones for variable rate application are often defined manually by agronomists and farmers. This paper proposes a novel methodology for the automatic definition of zones for variable rate application. This approach comprises multi-dimensional spatio-temporal data integration methods, clustering-based data classification and a zone creation and representation procedure. In this way, the harmonization of heterogeneous data sources, augmented with different clustering algorithms, enable the delineation of management zones and subsequent construction of maps for potential variable rate applications. Experimental results confirm the effectiveness and efficiency of the proposed approach.

Since the beginning of the 21st century, there has been an increase in the agricultural area devoted to olive growing and in the consumption of extra virgin olive oil (EVOO). The continuous change in cultivation techniques implemented poses new challenges to ensure environmental and economic sustainability. In this context, precision oliviculture (PO) is having an increasing scientific interest and impact on the sector. Its implementation depends on various technological developments: sensors for local and remote crop monitoring, global navigation satellite system (GNSS), equipment and machinery to perform site-specific management through variable rate application (VRA), implementation of geographic information systems (GIS), and systems for analysis, interpretation, and decision support (DSS). This review provides an overview of the state of the art of technologies that can be employed and current applications and their potential. It also discusses the challenges and possible solutions and implementations of future technologies such as IoT, unmanned ground vehicles (UGV), and machine learning (ML). [ABSTRACT FROM AUTHOR]

Remote sensing technology has long been used to detect and map crop diseases. Airborne and satellite imagery acquired during growing seasons can be used not only for early detection and within-season management of some crop diseases, but also for the control of recurring diseases in future seasons. With variable rate technology in precision agriculture, site-specific fungicide application can be made to infested areas if the disease is stable, although traditional uniform application is more appropriate for diseases that can spread rapidly across the field. This article provides a brief overview of remote sensing and precision agriculture technologies that have been used for crop disease detection and management. Specifically, the article illustrates how airborne and satellite imagery and variable rate technology have been used for detecting and mapping cotton root rot, a destructive soilborne fungal disease, in cotton fields and how site-specific fungicide application has been implemented using prescription maps derived from the imagery for effective control of the disease. The overview and methodologies presented in this article should provide researchers, extension personnel, growers, crop consultants, and farm equipment and chemical dealers with practical guidelines for remote sensing detection and effective management of some crop diseases.

A study was conducted, on a 30 ha wheat field under a solar energy powered center pivot irrigation system in a commercial farm located 400 km north of Khartoum, Sudan, to evaluate the feasibility of variable rate fertilizer application of Diammonium Phosphate (DAP) on wheat crop based on variable soil phosphorus content. Soil phosphorus content was divided into three categories (low: 3.75 to 4.50 ppm, medium: 4.51 to 5.25 ppm and high: 5.26 to 6.00 ppm) and a GIS soil phosphorus content map of the experimental field was generated. Three variable application rates (200 kg ha−1, 160 kg ha−1 and 120 kg ha−1) of granular DAP fertilizer were determined to fit the low, medium and high soil phosphorus contents, respectively. The the DAP fertilizer rate of 200 kg ha−1 was the rate practiced for wheat production in the experimental farm.The normalized Difference Vegetation Index (NDVI), measured at different wheat growth stages using sentinel-2 satellite images, and wheat grain yield were used to evaluate the response of wheat crop to the variable DAP fertilizer application rates. Excluding the tillering stage, the results showed significant differences in the NDVI values among different soil phosphorus levels and DAP fertilizer application rates at other growth stages, including stem elongation, grain filling and maturity stages. Moreover, wheat yield results showed significant differences among different soil phosphorus contents (P > F = 0.0001) and DAP fertilizer application rates (P > F = 0.0457). On the average, the highest yield of 2.449 t ha−1 was recorded in the field areas treated with the high DAP fertilizer application rate (200 kg ha−1), where the second highest yield of 2.441 t ha−1 was observed in field areas under the medium DAP fertilizer application rate (160 kg ha−1); however, no significant differences between these two yield values. Based on these results, the total savings of the DAP fertilizer was estimated to be 792 kg (equivalent to 475.2 $) in the experimental field (30 ha), only when the medium DAP fertilizer rate (160 kg ha−1) was used in the medium and high soil phosphorus zones, saving 15.84 $ ha−1 without affecting production.

