Optimization study on diagnostic methods for winter wheat water stress using UAV-borne thermal infrared imagery

Drone-based phenotyping using unmanned aerial vehicles (UAVs) has emerged as a revolutionary approach for high-throughput, precise, and scalable measurement of plant traits critical to crop improvement. This technology integrates advanced imaging sensors—including RGB, multispectral, hyperspectral, and thermal cameras—with sophisticated image processing and artificial intelligence algorithms to non-destructively capture key phenotypic data such as plant height, biomass, canopy temperature, maturity timing, and disease symptoms under natural field conditions. Compared with traditional manual phenotyping and satellite-based remote sensing, UAV phenotyping offers superior spatial and temporal resolution, enabling dynamic monitoring of complex traits such as drought tolerance and disease resistance. Applications span early stress detection, quantitative trait assessment, yield prediction, and accelerating breeding cycles by facilitating objective, rapid selection of superior genotypes across multiple crop species. Despite its transformative potential, challenges remain in standardizing protocols, managing large-scale complex datasets, integrating phenotypic with genomic and environmental data, and providing training resources for widespread adoption. Ongoing advancements in sensor technology, data analytics, open-source tools, and capacity building are poised to cement drone-based phenotyping as a cornerstone technology for sustainable, climate-resilient crop breeding and global food security.

Precision technologies are crucial for sustainable water management, as water scarcity and ineffective irrigation techniques continue to pose significant challenges in agriculture. One of the bases of plant-based irrigation scheduling is plant canopy temperature, which has become a reliable indicator of crop water status. The primary sensor technologies used to measure the temperature of leaves and canopies are discussed in this review, including integrated circuit sensors, thermistors, thermocouples, infrared thermometers, and infrared thermal imaging systems. Thermistors and thermocouples provide precise and affordable point-based measurements, but their scalability and installation are limited. For real-time canopy monitoring, infrared thermometers and thermal imaging provide non-contact options. Despite their higher price, thermal cameras enable the analysis of spatial variability. Low-cost irrigation system automation is made feasible by integrated circuit (IC) sensors, like the LM35, which combine accuracy and affordability. Research confirms that under deficit irrigation strategies, canopy temperature-based indices, notably the Crop Water Stress Index (CWSI), improve water use efficiency and enhance yield responses. However, sensor calibration, environmental variability, and the balance between accuracy and cost continue to be ongoing challenges.

Temperature is a key physiological indicator of plant health, influenced by factors including water status, disease and developmental stage. Monitoring changes in multiple factors is helpful for early diagnosis of plant growth. However, there are a variety of complex light interference phenomena in the greenhouse, so traditional detection methods cannot meet effective online monitoring of strawberry health status without manual intervention. Therefore, this paper proposes a leaf soft-sensing method based on a thermal infrared imaging sensor and adaptive image screening Internet of Things system, with additional sensors to realize indirect and rapid monitoring of the health status of a large range of strawberries. Firstly, a fuzzy comprehensive evaluation model is established by analyzing the environmental interference terms from the other sensors. Secondly, through the relationship between plant physiological metabolism and canopy temperature, a growth model is established to predict the growth period of strawberries based on canopy temperature. Finally, by deploying environmental sensors and solar height sensors, the image acquisition node is activated when the environmental interference is less than the specified value and the acquisition is completed. The results showed that the accuracy of this multiple sensors system was 86.9%, which is 30% higher than the traditional model and 4.28% higher than the latest advanced model. It makes it possible to quickly and accurately assess the health status of plants by a single factor without in-person manual intervention, and provides an important indication of the early, undetectable state of strawberry disease, based on remote operation.

The brown planthoppers (BPHs) are serious pests of rice in Southeast Asia which often cause a heavy loss of rice. Monitoring BPH populations and prediction of their damages to rice are more important for the precious control of this pest. Nowadays, highly efficient monitoring and predicting methods for BPHs are still rare. Here, the canopy temperatures of rice damaged by different number of BPHs were examined using the thermal imaging technique, and relationships between canopy temperature and population size of BPHs or rice yield were analyzed. The result showed that there was a significant and stable correlation between rice canopy temperatures and BPH population sizes on these rice plants in four consecutive years of plot experiments. Further, canopy temperatures of rice measured at 8:00–10:00 am were negatively related to the population size of BPHs, but not the temperature measured at noon and in the afternoon. Canopy temperatures of rice at booting and heading growth periods were also strongly related to the rice yield and its loss rate damaged by BPHs. Based on the difference between air temperature and mean canopy temperature of rice, the BPH population size could be monitored using an exponential function model, and the rice yield could be predicted by a linear model. A promising framework of automatic monitoring BPH populations was developed based on canopy temperatures of rice.

