Lightweight Detection System with Global Attention Network (GloAN) for Rice Lodging.

Abstract

Rice lodging seriously affects rice quality and production. Traditional manual methods of detecting rice lodging are labour-intensive and can result in delayed action, leading to production loss. With the development of the Internet of Things (IoT), unmanned aerial vehicles (UAVs) provide imminent assistance for crop stress monitoring. In this paper, we proposed a novel lightweight detection system with UAVs for rice lodging. We leverage UAVs to acquire the distribution of rice growth, and then our proposed global attention network (GloAN) utilizes the acquisition to detect the lodging areas efficiently and accurately. Our methods aim to accelerate the processing of diagnosis and reduce production loss caused by lodging. The experimental results show that our GloAN can lead to a significant increase in accuracy with negligible computational costs. We further tested the generalization ability of our GloAN and the results show that the GloAN generalizes well in peers' models (Xception, VGG, ResNet, and MobileNetV2) with knowledge distillation and obtains the optimal mean intersection over union (mIoU) of 92.85%. The experimental results show the flexibility of GloAN in rice lodging detection.

Plant phenotyping relevance

UAV画像からイネの倒伏領域（植物状態）を抽出する軽量な検出システムとGloANを提案・評価しており、表現型取得・推定手法が中心である。

Code and data availability

The authors explicitly state that the rice lodging dataset (UAV images with annotations) and the source code for the GloAN analysis are open sourced and publicly available on GitHub.