ABSTRACT Spatial variability management of soil chemical attributes is one of the approaches to be employed in the face of the constant challenge of increasing agricultural yield to meet world demand. In this sense, precision agriculture has as one of its tools the application of inputs at varying rates, which seeks to determine the ideal amount of fertilizer at each point of the crop, contrary to the conventional recommendation approach based on average values. In this context, this work studied the fertilizer recommendation methods used in site-specific nutrient management and the calculation methodologies for N, P, and K recommendations. For this purpose, a systematic literature study (SLS), consisting of systematic literature mapping, snowballing, and systematic literature review was performed. The analyzed studies were grouped into five domains (precision agriculture, soil fertility, site-specific nutrient application, fertilizer recommendation methods, and recommendation software for site-specific nutrient application). As a result, the SLS identified 12 methods for recommending N, nine for recommending P, and six for recommending K, in addition to five computer programs for precision agriculture that perform fertilizer recommendations at varying rates.

Unmanned aerial vehicles (UAVs) are tools for mechanized agriculture: they are used to alleviate maladies in a variety of fields through commercial, scientific, agricultural, and infrastructure enhancement. The purpose of the paper is to illuminate knowledge on mechanized agriculture using unmanned aircraft systems for pesticides and fertilizer application in obstacle rich farm. Various journal papers were reviewed to ascertain the state-of-the-art in agricultural unmanned aerial vehicles. X-rayed are unmanned aerial vehicle agrochemicals spraying architecture and efficacy, deployment and control strategies, obstacle sensing and avoidance systems, development/studies, and the limitations of the technology. The review shows that great strides have been made to develop agricultural unmanned aerial vehicles that can autonomously identify obstacle type, realize desired avoidance actions, and carry out variable rate agrochemical application. It is however noted that studies should continue on developing protocols and standard operation procedure, more human friendly interface platform, power technology, higher payload, real time quality imagery and robust mechanical features as well as enhanced sense and avoidance technology to meet the requirement of agricultural unmanned aerial vehicle for real time autonomous actions, flight endurance, low speed and low altitude. The paper therefore addressed the lack of awareness and absence of dedicated education on precision agriculture in the farming sector that has since ensured that its adoption level as a preferred system of farming remains very low in Nigeria despite the many benefits of unmanned aircraft vehicle farming technology. [ABSTRACT FROM AUTHOR]

Total nitrogen (N) content in aboveground biomass (N-uptake) in winter wheat (Triticum aestivum L.) as measured in a national monitoring programme was scaled up to full spatial coverage using Sentinel-2 satellite data and implemented in a decision support system (DSS) for precision agriculture. Weekly field measurements of N-uptake had been carried out using a proximal canopy reflectance sensor (handheld Yara N-Sensor) during 2017 and 2018. Sentinel-2 satellite data from two processing levels (top-of-atmosphere reflectance, L1C, and bottom-of-atmosphere reflectance, L2A) were extracted and related to the proximal sensor data (n = 251). The utility of five vegetation indices for estimation of N-uptake was compared. A linear model based on the red-edge chlorophyll index (CI) provided the best N-uptake prediction (L1C data: r2 = 0.74, mean absolute error; MAE = 14 kg ha−1) when models were applied on independent sites and dates. Use of L2A data, rather than L1C, did not improve the prediction models. The CI-based prediction model was applied on all fields in an area with intensive winter wheat production. Statistics on N-uptake at the end of the stem elongation growth stage were calculated for 4169 winter wheat fields > 5 ha. Within-field variation in predicted N-uptake was > 30 kg N ha−1 in 62% of these fields. Predicted N-uptake was compared against N-uptake maps derived from tractor-borne Yara N-Sensor measurements in 13 fields (1.7–30 ha in size). The model based on satellite data generated similar information as the tractor-borne sensing data (r2 = 0.81; MAE = 7 kg ha−1), and can therefore be valuable in a DSS for variable-rate N application. [ABSTRACT FROM AUTHOR]

