Cotton Stand Count Using UAS Imagery and Deep Learning

Stand count is critical for growers to make decisions for replanting and other site-specific management to avoid yield loss. This study applied and compared two object detection models, MobileNets and CenterNet, in cotton stand count using unmanned aerial system (UAS) images. The results showed that the overall mean precision and recall for the CenterNet model were higher than those for the MobileNets model. The CenterNet trained model had an R2 value of 0.60 and the MobileNets trained model had an R2 value of 0.48. The accuracy for the CenterNet model was 61% and for the MobileNets model was 46%. The results indicate that the CenterNet model has a better overall performance on cotton plant detection and counting.Cotton, Stand count, UAS, MobileNets, CenterNet, Deep Learnin