Research on detection and counting method of wheat ears in the field based on YOLOv11-EDS

As a major food crop, accurate detection and counting of wheat ears in the field are of great significance for yield estimation. Aiming at the problems of low detection accuracy and large computational load of existing detection and counting methods in complex farmland environments, this study proposes a lightweight wheat ear detection model, YOLOv11-EDS.

First, the Dysample dynamic upsampling operator is introduced to optimize the upsampling process of feature maps and enhance feature information transmission. Second, the Direction-aware Oriented Efficient Channel Attention mechanism is introduced to make the model focus more on key features and improve the ability to capture wheat ear features. Finally, the Slim-Neck module is introduced to optimize the feature fusion structure and enhance the model's processing capability for features of different scales.

Experimental results show that the performance of the improved YOLOv11-EDS model is significantly improved on the global wheat ear dataset. The precision is increased by 2.0 percentage points, the recall by 3.5 percentage points, mAP@0.5 by 1.5 percentage points, and mAP@0.5:0.95 by 2.5percentage points compared with the baseline model YOLOv11. Meanwhile, the model parameters are reduced to 2.5 M, and the floating-point operations are reduced to 5.8 G, which are 0. 1 M and 0.5 G lower than the baseline model, respectively, achieving dual optimization of accuracy and efficiency. The model still demonstrates excellent detection performance on a self-built iPhone-view wheat ear datasets, fully verifying its robustness and environmental adaptability.

This study provides an efficient solution for the automated analysis of wheat phenotypic parameters in complex farmland environments, which is of great value for promoting the development of smart agriculture.