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Crop health assessment and early yield predictions are highly crucial under biotic stress conditions for crop management and market planning by farmers and policy planners. The objective of this study was, therefore, to assess the impact of different levels of wilt disease on the biophysical parameters of chickpea and developing machine learning (ML) models for early yield prediction. Field experiments were carried out over three years at the Indian Agricultural Research Institute research farm in New Delhi. Thermal and visible images were collected alongside the measurement of crop biophysical parameters, including leaf area index (LAI), photosynthesis, transpiration rate, stomatal conductance, relative leaf water content (RWC), membrane stability index (MSI), and NDVI, for 85 chickpea genotypes with varying levels of wilt resistance. ML models were developed for early yield prediction by combining visible and thermal image indices with biophysical parameters. The results showed that the canopy temperatures were directly correlated with increasing levels of wilt severity. Crop photosynthesis, stomatal conductance, transpiration, LAI, RWC, MSI, and NDVI dropped significantly with increasing levels of wilt severity. Yield reductions of 44-69% were observed in susceptible genotypes. Machine learning models were able to give accurate early yield predictions. The accuracy of the models increases as we move closer to the harvest. Ranking of the model's performances indicated that XGB is the best model to predict chickpea yield under wilt conditions. NDVI was identified as most important variable for yield prediction. The findings of the study quantified the impacts of wilt on important crop biophysical parameters and highlighted the suitability of ML models in early yield prediction under different levels of disease severity. [ABSTRACT FROM AUTHOR]

The use of thermal-based crop water stress index (CWSI) has been studied in many crops in semi-arid regions and found as an effective method in detecting real-time crop water status of commercial fields remotely and non-destructively. However, to our knowledge, no previous studies have validated the usefulness of CWSI in a temperate crop like wild blueberries. Additionally, the temporal changes of the water status estimation model has not been well-studied. In this multi-year study, Unoccupied Aerial Vehicle (UAV)-borne thermal imageries were collected in 2019, 2020, and 2021 to test the temporal effects and the impact of different approach-based reference temperatures (Twet, wet reference temperature; Tdry, dry reference temperature) on leaf water potential (LWP) estimation models using CWSI in two large adjacent wild blueberry fields in Maine, United States. We found that different sampling dates have a significant impact on LWP estimation models using CWSISE (statistical Twet and empirical Tdry reference) and CWSISS (statistical Twet and statistical Tdry reference). Further, CWSIBB calculated with bio-indicator-based Twet and Tdry reference was found more effective (r² = 0.79) in estimating LWP in 2021, compared to the CWSISE and CWSISS approaches in 2019 (r² = 0.34 & r² = 0.36), 2020 (r² = 0.38 & r² = 0.44) and 2021 (r² = 0.43 & r² = 0.46). CWSIBB -LWP model-based crop water status maps show high variation in the crop water status of wild blueberries, even in an evenly irrigated field, suggesting the potential of UAV-borne thermal cameras to detect real-time crop water status within the field, with the CWSIBB calculated from bio-indicator-based references being more reliable. Our results could be used for precision irrigation to increase the overall water use efficiency and profitability of wild blueberry production. [ABSTRACT FROM AUTHOR]

Canopy temperature is an important parameter for monitoring the physiological status of maize. The study of its change law can help to improve the growth environment and increase the yield and quality. Based on the RGB, thermal infrared, and multispectral images obtained by an Unmanned Aerial Vehicle (UAV), two peak threshold methods were used in this study to eliminate the soil background and obtain the maize canopy information in the study area. Then, by using the radiation transmission model and Planck's law, the maize canopy temperature was extracted, and the correlation of canopy temperature change with the regional coverage and soil drought degree was analysed. Results indicated that the accuracy of canopy temperature extraction can be effectively enhanced by eliminating the influence of soil background. At jointing and filling stages, the Pearson correlation coefficients r of canopy temperature and coverage were 0.731 and 0.722, respectively, and the slope of the linear fitting model was less than 0, indicating that the coverage has a negative impact on canopy temperature, i.e., the higher the coverage, the lower the canopy temperature, and vice versa. Additionally, statistical analysis of the canopy temperature and temperature vegetation drought index (TVDI) clustering results at the jointing and filling stages demonstrated that the total overlapping rates of the categories were 72.5% and 41.0% respectively, suggesting that maize canopy temperature is related to regional soil drought degree, the change range of canopy temperature increases with the TVDI value, and the soil drought degree at the jointing stage has a large impact on maize canopy temperature change than that at the filling stage. This study provides technical support for extracting and analysing crop canopy temperature change by using UAV thermal infrared technology. [ABSTRACT FROM AUTHOR]

Awns illustrate an important photosynthetic organ of the cereal spike, especially under water stress. Barley (Hordeum vulgare L.) spikes are distinguished by long awns; nevertheless, the actual changes in barley awn dimensions under drought stress have not been adequately studied. In the present work, six barley genotypes with varying degrees of drought tolerance were investigated in a 2-year field experiment under well-watered (WW) and terminal drought stress (TDS) conditions based on randomized complete block design with three replications. The results showed that although TDS caused a notable reduction in awn dimensions compared to WW, the density of barbs on the awn surface significantly increased in all barley genotypes. In addition, the barb contact angle decreased significantly under TDS conditions compared to WW. Awn area had a significantly positive correlation with thousand grain weight and grain yield in both treatments and growing seasons. Also, highly significant correlations were observed between leaf stomatal conductance, awn area, and barb density under both conditions. In TDS, two genotypes demonstrated lower carbon isotope discrimination: Morocco, with higher awn dimensions, and PBYT 46, with a higher increase in barbs density on the awn surface and a lower canopy temperature. This research underlines barbs density on the awn surface by reflecting the light and a lower increase in the canopy temperature plays a more pivotal role than awn dimensions on water use efficiency in barley under terminal drought. [ABSTRACT FROM AUTHOR]

