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

Gaobi Kang¹, Jian Wang², Fanguo Zeng¹

¹Department of Electronic Engineering, South China Agricultural University, Guangzhou 510642, China.

Plants (Basel, Switzerland)

|April 28, 2023

Summary

This study introduces a new lightweight system using unmanned aerial vehicles (UAVs) and a global attention network (GloAN) for efficient rice lodging detection. The system accurately identifies lodging areas, minimizing crop loss and improving rice production management.

Keywords:

global attention network knowledge distillation rice lodging sematic segmentation smart agriculture transfer learning unmanned aerial vehicles

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Area of Science:

Agricultural Science
Computer Vision
Remote Sensing

Background:

Rice lodging significantly impacts crop yield and quality, necessitating efficient monitoring.
Traditional manual detection methods are labor-intensive and time-consuming, leading to delayed interventions.
Internet of Things (IoT) and unmanned aerial vehicles (UAVs) offer advanced solutions for crop stress monitoring.

Purpose of the Study:

To develop a novel, lightweight detection system for identifying rice lodging areas using UAVs.
To improve the efficiency and accuracy of rice lodging diagnosis.
To reduce production losses associated with rice lodging.

Main Methods:

Leveraging UAVs for acquiring rice growth distribution data.
Proposing a global attention network (GloAN) for efficient and accurate detection of lodging areas.
Utilizing knowledge distillation to test the generalization ability of GloAN with other models (Xception, VGG, ResNet, MobileNetV2).

Main Results:

The proposed GloAN system achieved a significant increase in detection accuracy with minimal computational overhead.
GloAN demonstrated strong generalization capabilities when integrated with other deep learning models.
An optimal mean intersection over union (mIoU) of 92.85% was achieved, highlighting the system's effectiveness.

Conclusions:

The developed UAV-based system with GloAN offers a flexible and efficient solution for rice lodging detection.
This technology can accelerate diagnostic processing and mitigate economic losses in rice farming.
The findings support the adoption of advanced remote sensing techniques for precision agriculture.