Conference paper Open Access

Cotton Stand Count Using UAS Imagery and Deep Learning

Zhe Lin; Wenxuan Guo; Ahmed Harb Rabia

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 Learning
Files (667.9 kB)
Name Size
Cotton Stand Count Using UAS Imagery and Deep Learning.pdf
667.9 kB Download
All versions This version
Views 2020
Downloads 77
Data volume 4.7 MB4.7 MB
Unique views 1616
Unique downloads 77


Cite as