Research on object detection for small objects in agriculture: taking red bayberry as an example

Abstract

With the continuous improvement of intelligent management level in red bayberry orchards, the demand for automatic picking and automatic sorting is becoming increasingly apparent. The prerequisite for achieving these automated processes is to quickly identify the maturity of red bayberries by object detection. In this study, we classified red bayberry into 8 levels of maturity and achieved an object detection precision of 88.9%. We used a fast object detection model, combined with small object optimization methods and small feature extraction layers to get higher precision.