CSNet: A Count-Supervised Network via Multiscale MLP-Mixer for Wheat Ear Counting

Abstract

Wheat is the most widely grown crop in the world, and its yield is closely related to global food security. The number of ears is important for wheat breeding and yield estimation. Therefore, automated wheat ear counting techniques are essential for breeding high-yield varieties and increasing grain yield. However, all existing methods require position-level annotation for training, implying that a large amount of labor is required for annotation, limiting the application and development of deep learning technology in the agricultural field. To address this problem, we propose a count-supervised multiscale perceptive wheat counting network (CSNet, count-supervised network), which aims to achieve accurate counting of wheat ears using quantity information. In particular, in the absence of location information, CSNet adopts MLP-Mixer to construct a multiscale perception module with a global receptive field that implements the learning of small target attention maps between wheat ear features. We conduct comparative experiments on a publicly available global wheat head detection dataset, showing that the proposed count-supervised strategy outperforms existing position-supervised methods in terms of mean absolute error (MAE) and root mean square error (RMSE). This superior performance indicates that the proposed approach has a positive impact on improving ear counts and reducing labeling costs, demonstrating its great potential for agricultural counting tasks. The code is available at http://csnet.samlab.cn .