Early Detection of Citrus Huanglongbing by UAV Remote Sensing Based on MGA-UNet

Citrus Huanglongbing (HLB), also known as citrus greening, is a severe disease that has caused substantial economic damage to the global citrus industry. Early detection is challenging due to the lack of distinctive early symptoms, making current diagnostic methods often ineffective. Therefore, there is an urgent need for an intelligent and timely detection system for HLB. This study leverages multispectral imagery acquired via unmanned aerial vehicles (UAVs) and deep convolutional neural networks. We introduce a novel model, MGA-UNet, specifically designed for HLB recognition. This image segmentation model enhances feature transmission by integrating channel attention and spatial attention within the skip connections. Furthermore, we evaluate the comparative effectiveness of high-resolution and multispectral images in HLB detection, finding that multispectral imagery offers superior performance. To address data imbalance and augment the dataset, we employ a generative model, DCGAN, for data augmentation, significantly boosting the model's recognition accuracy. Our proposed model achieved a mIoU of 0.89, a mPA of 0.94, a precision of 0.95, and a recall of 0.94 in identifying diseased trees. The intelligent monitoring method for HLB presented in this study offers a cost-effective and highly accurate solution, holding considerable promise for the early warning of this disease.柑橘黄龙病（Citrus Huanglongbing, HLB），亦称柑橘绿斑病，是一种给全球柑橘产业造成巨额经济损失的毁灭性病害。由于早期缺乏特异性症状，早期检测极具挑战性，当前主流诊断方法往往收效甚微，因此亟需一套智能且高效的HLB实时检测系统。本研究借助无人机（unmanned aerial vehicles, UAVs）采集的多光谱图像与深度卷积神经网络，提出了一款专为HLB识别设计的新型模型MGA-UNet。该图像分割模型通过在跳跃连接中集成通道注意力与空间注意力机制，有效强化了特征传递效率。此外，本研究对比评估了高分辨率图像与多光谱图像在HLB检测中的应用性能，结果表明多光谱图像具备更优的检测效果。为解决数据集类别不平衡问题并扩充训练数据，本研究采用生成模型DCGAN开展数据增强操作，显著提升了模型的识别准确率。所提模型在病株识别任务中取得了平均交并比（mean Intersection over Union, mIoU）0.89、平均像素准确率（mean Pixel Accuracy, mPA）0.94、精确率0.95以及召回率0.94的优异性能。本研究提出的HLB智能监测方法提供了一种低成本、高精度的解决方案，在该病害的早期预警领域具备可观的应用前景。