Advances in automation and data science have led agriculturists to seek real-time, high-quality, high-volume crop data to accelerate crop improvement through breeding and to optimize agronomic practices. Breeders have recently gained massive data-collection capability in genome sequencing of plants. Faster phenotypic trait data collection and analysis relative to genetic data leads to faster and better selections in crop improvement. Furthermore, faster and higher-resolution crop data collection leads to greater capability for scientists and growers to improve precision-agriculture practices on increasingly larger farms; e.g., site-specific application of water and nutrients. Unmanned aerial vehicles (UAVs) have recently gained traction as agricultural data collection systems. Using UAVs for agricultural remote sensing is an innovative technology that differs from traditional remote sensing in more ways than strictly higher-resolution images; it provides many new and unique possibilities, as well as new and unique challenges. Herein we report on processes and lessons learned from year 1—the summer 2015 and winter 2016 growing seasons–of a large multidisciplinary project evaluating UAV images across a range of breeding and agronomic research trials on a large research farm. Included are team and project planning, UAV and sensor selection and integration, and data collection and analysis workflow. The study involved many crops and both breeding plots and agronomic fields. The project’s goal was to develop methods for UAVs to collect high-quality, high-volume crop data with fast turnaround time to field scientists. The project included five teams: Administration, Flight Operations, Sensors, Data Management, and Field Research. Four case studies involving multiple crops in breeding and agronomic applications add practical descriptive detail. Lessons learned include critical information on sensors, air vehicles, and configuration parameters for both. As the first and most comprehensive project of its kind to date, these lessons are particularly salient to researchers embarking on agricultural research with UAVs.

Accurate and reliable calibration methods are required when applying unmanned aerial vehicle (UAV)-based thermal remote sensing in precision agriculture for crop stress monitoring, irrigation planning, and harvesting. The primary objective of this study was to improve the calibration accuracies of UAV-based thermal images using temperature-controlled ground references. Two temperature-controlled ground references were installed in the field to serve as high- and low-temperature references, approximately spanning the expected range of crop surface temperatures during the growing season. Our results showed that the proposed method using temperature-controlled references was able to reduce errors due to ambient conditions from 9.29 to 1.68 °C, when tested with validation panels. There was a significant improvement in crop temperature estimation from the thermal image mosaic, as the error reduced from 14.0 °C in the un-calibrated image to 1.01 °C in the calibrated image. Furthermore, a multiple linear regression model (R² = 0.78; p-value < 0.001; relative RMSE = 2.42%) was established to quantify soil moisture content based on canopy surface temperature and soil type, using UAV-based thermal image data and soil electrical conductivity (ECa) data as the predictor variables.

Tissue nitrate (NO ₃ ) concentration (TNC) in leafy greens generally decreases with increasing light intensity and photoperiod in controlled environment studies. Harvesting late in the day has been recommended as a way to produce leafy greens with lower TNC, although data from field research do not support this recommendation. This study investigated the effect of time of day of harvest on TNC in lettuce ( Lactuca sativa L.) and spinach ( Spinacia oleracea L.) grown in the field during the summer at Pullman, WA (lat. 46° N) and Fairbanks, AK (lat. 64° N). Whole plants were sampled every 2 h on three separate, 24-h harvest dates at each latitude. Plants were dried, ground, and analyzed for NO ₃ -N. At the high-latitude location, TNC decreased linearly during the day (1000 to 2300 hr ) on all three dates for spinach and one for lettuce. At the low-latitude location, TNC decreased linearly during the day (1000 to 1900 hr ) on one date and increased linearly during the night (2000 to 0400 hr ) on two dates for lettuce. The TNC (average 287 to 607 mg NO ₃ -N/kg fresh weight for lettuce and 141 to 189 mg NO ₃ -N/kg fresh weight for spinach) and magnitude of diurnal fluctuation (generally less than 25%) should not pose a human health risk regardless of when plants are harvested.