Water scarcity profoundly affects crop growth in rain-fed regions, including the Pacific Northwest (PNW) of the USA. While unmanned aerial vehicles (UAVs) are integral for crop monitoring in breeding programs, their use is resource-intensive and necessitates pilot presence in the field. Alternatively, Internet of Things (IoT)-based sensor systems offer continuous, remote, and real-time monitoring, but their data integrity requires validation for field applications. This study developed a Raspberry Pi-based sensor system (AGIcam+) and compared its efficacy with UAV in discerning crop responses to drought conditions across various wheat varieties in the PNW region. Multispectral and thermal data were collected across wheat trials (Winter 2023; Spring 2022, 2023) at crucial growth stages – preheading, heading, and post-heading – under varied drought stress conditions. Key vegetation indices and temperature measurements were extracted for a comparative drought performance analysis. Results indicate significant correlations between AGIcam+ and UAV data, more pronounced during the heading and post-heading stages. Pearson’s correlation coefficients for the average normalized difference vegetation index (NDVI) and the temperature data exhibited ranges of 0.81-0.88 and 0.77-0.96 (P < 0.01), respectively, across all trials during the heading stages. Yield prediction models using random forest regression analysis from both systems’ data underscored AGIcam+’s accuracy in yield estimation, demonstrating performance comparable to UAV, as evidenced in the Spring 2023 trial ( AGIcam+: R2 = 0.85, RMSE = 796.9 kg/ha; UAV: R2 = 0.84, RMSE = 825.0 kg/ha). These findings underscore AGIcam+ as a resource-efficient crop monitoring alternative, effectively capturing responses to environmental conditions and facilitating accurate yield predictions under drought stress.

Multispectral imaging with unmanned aircraft systems (UAS) is a promising high-throughput phenotyping technology that has been shown to help understand the causal mechanisms associated with crop productivity. This imaging technology can accurately predict complex agronomic traits like grain yield within a given generation, creating the potential to fast-track selections in plant breeding and increase genetic gains. The objective of this study was to determine the effectiveness and efficiency of prediction on grain yield in an abnormal drought year across locations within a breeding program. Eleven spectral reflectance indices (SRI) including NDRE, NWI, NDVI, and percent canopy cover were used to evaluate Washington State University winter wheat breeding lines between 2018 and 2021. Data was collected using a DJI Inspire 2 drone, equipped with a Sentera Quad Multispectral Sensor, and collected at the heading date. Lines were observed from single location, single replication preliminary yield trials to multi-location, replicated advanced yield trials. Lines advanced in the breeding program were evaluated across 13 different location-year trials. The calculated SRIs and canopy cover were used individually and in combination as fixed effects in mixed model prediction for grain yield under drought conditions. Models were independently validated with 2021 data. Across locations, SRIs are shown to improve the prediction performance for grain yield under abnormal drought conditions by as much as 40% in the case of NDRE. This research is vital for plant breeders to understand the utility of UAS imaging in variety improvement when dealing with abnormal growing seasons.