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Precision irrigation management is based on the accuracy and feasibility of sensor data assessing the plant water status. Multispectral and thermal infrared images acquired from an unmanned aerial vehicle (UAV) were analyzed to evaluate the applicability of the data in the assessment of variants of subsurface irrigation configurations. The study was carried out in a Cabernet Sauvignon orchard located near Benton City, Washington. Plants were subsurface irrigated at a 30, 60, and 90 cm depth, with 15%, 30%, and 60% irrigation of the standard irrigation level as determined by the grower in commercial production management. Half of the plots were irrigated using pulse irrigation and the other half using continuous irrigation techniques. The treatments were compared to the control plots that received standard surface irrigation at a continuous rate. The results showed differences in fruit yield when the control was compared to deficit irrigated treatments (15%, 30%, 60% of standard irrigation), while no differences were found for comparisons of the techniques (pulse, continuous) or depths of irrigation (30, 60, 90 cm). Leaf stomatal conductance of control and 60% irrigation treatments were statistically different compared to treatments receiving 30% and 15% irrigation. The normalized difference vegetation index (NDVI), green normalized difference vegetation index (GNDVI), and canopy temperature were correlated to fruit yield and leaf stomatal conductance. Significant correlations (p < 0.01) were observed between NDVI, GNDVI, and canopy temperature with fruit yield (Pearson's correlation coefficient, r = 0.68, 0.73, and −0.83, respectively), and with leaf stomatal conductance (r = 0.56, 0.65, and −0.63, respectively) at 44 days before harvest. This study demonstrates the potential of using low-altitude multispectral and thermal imagery data in the assessment of irrigation techniques and relative degree of plant water stress. In addition, results provide a feasibility analysis of our hypothesis that thermal infrared images can be used as a rapid tool to estimate leaf stomatal conductance, indicative of the spatial variation in the vineyard. This is critically important, as such data will provide a near real-time crop stress assessment for better irrigation management/scheduling in wine grape production.

Anti-hail (protective) netting was originally developed to protect horticultural crops from hail damage. Netting reduces the amount and modifies the light reaching the orchard canopy. It also has the potential to optimize conditions for canopy and fruit growth and mitigate abiotic stress as climate change leads to increased temperatures. This study measured the effect of different colors of netting on the above and below-ground environment and apple sunburn incidence in a 3-year-old 'Honeycrisp' apple orchard growing in an irrigated desert climate in comparison to a traditional uncovered control. Netting did not affect air temperature or relative humidity within the orchard canopy, but reduced wind speed by 40% compared to the uncovered control. Netting reduced soil temperature and improved soil moisture at 20 and 40 cm depths throughout the study period compared to the uncovered control. Amongst different colors of netting tested in this study, pearl and blue netting significantly reduced soil temperature compared to red netting. Netting also reduced photosynthetically active radiation (PAR) by approximately 20% and strongly reduced fruit surface temperature during hot periods. During full sunlight, differences in maximum fruit surface temperature between the uncovered control and the protective netting were 2.6 to 4.3°C under full sun conditions and reduced the incidence and severity of sunburn measured at harvest. As temperatures warm in the future, netting provides a viable option to mitigate some of the negative effects of excessive temperature and light on apple production in hot, dry growing regions.

Bitter pit is a physiological disorder that is defined as brown, corky and roundish lesions, which can develop in apples before and after harvest. This disorder greatly reduces the product utilization value of the fruit, and can result in several million dollar economic loss to the apple industry. Computed Tomography (CT) imaging is a non-destructive and rapid sensing technique that can be applied to packaged apples. In this study, healthy and bitter pit Honeycrisp apples were harvested from two field sites and stored for 63 days. CT images of the sampled apples were collected on 0, 7, 14, 21, 35 and 63 days after harvest. Images were analyzed to estimate the total pit area in each of the individual apples and were related to pit incidence and progression in different stages of storage. Results showed pit development during the storage period in bitter pitted apples. The rate of progression differed in samples collected from different field sites. Further analysis for pit distribution along each of the bitter pit affected apples showed 54% of pits located at the calyx-end of apples in comparison with middle and stem-end. Classification of healthy and bitter pitted apples using logistic regression based method resulted in false negative of 7-21%.

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Core Ideas Water stress in multiple quinoa varieties were introduced. Remote sensing technologies were evaluated to assess water stress response. Handheld and unmanned aerial system based sensors provided reliable data. Thermal imaging data could capture stress in quinoa varieties. Quinoa (Chenopodium quinoa Willd.) is a crop known for its tolerance to abiotic stress such as drought and salinity. Quinoa is also a versatile superfood, which is gluten‐free and high in protein, vitamins, nutrients, and beneficial antioxidants. Washington State University's quinoa breeding program efforts focus on identification and development of varieties that are resilient to local environmental conditions. In this study, high‐throughput phenotyping techniques were applied to evaluate the performance of quinoa varieties under different irrigation regimes. Handheld multispectral radiometer (Crop Scan), proximal sensing system using ground platform, and remote sensing with unmanned aerial system (UAS) were used to assess the performance of eight quinoa varieties under two irrigation treatments (non‐irrigated and irrigated). Crop Scan data, along with multispectral and thermal infrared images, were acquired at multiple time points at different stages of crop development during the season. In general, the normalized difference vegetation index, water band index, and green normalized difference vegetation index (GNDVI) data extracted from Crop Scan, and GNDVI and canopy temperature data extracted from UAS were able to detect irrigation treatment effects. Comparing the spectral data acquired at multiple scales indicated that Crop Scan data were highly and significantly correlated with remote sensing data (|r| = 0.57–0.85). Rapid data acquisition and the ability to detect differences among varieties under water stress highlight the application of remote sensing techniques as a high‐throughput phenotyping tool to evaluate quinoa.

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