Drought stress poses a major challenge to global maize (Zea mays L.) production, significantly affecting food security. This study aimed to identify drought-tolerant maize genotypes by evaluating their morphological characteristics under controlled and field conditions. Morphological traits, such as shoot and root length in the seedling stage and plant height, canopy temperature, ear length, and kernel weight at vegetative and reproductive stages, were analyzed under drought stress conditions induced by PEG and reduced field capacity. Significant genetic variability was observed among the genotypes for most traits, with phenotypic coefficient of variation (PCV) consistently higher than genotypic coefficient of variation (GCV). Heritability estimates ranged from 37% to 99.82%, with traits such as plant height, kernel weight per ear, ear weight, hundred-seed weight, and yield per pot demonstrating both high heritability and genetic advance. Yield showed positive correlations with plant height, ear length, ear diameter, kernel number per row, and kernel weight per ear, while negatively correlating with days to ear moisture loss. Principal Component Analysis (PCA) revealed that the first five components explained 83.46% of the total variation, with yield-related traits contributing most to the variation. Cluster analysis revealed eight distinct groups at the seedling stage and six at the vegetative and reproductive stages, with genotypes in Clusters 5 and 6 exhibiting superior drought tolerance, particularly in traits like root number, kernel number per ear, and kernel weight per ear. This study identified BHM-7, BHM-14, and BHM-15 as genotypes with superior drought tolerance at the reproductive stage, while Black, Violet, and White Vutta showed resilience at the seedling stage, exhibiting greater plant vigor under drought stress. Recognizing these genotypes as a significant step toward breeding drought-resistant maize varieties, contributing to food security and sustainable agriculture.

The present experiment entitled “Effect of irrigation methods and nutrient managements on growth of late sown wheat (Triticum aestivum)” was conducted during rabi season in year 2023-24 at Instructional cum research farm, Barrister Thakur Chhedilal College of Agriculture and Research Station, Bilaspur, (C.G.). The experiment was laid out in Split Plot Design (SPD) with Irrigation methods (03): Main plot, Nutrient management (05): Sub plot treatments and three replications. The treatment consists of I1 (Surface Irrigation (Check Basin) Dept of irrigation 6 cm (IW/CPE =1), I2 (Sprinkler Irrigation (Dept of irrigation 3 cm)), I3 (Surface + Sprinkler Irrigation (Dept of irrigation 3 cm)) at Main plot: Irrigation methods and N1 (100% RDN (90:60:40 kg NPK/ha)), N2 (100% RDN through (FYM enriched by Rock Phosphate + Microbial + Consortia + Jivamrit+ Decomposer)), N3 (50% RDN through enriched FYM with microbial Consortia + 50% % RDN through FYM enriched by Rock Phosphate + Consortia + Jivamrit + decomposer), N4 (50% RDN through Urea + 25% RDN through enriched FYM with microbial Consortia + 25% RDN through FYM enriched by Rock Phosphate + Consortia + Jivamrit + decomposer), N5 (Control (No NPK)) at Sub plot: Nutrient management. The results showed at Main plot: Irrigation methods, the I3 (Surface + Sprinkler Irrigation (Dept of irrigation 3 cm)) performed the best in terms of ear length (cm), ear weight (g), number of ears plant-1, physiological maturity, number of grains ear head-1, 1000 grain weight (g), grain yield (q ha-1), biomass yield (q ha-1), harvest index, which was followed closely by the treatment I2 (sprinkler irrigation) and the lowest was observed in the treatment I1 (Surface Irrigation (Check Basin)). The results showed at Sub plot: Nutrient management, the N4 (50% RDN through Urea + 25% RDN through enriched FYM with microbial Consortia + 25% RDN through FYM enriched by Rock Phosphate + Consortia + Jivamrit + decomposer) performed the best in terms of plant population (no. m-2), plant height (cm), Number of tillers (no. m-2), plant canopy temperature at CRI, flowering, milking stage, plant canopy humidity at CRI, flowering, milking stage, number of active leaves plant-1, dry matter accumulation (g day-1 plant-1), crop growth rate (CGR) (g day-1 m-2), relative growth rate (RGR) (g g-1 day-1), water expanse (mm) and water expanse efficiency (kg cm), which was followed closely by the treatment N1 (100% RDN) and N2 (100% RDN through FYM enriched by Rock Phosphate + Microbial + Consortia + Jivamrit+ Decomposer) and the lowest was observed in the treatment N5 (Control).