Intergovernmental panel on climate change has predicted a 1.5 °C increase in temperature and a 4–27% decrease in precipitation in the next decade. Drought and heat cause 60% and 40% wheat yield losses by altering the physiology of the crop. Understanding the impact of these stresses on wheat physiological traits can help in developing tolerant genotypes. Forty-two genotypes were evaluated under non-stress (TSIR-NS), drought (TSRF-DR), and heat stress (LSIR-HT) for two crop seasons. The experiments were laid in rectangular lattice (6 × 7) design with two replications. Data for grain yield and various physiological traits were recorded at GS70 and GS75. Grain yield (GY) was reduced by 29.0%, 16.4% under heat, and by 48.7% and 30.2% under drought stress during the two seasons. Heat susceptibility index (HSI) and drought susceptibility index (DSI) of these genotypes ranged from 0.3 to 1.8 and 0.4 to 1.4. Among the top five high-yielding lines under TSIR-NS, only one (G39) was drought tolerant, while under LSIR-HT, four high-yielding lines (G40, G41, G04, and G35) of the top five genotypes were heat tolerant. Conversely, the top five high-yielding lines under TSRF-DR were drought tolerant. Overall, 21 lines were tolerant to heat stress and 22 to drought and 12 genotypes were tolerant to both stresses. Chlorophyll was higher under LSIR-HT and TSRF-DR. Normalized difference vegetation index at GS70 (NDVIA) and normalized difference vegetation index at GS75 (NDVI15DAA) suffered a drastic reduction under TSRF-DR. The stomatal conductance (gs) and transpiration (E) were greatly reduced under drought and increased under LSIR-HT. Assimilation (A) and photosynthetic water use efficiency (WUE) were reduced under heat stress. The correlation of physiological traits with GY was also calculated. The traits chlorophyll fluorescence at GS70 (CFLA), NDVI 15DAA, and E contributed toward GY under TSIR-NS, CFLA, NDVI15DAA, WUE, Canopy temperature at GS75 (CT15DAA), and gs under TSRF-DR, and A, NDVIA, CFLA, and chlorophyll fluorescence at GS75 (CFL15DAA) under LSIR-HT. CFLA contributed toward GY under all environments. Finally, traits such as A, E, WUE, and CFL15DAA were impacted by both stresses, while NDVIA and NDVI15DAA and gs were only affected by drought and CFLA and CTA were only influenced by heat stress. [ABSTRACT FROM AUTHOR]

There is growing interest in using canopy temperature (Tc), including crop water Stress index (CWSI), for irrigation management. However, Tc varies greatly in one day, while soil water content (SWC) varies little, which may lead to different conclusions on whether irrigation is needed based on CWSI at different times. For this end, Tc of winter wheat was continuously monitored, and the data of such environmental factors as atmospheric temperature and soil water content (SWC) were simultaneously collected. CWSI was calculated based on empirical formulation and Tc and CWSI were generalized based on the normalization formulation. The correlation SWC between Tc and CWSI before and after generalization was compared and error analysis was based on SWC theoretical formula. The results showed: (1) the accuracy of SWC retrieval by Tc and CWSI increased firstly and then decreased with time during the day. The optimal time for Tc monitoring SWC was between 10:00 ~ 16:00 (R2 > 0.72) and the optimal time for CWSI monitoring SWC was between 9:00 ~ 18:00 (R2 > 0.69). (2) CWSI and Tc were mapped based on the relationship between crop water stress and soil water deficit and normalized canopy temperature expressions characterized the relationship between crop water stress and soil water deficit. (3) The accuracy of inversion of SWC by mapping Tc from 18:00 ~ 8:00 is increased from 0.5 ~ 0.6 to 0.7 ~ 0.8; the accuracy of soil water content inversion by mapping CWSI from 18:00 ~ 8:00 was improved from 0.2 ~ 0.4 to 0.4 ~ 0.6. (4) The theoretical expression of SWC deduced based on CWSI also proves that considering the relationship between crop water stress and soil water deficit change can effectively reduce the relative error from 30 to 5% in the morning and evening. This study contributes to the understanding of the reason why the correlation between Tc and SWC varies greatly during the day and solves the time-limited problem of thermal infrared remote sensing monitoring of crop water stress. [ABSTRACT FROM AUTHOR]

