Abbreviations: UAS, unoccupied aerial system; RGB, red-green-blue; EGI, Excess Greenness Index; VARI, visible atmospheric resistant index; GIS, geographic information system.ABSTRACTThe analysis of unoccupied aerial system (UAS) imagery remains a bottleneck to obtaining actionable data due to its complexity and required specialized skill set. We introduce a workflow designed to balance usability and flexibility in processing UAS imagery using QGIS, an open-source geographic information system (GIS) software. The workflow consists of four sequential steps: semi-automated plant classification, user-corrected plot delineation, spectral index calculation, and data extraction. We tested this workflow on barley UAS image data, collected over 13 flights throughout the growing season. As a proof-of-concept, multiple RGB indices were calculated to test for relationships to ground-sampled data and exemplify applications of spectral analysis. Our results indicate a strong correlation between the Visible Atmospheric Resistant Index (VARI) and ground truth data, particularly when integrated over multiple flights. This open-source workflow provides a lower barrier-to-entry solution for researchers and producers, facilitating the broader adoption of UAS technology in agriculture. By automating routine tasks while allowing user intervention for critical adjustments, this approach enhances the efficiency of agricultural data analysis.

A comprehensive survey and subject-expert interviews conducted among agricultural researchers investigated perceived value and barriers to the adoption of unoccupied aerial systems (UAS) in agricultural research.  The study involved 154 respondents from 21 countries representing various agricultural sectors. The survey identified three key applications considered most promising for UAS in agriculture: precision agriculture, crop phenotyping/plant breeding, and crop modeling. Over 80% of respondents rated UAS for phenotyping as valuable, with 47.6% considering them very valuable. Among the participants, 41% were already using UAS technology in their research, while 49% expressed interest in future adoption. Current users highly valued UAS for phenotyping, with 63.9% considering them very valuable, compared to 39.4% of potential future users. The study also explored barriers to UAS adoption. The most commonly reported barriers were the "High cost of instruments/devices or software" (46.0%) and the "Lack of knowledge or trained personnel to analyze data" (40.9%). These barriers persisted as top concerns for both current and potential future users. Respondents expressed a desire for detailed step-by-step protocols for drone data processing pipelines (34.7%) and in-person training for personnel (16.5%) as valuable resources for UAS adoption. The research sheds light on the prevailing perceptions and challenges associated with UAS usage in agricultural research, emphasizing the potential of UAS in specific applications and identifying crucial barriers to address for wider adoption in the agricultural sector.

Producers desire varieties that consistently perform with high yields and end-use qualities. Unlike easily recognized average yield improvements, yield stability over time is less examined, especially when considering the role of breeding relative to other factors like management and changing climatic conditions. Our study system was a 70-year historical dataset from which we estimated the year-over-year stability of Triticum aestivum, winter wheat varieties released by Montana's Agricultural Experimental Station. We examined yield stability within six locations representing diverse growing conditions across Montana and found evidence that breeding has improved stability at specific locations and not at others. Newer varieties showed improved year-over-year stability at locations that tended to have the lowest yields and more extreme weather conditions, reflecting that year-over-year stability has a genotype-by-environment component. We examined the role of climatic conditions, including temperature and rainfall to understand if reduced climatic variability was driving patterns of improved stability at these sites. However, the impact of breeding remained, or became evident when accounting for climatic variables. Together, these findings suggest that breeding's strong selective pressures improve second order traits.