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.

No abstract

Core Ideas We comprehensively validated the use of UAS in sorghum and maize breeding programs. Temporal estimates of plant growth will allow researchers to elucidate new phenotypes. The stage of the breeding pipeline dictates the applicability of UAS platforms. The implementation of UAS is demonstrated in different crop species. Monetary and time costs should be considered before implementation of UAS. To meet future world food and fiber demands, plant breeders must increase the rate of genetic improvement of important agricultural crops. One of the biggest obstacles now facing crop scientists is a phenotyping bottleneck. To ease this burden, the emerging technology of unmanned aerial systems (UAS) presents an exciting opportunity. To assess the utility of UAS, it is important to investigate their application across multiple crop species. Terminal plant height is of great importance to maize (Zea mays L.) and sorghum [Sorghum bicolor (L.) Moench] breeders and has been hypothesized to be useful but has been logistically impractical to measure in the field. In this study, we statistically analyzed in depth the ability of UAS to estimate height in sorghum (advanced and early generation material) and maize (optimal and late material) and the application of these estimates in breeding programs. We found that UAS explain genotypic variation similarly to ground‐truth methods and that the repeatability of the methodology is high (R = 0.61–0.99), indicating effective differentiation of genotypes. Additionally, correlations between ground truth and UAS measurements were moderate to high for all materials (r = 0.4–0.9). Finally, we present a novel application for the technology in the form of high‐resolution temporal growth curves. Using these UAS‐generated growth curves, new physiological insights can be obtained and new avenues of scientific investigation are possible.

Continuing population growth will result in increasing global demand for food and fiber for the foreseeable future. During the growing season, variability in the height of crops provides important information on plant health, growth, and response to environmental effects. This paper indicates the feasibility of using structure from motion (SfM) on images collected from 120 m above ground level (AGL) with a fixed-wing unmanned aerial vehicle (UAV) to estimate sorghum plant height with reasonable accuracy on a relatively large farm field. Correlations between UAV-based estimates and ground truth were strong on all dates (R2 > 0.80) but are clearly better on some dates than others. Furthermore, a new method for improving UAV-based plant height estimates with multi-level ground control points (GCPs) was found to lower the root mean square error (RMSE) by about 20%. These results indicate that GCP-based height calibration has a potential for future application where accuracy is particularly important. Lastly, the image blur appeared to have a significant impact on the accuracy of plant height estimation. A strong correlation (R2 = 0.85) was observed between image quality and plant height RMSE and the influence of wind was a challenge in obtaining high-quality plant height data. A strong relationship (R2 = 0.99) existed between wind speed and image blurriness.

No abstract