Aims: Quantitative characterization of the time-lag effect between canopy temperature and atmospheric temperature and its controlling factors in the agricultural ecosystem may contribute to a higher inversion accuracy of soil water content using canopy-air temperature information. Methods: Tc of winter wheat were continuously monitored, and the data of such environmental factors as solar radiation (Rs), atmospheric temperature (Ta), relative humidity (RH) and soil water content (SWC) were simultaneously collected. Results: Hysteresis existed between Tc and Ta over the diel cycles, and different weather and irrigation levels did not change the direction of the time lag loop. the key driver regulating the diel hysteresis pattern between Tc and Ta varied under different weather: on rainy days, key driver was Rs while on cloudy and sunny days, the key driver was RH. the multiple regression model indicated that together Rs, Ta, RH, and SWC explained 58 ± 10% of the variation of time-lag effect. Path analysis showed on rainy days the key driver (Rs and RH) could enhance the time-lag effect through other indirect factors (Ta and SWC); on cloud days the key driver (RH and SWC) could inhibit the time-lag effect through other indirect factors (Ta); On sunny days this mutual inhibition was further significant. Conclusions: These findings indicated a dynamic process of time-lag effect between Tc and Ta with different weather and different irrigation levels. This study contributes to the understanding of the time-lag effect and its driving factors and this analysis provides the basis for further improvement in monitoring crop water deficit. [ABSTRACT FROM AUTHOR]

Crop water stress index (CWSI) has been widely used in soil moisture monitoring. However, the influence of the time lag effect between canopy temperature and air temperature on the accuracy of soil moisture monitoring with different CWSI models has not been further investigated. Therefore, based on the continuous record of canopy temperature and air temperature, this study explored the influence of canopy-air temperature hysteresis on the diagnosis of soil moisture with three CWSI models (CWSIT—theoretical, CWSIE—empirical, CWSIH—hybrid). The results show (1) the peak time of canopy temperature was ahead of that of air temperature, and the lag time varied under different soil moisture conditions. When the soil moisture was seriously deficient, the lag time decreased. However, from jointing-heading period to filling-ripening period, the lag time became longer. (2) The values of CWSIT, CWSIE, and CWSIH decreased when the time lag effect was considered. In jointing-heading period, heading-filling period, and filling-ripening period, CWSIT had the highest accuracy in soil moisture monitoring without the consideration of the time lag effect. When the time lag effect was considered, the monitoring accuracy of CWSIE and CWSIH was greatly improved and higher than that of CWSIT, while that of CWSIT was reduced. The findings provided a basis for further improving the accuracy of soil moisture monitoring with CWSI models. [ABSTRACT FROM AUTHOR]

Pre-exposure of plants to moderate stress may create a "stress memory" that enables a fast protective response to next stress event. Protocols to differentiate the crop genotypes for such traits are essential for genetic improvement of plant stress tolerance. We hypothesized that memory due to soil moisture deficit may get reflected in leaf canopy temperature responses to fluctuating ambient temperature. We conducted a factorial completely randomized block design experiment with two treatments: well-watered (WW) and water deficit stress (WS) in three and two popular cultivars of sugarcane and sorghum, respectively, with eight replications. The 30 and 15 days old plants of sugarcane and sorghum were exposed to soil moisture deficit for 25 and 10 days, respectively, followed by their recovery. For differentiating the response of plants after stress recovery (ASR), the WW and WS plants were labelled as WWASR and WSASR, respectively. After 30 days of recovery, canopy temperature responses to ambient temperature fluctuations were monitored. Warming of the canopy was faster in WSASR as compared to WWASR plants. Further, there was a difference within the WSASR cultivars, indicating the existence of genetic variation in recalling and responding to desiccation caused by increasing ambient temperature. These indicated that water exhaustive and non-exhaustive C4 plants differ in their adaptive mechanisms associated with transpirational cooling of the plant canopy. The pixel-wise analysis of thermal images of plants provided a more effective differentiation of stress responses among sugarcane and sorghum cultivars compared to the conventional method of recording canopy temperature. The existence of cultivar differences suggests that the trait representing stress memory can be phenotyped with protocols optimized with the IR imaging system and may serve as a promising tool for phenotyping crop stress tolerance. [ABSTRACT FROM AUTHOR]

The crucial obstacle in the research of plant science is to distinguish between superior genotypes and its selection. Machine vision can recognize the better genotype precisely, effortlessly and in asymptomatic manner. Genotype differentiation based on machine vision by using thermal imaging parameters is evaluated and elaborated in this article. The main objective of this study is to assess better performance and select superior genotypes based on the common thermal indicators. Mungbean and chickpea crops were studied and measured in greenhouse conditions with well-watered and water stress treatments. An algorithm is developed for extracting parameters from thermal images and implemented in a Python tool using OpenCV (cv2), Pandas packages. The genotypes of the crops were contrasted for drought tolerance to be able to differentiate drought responses with thermal imaging. The result of the experiments express that crops are differentiated between treatments and discriminated among genotypes within a treatment. These results were validated with the soil moisture data, which was collected on simultaneous day of the image captured. [ABSTRACT FROM AUTHOR]

Non-invasive techniques such as RGB, thermal imaging, and optical sensing can provide information on plant water stress before it occurs. In the present study, the maize crop (Var. DMRH 1301) was cultivated with three different sowing dates (1, 15, and 30 June) under rainfed condition. The RGB and thermal images along with crop growth parameters were recorded periodically along with soil moisture in maize. A deep learning model (DarkNet53) was applied on RGB as well as thermal images for the identification of water stress. The occurrence of rainfall and thereby soil moisture had a significant effect on crop canopy temperature under different dates of sowing. The canopy temperature histogram under water stress showed bimodal distribution whereas unimodal distribution was observed under non-stress condition. The maximum accuracy of the DarkNet53 model at 20 epochs and 8 batch size for the identification of water stress in RGB and thermal imagery was 94.1 and 99.6%, respectively. The testing accuracy of identification of water stress for RGB imagery were 94.1, 90.5, and 92.3% and for thermal imagery 95.7, 91.5, and 99.6% in early, timely and late sown maize, respectively. Overall, thermal imagery input had higher accuracy for water stress identification. The present study showed that non-destructive, rapid vision-based techniques using digital imagery can helpto characterize plant growth, water stress, and thereby, yield. [ABSTRACT FROM AUTHOR]

