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Early detection of Citrus Huanglongbing by UAV remote sensing based on MGA-UNet

文献类型： 外文期刊

作者： Ye, Naibo 1 ; Mai, Wenyong 2 ; Qin, Feng 1 ; Yuan, Sen 1 ; Liu, Bo 1 ; Li, Zaiyuan 1 ; Liu, Conghui 1 ; Wan, Fanghao 1 ; Qian, Wanqiang 1 ; Wu, Zhongzhen 2 ; Qiao, Xi 1 ;

作者机构： 1.Chinese Acad Agr Sci, Agr Genom Inst Shenzhen, Minist Agr & Rural Affairs, Guangdong Lab Lingnan Modern Agr,Genome Anal Lab, Shenzhen, Peoples R China

2.Zhongkai Univ Agr & Engn, Inst Management Invas Alien Species, Guangzhou City Key Lab Subtrop Fruit Trees Outbrea, Guangzhou, Peoples R China

关键词： Citrus HuangLongBing; citrus greening; UAV; multispectral images; deep learning; generative model

期刊名称：FRONTIERS IN PLANT SCIENCE （ 影响因子：4.8； 五年影响因子：5.7 ）

ISSN： 1664-462X

年卷期： 2025 年 16 卷

页码：

收录情况： SCI

摘要： 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. This study 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, this study 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, this study 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.

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