Site-specific nitrogen (N) management in precision agriculture is used to improve nitrogen use efficiency (NUE) at the field scale. The objective of this study has been (i) to better understand the relationship between data derived from an unmanned aerial vehicle (UAV) platform and the crop temporal and spatial variability in small fields of about 2 ha, and (ii) to increase knowledge on how such data can support variable application of N fertilizer in winter wheat (Triticum aestivum). Multi-spectral images acquired with a commercially available UAV platform and soil available mineral N content (Nmin) sampled in the field were used to evaluate the in-field variability of the N-status of the crop. A plot-based field experiment was designed to compare uniform standard rate (ST) to variable rate (VR) N application. Non-fertilized (NF) and N-rich (NR) plots were placed as positive and negative N-status references and were used to calculate various indicators related to NUE. The crop was monitored throughout the season to support three split fertilizations. The data of two growing seasons (2017/2018 and 2018/2019) were used to validate the sensitivity of spectral vegetation indices (SVI) suitable for the sensor used in relation to biomass and N-status traits. Grain yield was mostly in the expected range and inconsistently higher in VR compared to ST. In contrast, N fertilizer application was reduced in the VR treatments between 5 and 40% depending on the field heterogeneity. The study showed that the methods used provided a good base to implement variable rate fertilizer application in small to medium scale agricultural systems. In the majority of the case studies, NUE was improved around 10% by redistributing and reducing the amount of N fertilizer applied. However, the prediction of the N-mineralisation in the soil and related N-uptake by the plants remains to be better understood to further optimize in-season N-fertilization. [ABSTRACT FROM AUTHOR]

Within-field variability of crop yield levels has been extensively investigated, but the spatial variability of crop yield responses to agronomic treatments is less understood. On-farm precision experimentation (OFPE) can be a valuable tool for the estimation of in-field variation of optimal input rates and thus improve agronomic decisions. Therefore, the objectives of this study were to investigate the spatial variability of optimal input rates in OFPE and the potential economic benefit of site-specific input management. Mixed geographically weighted regression (GWR) models were used to estimate local yield response functions. The methodology was applied to investigate the spatial variability in corn response to nitrogen and seed rates in four cornfields in Illinois, USA. The results showed that spatial heterogeneity of model parameters was significant in all four fields evaluated. On average, the RMSE of the fitted yield decreased from 1.2 Mg ha−1 in the non-spatial global model to 0.7 Mg ha−1 in the GWR model, and the r-squared increased from 10 to 68%. The average potential gain of using optimized uniform rates of seed and nitrogen was US$ 65.00 ha−1, while the added potential gain of the site-specific application was US$ 58.00 ha−1. The combination of OFPE and GWR proved to be an effective tool for testing precision agriculture's central hypothesis of whether optimal input application rates display adequate spatial variability to justify the costs of the variable rate technology itself. The reported results encourage more research on response-based input management recommendations instead of the still widespread focus on yield-based algorithms. [ABSTRACT FROM AUTHOR]

The presented research deals with the variable application of N fertilizers (VRA NF) in the stand of winter wheat and with possibilities for evaluating the effect of such application on grain yield. The effect of VRA NF was assessed in close cooperation between Spearhead Czech Ltd. and Mendel University in Brno within an operational experiment. In 2018 and 2019, three experimental plots were always chosen on which VRA NF was implemented by conventional technique according to application maps. Each application map included control strips with the uniform NF application. The application maps were prepared based on the spectral analysis of satellite images. The individual plots were divided into three zones: Zone 1 with the lowest yield potential, Zone 2 with the medium yield potential, Zone 3 with the highest yield potential. The highest dose of N was at all times applied in Zone 3, and conversely the lowest dose was applied in Zone 1. In 2018 and 2019, the experimental fields were harvested by harvester New Holland CX 8080 which was equipped with the technology for the monitoring of grain yields. The main goal of data processing was to remove error data at first and then to re-calibrate them using the information about the weight of harvested grain. The expected benefit, i.e. a yield increase by min. 3% was found only in 2018, when the benefit was about 5%. In the following year, the difference between the conventional application of N and VRA technologies was minimal. However, this condition was probably caused by drought which negatively affected all stands. In particular, measured values of grain yield do not indicate the negative effect of VRA NF on grain yield. This research further showed the applicability of yield data acquired by harvest technology; however, the elimination of error data is necessary as well as their re-calibration according to total yield ascertained by weighing the total production of grain from a specific plot. [ABSTRACT FROM AUTHOR]