Abstract. Efficient irrigation management is vital for conserving water and maximizing productivity, making the crop water stress index (CWSI) a powerful remote sensing tool. CWSI computation requires lower and upper baselines, corresponding to no-stress and severe stress conditions, respectively. This study aims to compare two different methods for determining those baselines. In the empirical method, the non-water-stressed baseline (NWSB) is derived by the linear regression of the canopy-air temperature difference against vapor pressure deficit (VPD) for a well-watered crop. The combined method compares the NWSB coefficients to theoretical expressions to estimate aerodynamic and canopy resistance at potential transpiration. This work used infrared radiometers (IRR) to measure the canopy temperature of bean plants (Phaseolus vulgaris L.) cultivar ‘IAC1850’ under center-pivot irrigation. Since the empirical method is susceptible to fluctuations in meteorological data, an expressive amount of data had to be filtered out. When comparing the two methods, the RMSE is 1.0 °C for the lower baseline and 1.8 °C for the upper baseline. Future studies could use these baselines to provide CWSI maps from thermographic images.

The current study was conducted on several genotypes during crop season of Rabi 2021-2022 and 2022-2023 with the goal of to work out heritability in general and genetic advance in percentage of mean and estimate correlation coefficient among the growth and yield traits. The field experiments included 60 germplasms of bread wheat. Experimental trials were conducted in 12 environments (E1 to E12). The field experiments were laid out in Augmented Block Design (ABD). The observations on 26 physio-morphological and yield related traits were recorded in both non-stressed and heat-stressed environment. High genotypic and phenotypic coefficients of variation were observed for plant waxiness (8.897 and 11.943) followed by leaf rolling (6.390 and 12.240). Heritability coupled with genetic advance were identified in yield per plot (g), grain yield / plant (g), plant waxiness (0-10 scale). Grain yield per plant exhibited significant positive correlation with grain length (0.712), harvest index (0.629), leaf rolling (0.4638). It exhibited significant negative correlation with plant height (-0.461) and canopy temperature depression (-0.233).

High throughput image-based phenotyping is a powerful tool to non-invasively determine the development and performance of plants under specific conditions over time. By using multiple imaging sensors, many traits of interest can be assessed, including plant biomass, photosynthetic efficiency, canopy temperature, and leaf reflectance indices. Plants are frequently exposed to multiple stresses under field conditions where severe heat waves, flooding, and drought events seriously threaten crop productivity. When stresses coincide, resulting effects on plants can be distinct due to synergistic or antagonistic interactions. To elucidate how potato plants respond to single and combined stresses that resemble naturally occurring stress scenarios, five different treatments were imposed on a selected potato cultivar (Solanum tuberosum L., cv. Lady Rosetta) at the onset of tuberization, i.e. control, drought, heat, waterlogging, and combinations of heat, drought, and waterlogging stresses. Our analysis shows that waterlogging stress had the most detrimental effect on plant performance, leading to fast and drastic physiological responses related to stomatal closure, including a reduction in the quantum yield and efficiency of photosystem II and an increase in canopy temperature and water index. Under heat and combined stress treatments, the relative growth rate was reduced in the early phase of stress. Under drought and combined stresses, plant volume and photosynthetic performance dropped with an increased temperature and stomata closure in the late phase of stress. The combination of optimized stress treatment under defined environmental conditions together with selected phenotyping protocols allowed to revealthe dynamics of morphological and physiological responses to single and combined stresses. Here, a useful tool is presented for plant researchers looking to identify plant traits indicative of resilience to several climate change-related stresses.

The effect of mycorrhizal fungi and plant growth promoting rhizobacteria on some physiological properties, yield and soluble solid content (Brix) of ‘Uno Rosso’ F1 processing tomato was studied under water scarcity. Inoculation was performed with mycorrhizal fungi (M) and rhizobacteria preparation (PH) at sowing (M1, PH1) and sowing + planting (M2, PH2). The treated and untreated plants were grown with regular irrigation (RI = ET100%), with deficit irrigation (DI = ET50%) and without irrigation (I0). In drought, the canopy temperature of plants inoculated with arbuscular mycorrhizal fungi (M1, M2) decreased significantly, however, the decrease was small in those treated with the bacterium (PH1, PH2), while the SPAD value of the leaves of plants treated only with Phylazonit increased significantly. On two occasions, inoculations (M2, PH2) significantly increased the total yield and marketable yield, however, under water deficiency, a higher rate of green yield was detected than untreated plants. In dry year using deficit irrigation, the one-time inoculation (M1, PH1) provided a more favorable Brix value, while the double treatments reduced the Brix. In moderate water scarcity, the use of mycorrhizal inoculation (M2) is preferable, while under weak water stress, the use of rhizobacteria inoculation (PH2) is more favorable.