Canopy temperature (CT) is considered a reliable proxy for stomatal conductance. Low CT values of plant canopies under water-limited conditions are associated with high transpiration indicating plants' drought tolerance. Many U.S. Pacific Northwest (PNW) adapted wheat (Triticum aestivum L.) cultivars lack stress-adaptive traits resulting in poor performance in drought environments. This study aims to identify the stress-adaptive traits by evaluating the CT in spring wheat populations across different soil moisture conditions in the PNW. An infrared thermometer was used to estimate the CT in two families of recombinant inbred lines, 'Alpowa' × 'Express' (AE population) and 'Hollis' × 'Drysdale' (HD population), in rainfed and irrigated environments of the dryland PNW in 2011 to 2013. Higher reductions in grain yield up to 170%, spike length up to 25%, and spikelets spike−1 up to 19% were observed in a rainfed environment compared to the reductions in an irrigated environment. A significant variation in CT was observed in both AE and HD populations. With 1 °C increase in CT at the anthesis stage, grain yield was lowered up to 38 g m−2. Low CT was associated with high grain yield and agronomic traits in both wheat populations (r = − 0.18 to − 0.55, P ≤ 0.05). The highest association between CT and grain yield was observed at anthesis (r = − 0.47) and milking (r = − 0.38) stages (P ≤ 0.001). Our results show that screening for low CT during terminal wheat growth stage is an effective strategy for improving the selection of new drought-tolerant wheat varieties in the PNW. [ABSTRACT FROM AUTHOR]

As the ground-based instruments for measuring net radiation are costly and need to be handled skillfully, the net radiation data at spatial and temporal scales over Indian subcontinent are scanty. Sometimes, it is necessary to use other meteorological parameters to estimate the value of net radiation, although the prediction may vary based on season, ground cover and estimation method. In this context, artificial intelligence can be used as a powerful tool for predicting the data considering past observed data. This paper proposes a novel method to predict the net radiation for five crop surfaces using global solar radiation and canopy temperature. This contribution includes the generation of real-time data for five crops grown in West Bengal state of India. After manual analysis and data preprocessing, data normalization has been done before applying machine learning approaches for training a robust model. We have presented the comparison in various machine learning algorithm such as ridge and spline regression, random forest, ensemble and deep neural networks. The result shows that the gradient boosting regression and ridge regression are outperforming other ML approaches. The estimated predictors enable to reduce the number of resources in terms of time, cost and manpower for proper net radiation estimation. Thus, the problem of predicting net radiation over various crop surfaces can be sorted out through ML algorithm. [ABSTRACT FROM AUTHOR]

In sugarcane production, irrigation scheduling based on accurate estimation of the water status is essential to ensure profitable yields. Water stress causes an increment in leaf surface temperature since stomatal closure reduces transpiration; therefore, canopy temperature (CT) is recognized as a useful tool for water stress detection. Thermography could be an alternative way, yet few reports are available on its use in sugarcane. In the present study, we established well-watered and water-stressed sugarcane canopies and acquired CT readings and thermal images, concurrently measuring photosynthesis under a greenhouse condition to validate the usability of thermography. Water suspension raised the soil pF up to the permanent wilting point, putting the water-stressed canopy under a highly dry condition. The shortage of water hardly altered the appearance of sugarcane but significantly dropped photosynthesis and transpiration rates, resulting in increasing CT. The colorings of thermal images were also greatly affected by the water stress; consequently, it became easier to distinguish the well-watered and water-stressed canopies. R gray level was most sensitive to the changes in CT among colors, suggesting that it could be a good indicator of sugarcane water status. Since crop water stress index calculated from transpiration rate was well correlated with the differences of CT and R gray level between the canopies, our study demonstrated that both CT measurement and thermography are useful for water stress detection, however, considering its ease of evaluation and small differences of CT in case of bad weather, the use of thermal images could be more recommended. [ABSTRACT FROM AUTHOR]

This paper shows some practical experiences of using unmanned aerial vehicles-based platform for remote sensing in supporting precision agriculture mapping. There have been studies on unmanned aerial vehicles used to calculate plant water stress; however, the scientific reports of drone images that are used to predict best time and height are rare. The trial was conducted during 2020, in a five-year-old Anji tea plant experimental field, where drone captures images in a different time series of 27 flights during experimental days. This work aims to (1) investigate the appropriate thermography timing and altitude based on unmanned aerial vehicles remote sensing, (2) conduct a quantitative and qualitative study of various thermal orthomosaics and photographs, (3) establish workflow for high-resolution remote sensing application. All flights were operated at 3 m/s flying speed. Flights were performed during the testing day at about 09:00 h, 11:00 h, and 13:00 h. The drone images were taken at relative flying heights of 25 m, 40 m, and 60 m each day. The relationship between canopy temperature and plant-based variables was also established. The results reported that flights at 11:00 h and 60-m altitude orthomosaic could provide the best relation and accurate canopy temperature. On the other hand, the high relationship between stomatal conductance and canopy temperature was R2 0.98 at 11:00 h. The selection of optimal timing and altitude can provide rapid and reliable canopy temperature information. Overall, high resolution with low-altitude unmanned aerial vehicles images proved good relationship in order to assess the canopy temperature. [ABSTRACT FROM AUTHOR]