Profitability analyses of site-specific nitrogen (N) management strategies have often failed to provide satisfying reasons for adoption of precision farming technologies. However, effects of precision farming on product quality and price premiums, as well as on downside risk mitigation, are generally not taken into account. This study aimed to evaluate the comparative advantages of site-specific N management over uniform N management considering N supply on grain quality, and accordingly price premiums for wheat from a downside risk point of view. A virtual field was modelled with two subfields representing two distinctive yield zones to investigate how consideration of grain quality affects the economic potential of site-specific N management under temporally varying N mineralization and changing price patterns of wheat. Moreover, the extent was investigated to which site-specific N management can have a risk-reducing effect on economic shortfalls compared to uniform N management. Two site-specific N management options were assessed: variable N rate application using yield mapping and N sensor for real-time proximal sensing. Results indicated that even though crop yields were only slightly higher, higher expected protein contents of grains could be achieved with site-specific N management options compared to uniform N management. Baking wheat quality was secured to a greater extent with site-specific N management options. Higher average grain quality improved the economic benefits due to price premiums. A risk-reducing effect was observed with the site-specific N management by maintaining the baking wheat quality with a higher probability. Higher economic returns mostly compensated the additional costs for the precision farming technologies in the lower tail of the probability distribution and, thus, site-specific N management did not show any substantial disadvantage on downside risk as compared to uniform N management. [ABSTRACT FROM AUTHOR]

The presented paper deals with the issue of selecting a suitable system for monitoring the winter wheat crop in order to determine its condition as a basis for variable applications of nitrogen fertilizers. In a four-year (2017–2020) field experiment, 1400 ha of winter wheat crop were monitored using the ISARIA on-the-go system and remote sensing using Sentinel-2 multispectral satellite images. The results of spectral measurements of ISARIA vegetation indices (IRMI, IBI) were statistically compared with the values of selected vegetation indices obtained from Sentinel-2 (EVI, GNDVI, NDMI, NDRE, NDVI and NRERI) in order to determine potential hips. Positive correlations were found between the vegetation indices determined by the ISARIA system and indices obtained by multispectral images from Sentinel-2 satellites. The correlations were medium to strong (r = 0.51–0.89). Therefore, it can be stated that both technologies were able to capture a similar trend in the development of vegetation. Furthermore, the influence of climatic conditions on the vegetation indices was analyzed in individual years of the experiment. The values of vegetation indices show significant differences between the individual years. The results of vegetation indices obtained by the analysis of spectral images from Sentinel-2 satellites varied the most. The values of winter wheat yield varied between the individual years. Yield was the highest in 2017 (7.83 t/ha), while the lowest was recorded in 2020 (6.96 t/ha). There was no statistically significant difference between 2018 (7.27 t/ha) and 2019 (7.44 t/ha).

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Site-specific management of crops represents an important improvement in terms of efficiency and efficacy of the different labours, and its implementation has experienced a large development in the last decades, especially for field crops. The particular case of the spray application process for what are called "specialty crops" (vineyard, orchard fruits, citrus, olive trees, etc.) represents one of the most controversial and influential actions directly related with economical, technical, and environmental aspects. This study was conducted with the main objective to find possible correlations between data obtained from remote sensing technology and the actual canopy characteristics. The potential correlation will be the starting point to develop a variable rate application technology based on prescription maps previously developed. An unmanned aerial vehicle (UAV) equipped with a multispectral camera was used to obtain data to build a canopy vigour map of an entire parcel. By applying the specific software DOSAVIÑA®, the canopy map was then transformed into a practical prescription map, which was uploaded into the dedicated software embedded in the sprayer. Adding to this information precise georeferenced placement of the sprayer, the system was able to modify the working parameters (pressure) in real time in order to follow the prescription map. The results indicate that site-specific management for spray application in vineyards result in a 45% reduction of application rate when compared with conventional spray application. This fact leads to a equivalent reduction of the amount of pesticide when concentration is maintained constant, showing once more that new technologies can help to achieve the goal of the European legislative network of safe use of pesticides. [ABSTRACT FROM AUTHOR]

