Remote sensing is revolutionizing the phenotyping of agricultural field trials, but for many researchers, the extraction of plot‐level results is a bottleneck. We have developed the R package FIELDimageR as a user‐friendly tool to analyze orthomosaic images containing many plots. The basic workflow involves cropping and rotating the image, followed by the creation of a shapefile based on the experimental design. The package includes functions to calculate the number of plants per plot, canopy cover percentage, vegetation indices, and plant height. FIELDimageR is publicly available as a GitHub repository (https://github.com/filipematias23/FIELDimageR).

A comprehensive survey and subject‐expert interviews conducted among agricultural researchers investigated perceived value and barriers to the adoption of unoccupied aerial systems (UASs) in agricultural research. These systems are often referred to colloquially as drones and are composed of unoccupied/uncrewed/unmanned vehicles and incorporated sensors. This study of UASs involved 154 respondents from 21 countries representing various agricultural sectors. The survey identified three key applications considered most promising for UASs in agriculture: precision agriculture, crop phenotyping/plant breeding, and crop modeling. Over 80% of respondents rated UASs 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 UASs 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 UASs in specific applications and identifying crucial barriers to address for wider adoption in the agricultural sector.

Bison‐Fly is the open‐source pipeline of unoccupied aerial vehicle (UAV) applications for plant breeding developed by the Spring Wheat Breeding Program at North Dakota State University in partnership with the Drone2Phenome community. We present a step‐by‐step pipeline to collect, process, and apply UAV data for plant breeding to facilitate data processing, reduce errors, and create new traits. This open‐source R code can be easily adapted for different crops and improved according to user requirements. In addition, Bison‐Fly provides a free and complete dataset with (a) RGB, Multispectral, and Digital Surface Model orthomosaics from 15 time points throughout the season; (b) field trait data with experimental design; and (c) daily metadata with soil and weather information. We hope this pipeline helps guide students, researchers, and breeders on day‐to‐day activities. Bison‐Fly is available at https://github.com/filipematias23/Bison‐Fly.