Experiments were conducted to develop and assess a method by which the water content of a lettuce canopy can be nondestructively detected and estimated using a combination of spectra, RGB images, and canopy temperature. To this end, 130 lettuce samples grown in four different substrate water content levels were collected for data acquisition. In the spectroscopy procedure, five spectral intervals (380 variables) were selected by backward interval partial least squares and were further reduced to 48 wavelength variables, chosen using a genetic algorithm based on Savitzky–Golay smoothing and log (1/R) transformation. Then, 967, 1170, 1221, 1406, 1484, 1942, and 1985 nm optimum spectral variables were selected by the successive projection algorithm. Thirteen plant features were extracted from top- and front-view RGB images. These features comprised morphological, color, and textural features. An empirical crop water stress index was established based on dry and wet reference surfaces via thermal imagery. Subsequently, a principal component analysis was applied to the spectral variables and the image features, and an extreme learning machine was used to construct the multisensor and single-sensor models. The results show that the multisensor model had a correlation coefficient of prediction of 0.9018, which was found to be approximately 9.4 and 15.7% better than that of the spectral and image models. This work demonstrates that integrating spectra, RGB images, and canopy temperature with suitable algorithms offers a high potential for use in the nondestructive measurement of water content in lettuce, considerably improving accuracy over that using a single-sensor modality. [ABSTRACT FROM AUTHOR]

There are few whole-canopy or ecosystem scale assessments of the interplay between canopy temperature and photosynthesis across both spatial and temporal scales. The stable oxygen isotope ratio (δ18O) of plant cellulose can be used to resolve a photosynthesis-weighted estimate of canopy temperature, but the method requires independent confirmation. We compare isotope-resolved canopy temperatures derived from multi-year homogenization of tree cellulose δ18O to canopy-air temperatures weighted by gross primary productivity (GPP) at multiple sites, ranging from warm temperate to boreal and subalpine forests. We also perform a sensitivity analysis for isotope-resolved canopy temperatures that showed errors in plant source water δ18O lead to the largest errors in canopy temperature estimation. The relationship between isotope-resolved canopy temperatures and GPP-weighted air temperatures was highly significant across sites (p < 0.0001, R2 = 0.82), thus offering confirmation of the isotope approach. The previously observed temperature invariance from temperate to boreal biomes was confirmed, but the greater elevation of canopy temperature above air temperature in the boreal forest was not. Based on the current analysis, we conclude that canopy temperatures in the boreal forest are as warm as those in temperate systems because day-time-growing-season air temperatures are similarly warm. [ABSTRACT FROM AUTHOR]

Background: Traditional rice (Oryza sativa) varieties are valuable resources for the improvement of drought resistance. qDTY3.2 is a drought-yield quantitative trait locus that was identified in a population derived from the traditional variety Moroberekan and the drought-susceptible variety Swarna. In this study, our aim was to characterize the physiological mechanisms associated with qDTY3.2. Our approach was to phenotype fifteen BC2F3.4 lines for shoot and root drought resistance-related traits as compared to Swarna in the field under well-watered and drought stress conditions. Four BC2F3.4 lines contrasting for yield under drought were selected for detailed characterization of shoot morphology, water use related traits, flowering time and root system architecture in the field as well as in controlled environments (lysimeters in a greenhouse, and gel imaging platform in a growth chamber). Results: Across five field experiments, grain yield correlated significantly with root growth along the soil profile, flowering time, and canopy temperature under drought conditions. The four selected BC2F3.4 lines showed earlier flowering time, reduced distribution of root growth to shallow soil layers which resulted in lower water uptake (between 0 and 30 cm) and drought-induced increased distribution of root growth to deep soil layers (between 30 and 60 cm) as compared to Swarna in the field. Root system architecture phenotypes were confirmed in whole root systems in lysimeters, and corresponded to higher numbers of root tips in a gel imaging platform, highlighting the potential stability of some root traits across different growth stages and systems. Conclusions: We conclude that earlier flowering time, reduced shallow root growth, and drought-induced increased deep root growth are associated with the presence of qDTY3.2 since these phenotypes were consistently observed in the selected QTL lines with full introgression of qDTY3.2. We hypothesize that the qDTY3.2 associated RSA phenotypes led to better use of water and metabolic resources which, combined with earlier flowering time, improved yield under drought. [ABSTRACT FROM AUTHOR]

