Wheat Ear Detection Algorithm Based on Improved YOLOv4

Abstract

The continuously growing population requires improving the efficiency of agricultural production. Wheat is one of the most wildly cultivated crops. Intelligent wheat ear monitoring is essential for crop management and crop yield prediction. Although a variety of methods are utilized to detect or count wheat ears, there are still some challenges both from the data acquisition process and the wheat itself. In this study, a computer vision methodology based on YOLOv4 to detect wheat ears is proposed. A large receptive field allows viewing objects globally and increases the connections between the image points and the final activation. Specifically, in order to enhance the receptive field, additional Spatial Pyramid Pooling (SPP) blocks are added to YOLOv4 at the feature fusion section to extract multi-scale features. Pictures of wheat ears taken at different growth stages from two different datasets are used to train the model. The performance of the proposed methodology was evaluated using various metrics. The Average Precision (AP) was 95.16% and 97.96% for the two datasets, respectively. By fitting the detected wheat ear numbers and true wheat ear numbers, the R2 value was 0.973. The results show that the proposed method outperforms YOLOv4 in wheat ear detection. It indicates that the proposed method provides a technical reference for agricultural intelligence.

Keywords

• object detection
• wheat ear
• convolutional neural network
• intelligent agriculture