Cotton row spacing and unmanned aerial vehicle sensors. (9th December 2021)

• Record Type: Journal Article
• Authors: Wu, Wenzhuo; Hague, Steve Scott; Jung, Jinha; Ashapure, Akash; Maeda, Murilo; Maeda, Andrea; Chang, Anjin; Jones, Don; Thomasson, Alex; Landivar, Juan
• Is Part Of: Agronomy Journal. Volume 114:Number 1(2022)
• Page Start: 331
• Page End: 339
• Publication Date: 2021-12-09
• Subjects: Agronomy -- Periodicals; 630
• DOI: 10.1002/agj2.20902
• Languages: English
• ISSNs: 0002-1962

Abstract

Abstract: The 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. Core Ideas: Evaluation of the accuracy of UAV sensors to predict lint yield validates high‐throughput phenotyping technology. Our findings demonstrated the improvement of cotton yield predictions with UAV sensors in skip‐row patterns. The genotype × row spacing interaction was minimal, so UAVs can be used to predict cotton yield.