Small unopened cotton boll counting by detection with MRF-YOLO in the wild

Abstract

Accurate detection and counting of unopened cotton bolls at the early stage of cotton maturation is an effective way to develop crop load management and harvesting strategies in advance. However, robust and effective detection and counting of unopened bolls under complicated field conditions is a challenging task. In this study, we propose a deep learning method with multi-receptive field extraction based on YOLOX, called MRF-YOLO, to detect and count small targets, which we validate on a cotton boll dataset collected from a cotton farm. In the target detection part, a multi-scale residual block and an attention module are first introduced to enhance the extraction of cotton boll feature details. Second, a multi-receptive field extraction module is added to reduce the loss on small targets in the deep network. Finally, a small target detection layer is integrated to improve detection precision. Compared with existing methods, the proposed model achieves a 14.86% improvement in the average accuracy AP50:95 and AP50 of 92.75% while maintaining a high processing speed. In the counting part, we propose a detection-based counting method using MRF-YOLO. The mean squared error and coefficient of determination (R2) are 1.06 and 0.92, respectively. MRF-YOLO can be extended to a wide range of small target crop detection, enabling reliable yield predictions under real field conditions.