Cotton row spacing and unmanned aerial vehicle sensors

Abstract

AbstractThe use of unmanned aerial vehicles (UAVs) to identify the number and area of cotton (Gossypium hirsutum L.) bolls in a field plot can serve as an important high‐throughput phenotyping strategy for predicting seedcotton yield. The objectives of this study were to determine if the prediction of seedcotton yield using a UAV could be improved in skip‐row spacing versus solid‐row spacing and if a genotype × row‐spacing interaction occurs for important yield and fiber traits. A split‐plot design was used with the main plot being row spacing and the sub‐plot consisting of five cotton genotypes. Trials were conducted at three locations in 2017 and 2018. Seedcotton yield, lint yield, lint percent, and fiber qualities were measured for all treatments. In 2018, UAVs with red, green, and blue (RGB) cameras were flown across the fields at two locations to estimate open‐boll count and boll area at the end of the growing season. In general, lint yield and fiber quality were not affected by genotype × row spacing interactions. Seedcotton yield estimations from UAV‐based RGB sensors were improved when cotton was planted on a skip‐row spacing versus a solid row configuration. However, several issues beyond the improvement of seedcotton yield predictions with UAVs need to be considered before research programs use skip‐row spacing.