Highlights • Variable rate application (VRA) of agricultural inputs is essential for optimum crop yield. • This study was conducted to develop a control system for VRA of granular fertilizers. • The manual fertilizer rate adjustment system of a seed drill was modified to an automatic control system. • The overall error of the developed VRA system was ±2.6%. Abstract A study was conducted to develop a control system for variable rate application (VRA) of granular fertilizers and evaluate its performance. By the use of a microprocessor, a pneumatic cylinder and a rotary shaft encoder, the manual mechanical fertilizer rate adjustment system of a seed drill (SOLA TRISEM 294/R ESP, Model: 37193 TIPO250) was modified and transformed to a real-time automatic control system. Laboratory based tests were conducted to evaluate the developed system performance in automatically applying the predetermined desired application rates at different ground speeds, which were within the range of 3–12 km h−1. Linear regression and ANOVA tests were among the measures used to assess the accuracy of the developed system. Results of this study revealed that the developed system enabled an efficient automatic setting of the desired fertilizer application rate. The investigated operating speeds showed no significant effects on the performance of the system (P > F = 0.7968). In addition, the system exhibited an overall application rate error (difference between desired and actual application rate) of ±2.6%. The response time of the developed system was found to range between 6 and 11 ms for a change of one kilogram in the application rate. [ABSTRACT FROM AUTHOR]

A survey of Australian grain growers was conducted to gauge grower attitudes to crop and soil sensing and their role in nitrogen fertilizer management. The technologies considered were yield monitors, remote and proximal crop sensing, high resolution soil survey, soil moisture sensing and digital elevation models (DEM). Whereas Australian grain growers have readily adopted machine guidance and autosteer, and a majority have access to yield monitoring, the rate of use of many crop and soil sensors remains comparatively low. However, the survey results suggest a positive effect on sensor adoption through present use of yield mapping. Access to yield maps was significantly associated with the use of remotely sensed imagery, high resolution soil survey, soil moisture sensing, DEM and variable rate application of fertilizers and/or soil amendments. There is some support for proximal crop sensing, albeit with low present rates of use; the use of soil water sensors and DEM is presently very low. For the further development of precision agriculture (PA), the results make clear that expending effort in enhancing the adoption and use of yield maps would be valuable as a lever to gain 'buy-in' from growers to sensing and PA more broadly. Since growers use many sources of information to support fertilizer decision-making, any new fertilizer decision aid needs to establish a point of difference from, but be complementary to, existing tools. One way of achieving this would be to use sensors, supported by locally derived algorithms, as a key input to fertilizer decision support. [ABSTRACT FROM AUTHOR]

Canopy characterisation is a key factor for the success and efficiency of the pesticide application process in vineyards. Canopy measurements to determine the optimal volume rate are currently conducted manually, which is time-consuming and limits the adoption of precise methods for volume rate selection. Therefore, automated methods for canopy characterisation must be established using a rapid and reliable technology capable of providing precise information about crop structure. This research providedregression models for obtaining canopy characteristics of vineyards from unmanned aerial vehicle (UAV) and satellite images collected in three significant growth stages. Between 2018 and 2019, a total of 1400 vines were characterised manually and remotely using a UAV and a satellite-based technology. The information collected from the sampled vines was analysed by two different procedures. First, a linear relationship between the manual and remote sensing data was investigated considering every single vine as a data point. Second, the vines were clustered based on three vigour levels in the parcel, and regression models were fitted to the average values of the ground-based and remote sensing-estimated canopy parameters. Remote sensing could detect the changes in canopy characteristics associated with vegetation growth. The combination of normalised differential vegetation index (NDVI) and projected area extracted from the UAV images is correlated with the tree row volume (TRV) when raw point data were used. This relationship was improved and extended to canopy height, width, leaf wall area, and TRV when the data were clustered. Similarly, satellite-based NDVI yielded moderate coefficients of determination for canopy width with raw point data, and for canopy width, height, and TRV when the vines were clustered according to the vigour. The proposed approach should facilitate the estimation of canopy characteristics in each area of a field using a cost-effective, simple, and reliable technology, allowing variable rate application in vineyards.