Site-specific crop management is a promising approach to maximize crop yield with optimal use of rapidly depleting natural resources. Availability of high resolution crop data at critical growth stages is a key for real-time data-driven decisions during the production season. The goal of this study was to evaluate the possibility of using small unmanned aerial system (UAS)-based remote sensing technologies to monitor the crop stress of irrigated pinto beans (Phaseolus vulgaris L.) with varied irrigation and tillage treatments. A small UAS with onboard multispectral and infrared thermal imaging sensors was used to collect data from bean field plots on three growth stages in 2015 and 2016, respectively. Indicators including green normalized vegetation index (GNDVI), canopy cover (CC, ratio of ground covered by crop canopy to the total plot area) and canopy temperature (CT, °C) of crops were extracted from imaging data and correlated with ground-reference crop yield and leaf area index (LAI) estimated with a handheld ceptometer. Results show that GNDVI, CC and CT were able to differentiate crops with full and deficit irrigation treatments at each of the three growth stages in both years. Developed indicators were strongly correlated with to the crop yield with Pearson correlation coefficients (r) of approximate 0.71 and 0.72 for GNDVI and CC, respectively, in the early growth stage (54 days after planting) in both years. Canopy temperature showed even stronger correlation (r > 0.8) with yield at early growth stage. Performance of small UAS-based imagery-based indicators in crop stress monitoring and crop yield estimation was better than or comparable to that of the ground-based LAI estimates, indicating the potential of such remote sensing tool in rapid crop stress monitoring and management. [ABSTRACT FROM AUTHOR]

Although the information on the Normalized Difference Vegetation Index (NDVI) in plants under water deficit is often obtained from sensors attached to satellites, the increasing data acquisition with portable sensors has wide applicability in agricultural production because it is a fast, nondestructive method, and is less prone to interference problems. Thus, we carried out a set of experiments to investigate the influence of time, spatial plant arrangements, sampling size, height of the sensor and water regimes on NDVI readings in different soybean cultivars in greenhouse and field trials during the crop seasons 2011/12, 2012/13 and 2013/14. In experiments where plants were always evaluated under well-watered conditions, we observed that 9 a.m. was the most suitable time for NDVI readings regardless of the soybean cultivar, spatial arrangement or environment. Furthermore, there was no difference among NDVI readings in relation to the sampling size, regardless of the date or cultivar. We also observed that NDVI tended to decrease according to the higher height of the sensor in relation to the canopy top, with higher values tending to be at 0.8 m, but with no significant difference relative to 1.0 m-the height we adopted in our experiments. When different water regimes were induced under field conditions, NDVI readings measured at 9 a.m. by using a portable sensor were successful to differentiate soybean cultivars with contrasting responses to drought. [ABSTRACT FROM AUTHOR]

Irrigation scheduling is important in irrigation water management. In this study, full (FI), deficit (DI) and partial root drying (PRD) irrigation strategies were applied in Agria and Ramos potato cultivars. Canopy temperature (CT) and leaf water potential (LWP) were assessed as the potential tools for irrigation scheduling during the vegetative and productive growth stages. LWP varied between ca. -1.4 and -1.6 MPa and was not significantly different between FI, DI and PRD irrigation treatments. The LWP and CT values were not significant between the two potato cultivars during the measurements, but CT was frequently significant between the irrigation treatments such that the PRD treatments were significantly 5 and 2 oC warmer than FI and DI treatments, respectively. Higher CT in PRD caused significant yield penalty such that DI and FI produced almost two times higher fresh tuber yield. Analysis revealed that CT had significantly higher correlation (r=0.66) with water productivity (weight of fresh tuber yield divided by volume of applied irrigation water) than LWP, which its correlation was not significant (r=0.40). This showed that crop water productivity could be better controlled by CT. Moreover, it was realized that the seven-day irrigation interval was far longer than required and potatoes in all irrigation treatments were under water stress, especially in PRD, that might partially explain the 50% yield penalty of PRD compared to DI. The reason was that due to high atmospheric demand, soil water in the wet part of the root system would be completely depleted earlier by the time of next irrigation event. Conclusively, the CT is recommended as a more reliable crop water status and irrigation scheduling indicator than LWP in response to different water-saving irrigation managements. [ABSTRACT FROM AUTHOR]

Background: Grain yield of wheat is primarily determined by both grain number and grain weight, which often influence each other in response to environmental stimuli. Some of the genotypes are capable of maintaining high single grain weight (SGW) across the environments. Understanding mechanisms and factors associated with the superiority of such genotypes over others is necessary to enhance productivity of wheat. Results: Experiments were conducted to elucidate the physiological basis of high SGW of LOK-1, a wheat cultivar grown in dry and hot environments in the central and peninsular zones of India. SGW of LOK-1 was least affected by removal of spikelets indicating little competition between the grains within the spike for assimilates. Reduction in SGW due to defoliation was less and the contribution of stem reserves to the grain development was high in LOK-1 relative to other cultivars. It seems that high level of expression of genes such as expansin (TaExpA6) contributes to the high SGW of LOK-1. Conclusions: Source was not a limiting factor for grain growth of LOK-1 in contrast to other cultivars, whereas sink appeared to be a limiting factor in recently released/identified cultivars. Differences in the amounts of water soluble stem carbohydrate reserves translocated to grain could be one of the factors contributing to higher grain weight in LOK-1. High level expression of TaExpA6, one of the genes contributing to the elongation of endosperm, seems to be crucial for grain growth in wheat. [ABSTRACT FROM AUTHOR]

