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.

LodgeNet: an automated framework for precise detection and classification of wheat lodging severity levels in precision farming

“…Wheat lines with lodging resistance are more desirable, and a target trait in breeding ( Shah et al, 2019 ). Various stakeholders, including breeders, agronomists, plant physiologists, farmers, and crop insurance personnel, require accurate and timely assessment of wheat lodging to mitigate its impact on wheat growth, production and marketability ( Kang et al., 2023 ; Sun et al., 2023 ). Lodging resistance would help to minimize yield losses and maintain crop quality ( Yang et al., 2021 ).…”

Wheat Lodging Direction Detection for Combine Harvesters Based on Improved K-Means and Bag of Visual Words

“…Compared with traditional manual detection, typical lodging detection techniques mainly rely on sensors such as visible light, multispectral, hyperspectral, near-infrared, and radar [4] sensors on satellite [5], radar [6], and unmanned aerial vehicles (UAVs) [7]. The sensitivity of crop features such as texture, color, and vegetation index under single/multiple growth stages with a high-throughput and large field of view to lodging data is analyzed to recognize and classify lodging areas [8,9]. Its data are rich, but detection requires a large spatiotemporal span, lacks spatial information on crop lodging, and has scale limitations.…”