FIDMT-GhostNet: a lightweight density estimation model for wheat ear counting

Wheat is one of the important food crops in the world, and the stability and growth of wheat production have a decisive impact on global food security and economic prosperity. Wheat counting is of great significance for agricultural management, yield prediction and resource allocation. Research shows that the wheat ear counting method based on deep learning has achieved remarkable results and the model accuracy is high. However, the complex background of wheat fields, dense wheat ears, small wheat ear targets, and different sizes of wheat ears make the accurate positioning and counting of wheat ears still face great challenges. To this end, an automatic positioning and counting method of wheat ears based on FIDMT-GhostNet (focal inverse distance transform maps - GhostNet) is proposed. Firstly, a lightweight wheat ear counting network using GhostNet as the feature extraction network is proposed, aiming to obtain multi-scale wheat ear features. Secondly, in view of the difficulty in counting caused by dense wheat ears, the point annotation-based network FIDMT (focal inverse distance transform maps) is introduced as a baseline network to improve counting accuracy. Furthermore, to address the problem of less feature information caused by the small ear of wheat target, a dense upsampling convolution module is introduced to improve the resolution of the image and extract more detailed information. Finally, to overcome background noise or wheat ear interference, a local maximum value detection strategy is designed to realize automatic processing of wheat ear counting. To verify the effectiveness of the FIDMT-GhostNet model, the constructed wheat image data sets including WEC, WEDD and GWHD were used for training and testing. Experimental results show that the accuracy of the wheat ear counting model reaches 0.9145, and the model parameters reach 8.42M, indicating that the model FIDMT-GhostNet proposed in this study has good performance.