Extreme heat waves and drought are predicted to increase in frequency and magnitude with climate change. These extreme events often co-occur, making it difficult to separate their direct and indirect effects on important ecophysiological and carbon cycling processes such as photosynthesis. Here, we assessed the independent and interactive effects of experimental heat waves and drought on photosynthesis in Andropogon gerardii, a dominant C grass in a native mesic grassland. We experimentally imposed a two-week heat wave at four intensity levels under two contrasting soil moisture regimes: a well-watered control and an extreme drought. There were three main findings from this study. First, the soil moisture regimes had large effects on canopy temperature, leading to extremely high temperatures under drought and low temperatures under well-watered conditions. Second, soil moisture mediated the photosynthetic response to heat; heat reduced photosynthesis under the well-watered control, but not under the extreme drought treatment. Third, the effects of heat on photosynthesis appeared to be driven by a direct thermal effect, not indirectly through other environmental or ecophysiological variables. These results suggest that while photosynthesis in this dominant C grass is sensitive to heat stress, this sensitivity can be overwhelmed by extreme drought stress. [ABSTRACT FROM AUTHOR]

Numerous agronomical applications of remote sensing have been proposed in recent years, including water stress assessment at field by thermal imagery. The miniaturization of thermal cameras allows carrying them onboard the unmanned aerial vehicles (UAVs), but these systems have no temperature control and, consequently, drifts during data acquisition have to be carefully corrected. This manuscript presents a comprehensive methodology for radiometric correction of UAV remotely-sensed thermal images to obtain (combined with visible and near-infrared data) multispectral ortho-mosaics, as a previous step for further image-based assessment of tree response to water stress. On summer 2013, UAV flights were performed over an apple tree orchard located in Southern France, and 4 dates and 5 h of the day were tested. The 6400 m field plot comprised 520 apple trees, half well-irrigated and half submitted to progressive summer water stress. Temperatures of four different on-ground stable reference targets were continuously measured by thermo-radiometers for radiometric calibration purposes. By using self-developed software, frames were automatically extracted from the thermal video files, and then radiometrically calibrated using the thermal targets data. Once ortho-mosaics were obtained, root mean squared error (RMSE) was calculated. The accuracy obtained allowed multi-temporal mosaic comparison. Results showed a good relationship between calibrated images and on-ground data. Significantly higher canopy temperatures were found in water-stressed trees compared to well-irrigated ones. As high resolution field ortho-mosaics were obtained, comparison between trees opens the possibility of using multispectral data as phenotypic variables for the characterization of individual plant response to drought. [ABSTRACT FROM AUTHOR]

Aegilops speltoides is an important genetic resource for wheat improvement and has high levels of heat tolerance. A heat-tolerant accession of Ae. speltoides pau3809 was crossed with Triticum durum cv. PDW274, and BC2F4-6 backcross introgression lines (BILs) were developed, phenotyped for important physiological traits, genotyped using SSR markers and used for mapping the QTL governing heat tolerance component traits. A set of 90 BILs was selected from preliminary evaluation of a broader set of 262 BILs under heat stress. Phenotyping was conducted for physiological traits such as cell membrane thermostability, chlorophyll content, acquired thermotolerance, canopy temperature and stay green. Much variation for these traits was observed in random as well as selected sets of BILs, and comparison of the BILs with the recurrent parent showed improvement for these traits under normal as well as heat stress conditions, indicating that introgressions from Ae. speltoides might have led to the improvement in the heat tolerance potential of the BILs. Introgression profiling of the 90 BILs using SSR markers identified Ae. speltoides introgression on all the 14 chromosomes with introgressions observed on A as well as B genome chromosomes. QTL mapping identified loci for various heat tolerance component traits on chromosomes 2B, 3A, 3B, 5A, 5B and 7A at significant LOD scores and with phenotypic contributions varying from 11.1 to 28.7 % for different traits. The heat-tolerant BILs and QTL reported in the present study form a potential resource that can be used for wheat germplasm enhancement for heat stress tolerance. [ABSTRACT FROM AUTHOR]

Characterizing the spatial variability in water status across vineyards is a prerequisite for precision irrigation. The crop water stress index (CWSI) indicator was used to map the spatial variability in water deficits across an 11-ha 'Pinot noir' vineyard. CWSI was determined based on canopy temperatures measured with infrared temperature sensors placed on top of well-watered and water-stressed grapevines in 2009 and 2010. CWSI was correlated with leaf water potential (Ψ) ( R = 0.83). This correlation was also tested with results from high resolution airborne thermal imagery. An unmanned aerial vehicle equipped with a thermal camera was flown over the vineyard at 07:30, 09:30, and 12:30 h (solar time) on 31 July 2009. At about the same time, Ψ was measured in 184 grapevines. The image obtained at 07:30 was not useful because it was not possible to separate soil from canopy temperatures. Using the airborne data, the correlation between CWSI and Ψ had an R value of 0.46 at 09:30 h and of 0.71 at 12:30 h, suggesting that the latter was the more favorable time for obtaining thermal images that were linked with Ψ values. A sensitivity analysis of varying pixel size showed that a 0.3 m pixel was needed for precise CWSI mapping. The CWSI maps thus obtained by airborne thermal imagery were effective in assessing the spatial variability of water stress across the vineyard. [ABSTRACT FROM AUTHOR]

