Automatic detection and counting of wheat seedling based on unmanned aerial vehicle images.

Abstract

Wheat is an important food crop, wheat seedling count is very important to estimate the emergence rate and yield prediction. Timely and accurate detection of wheat seedling count is of great significance for field management and variety breeding. In actual production, the method of artificial field investigation and statistics of wheat seedlings is time-consuming and laborious. Aiming at the problems of small targets, dense distribution and easy occlusion of wheat seedling in the field, a wheat seedling number detection model (DM_IOC_fpn) combining local and global features was proposed in this study. Firstly, the wheat seedling image is preprocessed, and the wheat seedling dataset is built by using the point annotation method. Secondly, the density enhanced encoder module is introduced to improve the network structure and extract local and global contextual feature information of wheat seedling. Finally, the total loss function is constructed by introducing counting loss, classification loss, and regression loss to optimize the model, so as to enable accurate judgment of wheat seedling position and category information. Experiment on self-built dataset have shown that the root mean square error (RMSE) and mean absolute error (MAE) of DM_IOC_fpn were 2.91 and 2.23, respectively, which were 1.78 and 1.04 lower than the original IOCFormer. Compared with the current mainstream object detection models, DM_IOC_fpn has better counting performance. DM_IOC_fpn can accurately detect the number of small target wheat seedling, and better solve the problem of occlusion and overlapping of wheat seedling, so as to achieve the accurate detection of wheat seedling, which provides important theoretical and technical support for automatic counting of wheat seedlings and yield prediction in complex field environment.

Plant phenotyping relevance

小麦幼苗数という植物形質をUAV画像から自動抽出・計数するモデルを開発し、自作データセット上で性能評価しており、表現型取得手法が中心である。

Code and data availability

The paper describes a self-built UAV wheat seedling image dataset (2000 original images expanded to 4000 with point annotations) and the DM_IOC_fpn model, but no public repository, code deposit, or authors' URL is provided. The only availability statement is a promise to share raw data by contacting the authors. The FA

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