Modelling weed seed predation by carabids and its effects on crop production under contrasted farming systems

Plant canopies exhibit stronger thermoregulation capability at the seasonal than diurnal timescales

Polyethylene glycol-induced drought stress screening of selected Philippine high-yielding sugarcane varieties

Spatial corn canopy temperature extraction: How focal length and sUAS flying altitude influence thermal infrared sensing accuracy

Climate change is expected to influence crop growth through frequent drought and heat extremes, and thus, drought and heat tolerance are of increasing importance as major breeding goals for cereal crops in Central Europe. Plant physiological water status traits are suitable for phenotyping plant drought/heat tolerance. The objective of this study was to determine whether relative leaf water content (RLWC), plant canopy temperature (CT), and carbon isotope discrimination (CID) are suitable for phenotyping the drought/heat resistance of German winter wheat for future climate resilience. Therefore, a comprehensive field evaluation was conducted under drier and warmer conditions in Moldova using a space-for-time approach for twenty winter wheat varieties from Germany and compared to twenty regionally adapted varieties from Eastern Europe. Among the physiological traits RLWC, CT, and CID, the heritability of RLWC showed the lowest values regardless of year or variety origin, and there was no significant correlation between RLWC and grain yield regardless of the year, suggesting that RLWC did not seem to be a useful trait for distinguishing origins or varieties under continental field conditions. Although the heritability of CT demonstrated high values, the results showed surprisingly low and nonsignificant correlations between CT and grain yield; this may have been due to a confounding effect of increased soil temperature in the investigated dark Chernozem soil. In contrast, the heritability of CID in leaves and grain was high, and there were significant correlations between grain yield and CID, suggesting that CID is a reliable indirect physiological trait for phenotyping drought/heat resistance for future climate resilience in German wheat.

What lies beneath: Vertical temperature heterogeneity in a Mediterranean woodland savanna

In the present study, individual and combined effects of drought and heat stress were investigated on key physiological parameters (canopy temperature, membrane stability index, chlorophyll content, relative water content, and chlorophyll fluorescence) in two popular sorghum cultivars (Sorghum bicolor cvs. Phule Revati and Phule Vasudha) during the seedling stage. Estimating canopy temperature through pixel-wise analysis of thermal images of plants differentiated the stress responses of sorghum cultivars more effectively than the conventional way of recording canopy temperature. Cultivar difference in maintaining the canopy temperature was also responsible for much of the variation found in critical plant physiological parameters such as cell membrane stability, chlorophyll content, and chlorophyll fluorescence in plants exposed to stress. Hence, the combined stress of drought and heat was more adverse than their individual impacts. The continued loss of water coupled with high-temperature exposure exacerbated the adverse effect of stresses with a remarkable increase in canopy temperature. However, Phule Vasudha, being a drought-tolerant variety, was relatively less affected by the imposed stress conditions than Phule Revati. Besides, the methodology of measuring and reporting plant canopy temperature, which emerged from this study, can effectively differentiate the sorghum genotypes under the combined stress of drought and heat. It can help select promising genotypes among the breeding lines and integrating the concept in the protocol for precision water management in crops like sorghum.

Remote sensing devices as key methods in the advanced turfgrass phenotyping under different water regimes

Energy management in protected cropping is critical due to the high cost of energy use in high-tech greenhouse facilities. The main purpose of this research was to investigate the optimal strategy to reduce cooling energy consumption, by regulating the settings (opening/closing) of either vents or curtains during the day, at the protected cropping facility at Western Sydney University. We measured daily changes in air temperature and energy consumption under four treatments (open/closed combinations of vents and shade screens) and developed an optimal cooling strategy for energy management using multi-temperature acquisition points at different heights within a greenhouse compartment. The optimal treatment (vents open/curtains closed) reduced energy load at the rooftop, thereby maintaining a desirable plant canopy temperature profile, and reducing cooling energy. Daily energy consumption was lowest for vents open/curtains closed (70.5 kWh) and highest for vents closed/curtains open (121 kWh). It was also found that delaying the operation of opening and closing of vents and curtains until the plant canopy temperature reached 25 °C reduced cooling energy consumption and decreased heating energy consumption in the morning (e.g., 08:00 to 10:00). The estimated savings of 1.83 kWh per 1 °C cooling between the optimal (vents open/curtains closed) and least optimal (vents closed/curtains open) conditions had the potential for significant energy savings at 494 kWh per °C over a crop cycle of nine months in warm weather conditions. However, selection of the optimal cooling strategy utilising control of vents and curtains must also account for the impact from other greenhouse environmental factors, including light, humidity, and CO2 concentration, which may be crop specific.