Panicle Segmentation on UAV Captured Multispectral Paddy Crop Imagery

Automation of crop yield estimation is crucial to cultivate efficient breeding techniques to fulfill the increasing population demands and adapt to climate change. The panicle count of paddy is directly associated with the yield of crop. In this work, we implement computer vision-based image segmentation methods for segmenting panicles by utilizing Unmanned Aerial Vehicle (UAV) captured multispectral imagery. The developed algorithms in this study could be used as a reference for panicle counting models during the vegetative and heading stages of the paddy crop. In addition, we introduce a semi-automatic image annotation method for effortless creation of labeled paddy crop datasets. With this method, our developed tool collaborates with an annotation tool to offer preliminary image annotations, which users can subsequently refine the annotations.