Traditional sprayers adopt large-area uniform pesticide application, resulting in a low effective utilisation of pesticide and a harmful effect to the natural environment. Variable-rate spray is the key point to precision chemical application. However, it is inevitable that the spray pressure sharply fluctuates during variable-rate spray, which will definitely influence the spray characteristics, such as spray droplet sizes, spray angles, spray droplet velocities, etc., and reduce the efficiency of pesticide applications. Therefore, the research on how to keep the spray pressure constant during the process of variable- rate spray has practical significance to precision pesticide applications. In order to achieve the stability of spray pressure for variable- rate spray, a sprayer with constant-pressure control was set up using a closed-loop proportion integration differentiation (PID) controller of constant-pressure water-supply, which employed the techniques of single-phase alternating current (AC) chopper variablevoltage control and PID feedback regulation. Using hollow-cone nozzles, the spray volume was changed by adjusting spray pressure, frequency, and duty cycle of electromagnetic valve switching. The spray features concerning the spray angle and the spray volume distribution were studied in laboratory. The conclusions are as follows: i) for a given input spray pressure, the closed-loop controller with AC chopper and PID feedback regulation can effectively control the pressure fluctuations during the variable-rate spray through varying the frequency and the duty cycle; ii) the spray angle was slightly affected by the flow-rate change through adjusting the frequency and the duty cycle of electromagnetic valves or changing the number of open nozzles. The spray angle changes were controlled within the range of the maximum deviation 0.87° from mean value and the minimum deviation 0.03° from mean value; iii) when the spray pressure was set as 0.3 Mpa, the peak radial position of the spray volume distribution basically unchanged and the spray volume and the peak values of the spray distribution gradually increased with the increasing duty cycle. When the setting pressure was increased, the spray volume increased and the peak radial position of the spray distribution gradually expanded toward outside, however the peak value of the spray distribution decreased a little.

The uniform application (UA) of agrochemicals results in the over-application of harmful chemicals, increases crop input costs, and deteriorates the environment when compared with variable rate application (VA). A smart variable rate sprayer (SVRS) was designed, developed, and tested using deep learning (DL) for VA application of agrochemicals. Real-time testing of the SVRS took place for detecting and spraying and/or skipping lambsquarters weed and early blight infected and healthy potato plants. About 24,000 images were collected from potato fields in Prince Edward Island and New Brunswick under varying sunny, cloudy, and partly cloudy conditions and processed/trained using YOLOv3 and tiny-YOLOv3 models. Due to faster performance, the tiny-YOLOv3 was chosen to deploy in SVRS. A laboratory experiment was designed under factorial arrangements, where the two spraying techniques (UA and VA) and the three weather conditions (cloudy, partly cloudy, and sunny) were the two independent variables with spray volume consumption as a response variable. The experimental treatments had six repetitions in a 2 × 3 factorial design. Results of the two-way ANOVA showed a significant effect of spraying application techniques on volume consumption of spraying liquid (p-value < 0.05). There was no significant effect of weather conditions and interactions between the two independent variables on volume consumption during weeds and simulated diseased plant detection experiments (p-value > 0.05). The SVRS was able to save 42 and 43% spraying liquid during weeds and simulated diseased plant detection experiments, respectively. Water sensitive papers’ analysis showed the applicability of SVRS for VA with >40% savings of spraying liquid by SVRS when compared with UA. Field applications of this technique would reduce the crop input costs and the environmental risks in conditions (weed and disease) like experimental testing.

The existing seeding devices not fully provide quality of application of mineral fertilizers. Unevenness and instability of application reach 20–40% at demanded to 15%. It, first of all, associated with the absorption of fertilizers and imperfection of seeding devices for fertilizer. For a solution of the problem of ensuring quality of application of mineral fertilizers at their intra soil differentiated application the original design of the pin reel of a seeding device is offered. Pins of the reel are executed in the form of the tetrahedral truncated pyramids located on crossing right and left multiple-helical lines on a reel surface. Theoretical researches relied on regularities of classical mechanics. As a result of theoretical researches the expression connecting all key design and technological parameters is received: reel radius, number of pins on it, their height and the sizes of the top and lower basis, diameter of a driving wheel, rate of application, aisle width, and transfer ratio of the driving mechanism.