Testing the Agreement Between a Traditional and UAV-Based Method for Quantifying Skips in Suboptimal Cotton Stands

Highlights Agreement in the mean difference between the traditional and the UAV-based method only occurred in poor stands. Effects of different sampling sizes between methods were evident in mediocre-to-good stand assessments. Abstract. When suboptimal cotton stands occur, growers face the decision to accept or reject the stand. The replanting decision is difficult because the tradeoffs associated with replanting expenditures and reduced yields are difficult to objectively assess. Traditional methods like visual assessments and manual counts of cotton stands are commonly used to support a replanting decision. Typically, manual counts of skip size and frequency will provide more accurate assessments of the stand than visual assessments, but they are cumbersome to conduct and may not provide clear evidence that a replant is needed. Still, manual counts are popular among cotton farmers and the scientific community. Skip counts generated with the help of unmanned aerial vehicles (UAV) are less popular among cotton growers but provide more coverage and a larger sampling size across a given field. Therefore, UAVs have the potential to overcome the limitations associated with traditional methods. The motivation behind this study is to inform readers if manual methods can still be used for accurate decision-making regarding the replanting decision. More specifically, we study the interchangeability, or agreement, between a manual and a UAV-based method using Bland-Altman plots. Each method quantified skips greater than or equal to 0.91 m at different sampling sizes. Treatment plots varied in their stand counts, skip size, and skip frequency. Agreement between both methods was only found in the lowest stand treatment, where skips of large sizes were predominant. Conversely, methods disagreed in the higher stand where skips greater than or equal to 0.91 m were scarce. Keywords: Agriculture, Altman, Bland, Drone, Gaps, Precision, Remote, Sensing, UAS.