This paper deals with the assessment of heterogeneity in water status in a commercial orchard, as a prerequisite for precision irrigation management. Remote sensing-derived indicators could be suitable for mapping water stress over large areas, and recent studies have demonstrated that high resolution airborne thermal imagery enables the assessment of discontinuous canopies as pure tree crowns can be targeted, thus eliminating the background effects. Airborne campaigns were conducted over a drip-irrigated commercial orchard in Southwestern Spain composed of five different orchard tree crops. An unmanned aerial vehicle with a thermal camera onboard was flown three times during the day on 8 July 2010, at 9, 11 and 13 h (local time). Stem water potential was measured at the same time of the flights. In some irrigation units, irrigation was stopped prior to the measurement date to induce water deficits for comparative purposes. Several approaches for using the thermal data were proposed. Daily evolution of the differential between canopy and air temperature ( T − T) was compared to tree water status. The slope of the evolution of T − T with time was well correlated with water status and is proposed as a novel indicator linked with the stomatal behavior. The Crop Water Stress Index (CWSI) was calculated with the temperature data from the 13.00 h flight using an empirical approach for defining the upper and lower limits of T − T. The assessment of variability in water status was also performed using differences in relative canopy temperatures. Ample variability was detected among and within irrigation units, demonstrating that the approach proposed was viable for precision irrigation management. The assessment led to the identification of water-stressed areas, and to the definition of threshold CWSI values and associated risks. Such thresholds may be used by growers for irrigation management based on crop developmental stages and economic considerations. [ABSTRACT FROM AUTHOR]

A wheat ( Triticum aestivum L.) recombinant inbred line (RIL) population was used to identify quantitative trait loci (QTL) associated with yield, yield components, and canopy temperature depression (CTD) under field conditions. The RIL population, consisting of 118 lines derived from a cross between the stress tolerant cultivar 'Halberd' and heat stress sensitive cultivar 'Karl92', was grown under optimal and late sown conditions to impose heat stress. Yield and yield components including biomass, spikes m, thousand kernel weight, kernel weight and kernel number per spike, as well as single kernel characteristics were determined. In addition, CTD was measured during both moderate (32-33 °C) and extreme heat stress (36-37 °C) during grain-filling. Yield traits showed moderate to high heritability across environments with a large percentage of the variance explained by genetic effects. Composite interval mapping detected 25 stable QTL for the 15 traits measured, with the amount of phenotypic variation explained by individual QTL ranging from 3.5 to 27.1 %. Two QTL for both yield and CTD were co-localized on chromosomes 3BL and 5DL and were independent of phenological QTL. At both loci, the allele from Halberd was associated with both higher yield and a cooler crop canopy. The QTL on 3BL was also pleiotropic for biomass, spikes m, and heat susceptibility index. This region as well as other QTL identified in this study may serve as potential targets for fine mapping and marker assisted selection for improving yield potential and stress adaptation of wheat. [ABSTRACT FROM AUTHOR]

Heat tolerance in 45 chickpea, lentil, and faba bean genotypes was investigated during 2007/2008 and 2008/2009 at Alexandria Agriculture Research Station, Alexandria, Egypt, using screening methods employing the membrane thermostability technique. Threshold temperature to be used in screening for heat tolerance at germination was also investigated for each crop. Temperatures, responsible for 50% germination were 40, 33.5, and 29°C for chickpea, faba bean, and lentil, respectively. Germination percent under high temperature varied significantly ( P ≤ 0.05) amongst genotypes. Germination percentage ranged from 4.8 to 71.6, 39.2 to 90.0, and 4.8 to 68.6, in chickpea, lentil, and faba bean, respectively. Differences were significant ( P ≤ 0.05) among faba bean and chickpea genotypes. Membrane relative injury (RI%) showed significant ( P ≤ 0.05) variability among the genotypes and ranged from 10.57 to 58, 5.2 to 61.7, and 15.7 to 52.7 in chickpea, lentil, and faba bean, respectively. Canopy temperature was measured to evaluate heat avoidance in tested genotypes. Infra-red thermometry was used to measure canopy temperature and the gradient of canopy to ambient air temperature (∆T) in moisture stressed and unstressed treatments. Canopy temperature, leaf water potential (LWP) and leaf water content were affected by the level of soil moisture. Genotypes were able to bring their canopy temperatures to levels lower than ambient air temperatures but the differences were not significant. A heat stress index (HSI) were computed relating the ∆T in moisture stressed to unstressed treatments. Regression of leaf water potential (LWP) and the heat stress index (HSI) was significant ( P ≤ 0.05) in faba bean genotypes in the stressful environment. The results of the present investigation emphasize the efficiency of membrane thermostability technique in selection for heat tolerance in early stages of growth in food legumes. [ABSTRACT FROM AUTHOR]

Drought is one of the important factors limiting crop production in arid and semi-arid regions. Four drought resistance criteria, consisting of canopy temperature, stomatal resistance, transpiring area and rate of water loss by excised-leaves were examined in two experiments conducted in optimum and stress moisture conditions in 1995. A randomized complete block design with four replications and six genotypes was used for each experiment. Under optimum moisture conditions, there was a significant difference in canopy temperature at midday between drought resistant and drought sensitive genotypes. A significant linear relationship was also obtained between this criterion and yield reduction ratio as determined by Yr = 1-(Ys/Yp) (Ys and Yp = yield under stress and non-stress conditions, respectively), at the ear emergence stage. The only significant difference between cultivars in respect to stomatal resistance in stress conditions was that of adaxial leaf surface in ear emergence stage. No significant linear relationship was obtained between transpiring area and yield reduction ratio. Cultivars differed in respect to rate of water loss (RWL) and initial water content (IWC) in ear emergence and grain filling in stress environment only. Ultimately, three criteria namely canopy temperature, stomatal resistance and RWL, in explicated conditions were recognized as benificial drought resistance indicators. [ABSTRACT FROM AUTHOR]