Rice Plant Counting, Locating, and Sizing Method Based on High-Throughput UAV RGB Images

Abstract

Rice plant counting is crucial for many applications in rice production, such as yield estimation, growth diagnosis, disaster loss assessment, etc. Currently, rice counting still heavily relies on tedious and time-consuming manual operation. To alleviate the workload of rice counting, we employed an UAV (unmanned aerial vehicle) to collect the RGB images of the paddy field. Then, we proposed a new rice plant counting, locating, and sizing method (RiceNet), which consists of one feature extractor frontend and 3 feature decoder modules, namely, density map estimator, plant location detector, and plant size estimator. In RiceNet, rice plant attention mechanism and positive-negative loss are designed to improve the ability to distinguish plants from background and the quality of the estimated density maps. To verify the validity of our method, we propose a new UAV-based rice counting dataset, which contains 355 images and 257,793 manual labeled points. Experiment results show that the mean absolute error and root mean square error of the proposed RiceNet are 8.6 and 11.2, respectively. Moreover, we validated the performance of our method with two other popular crop datasets. On these three datasets, our method significantly outperforms state-of-the-art methods. Results suggest that RiceNet can accurately and efficiently estimate the number of rice plants and replace the traditional manual method.

Plant phenotyping relevance

UAV画像からイネ個体の位置・サイズ・個体数を推定する手法RiceNetを開発し、データセット構築と性能検証まで行っており、植物表現型取得が研究の中心です。

Code and data availability

The paper explicitly states that all RiceNet source code is publicly available at the authors' GitHub repository. The URC dataset (355 UAV images, 257,793 labeled points) is described but no explicit public deposit URL is given, so it is not listed as an actionable asset.