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

Abstract

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.

Plant phenotyping relevance

UAVマルチスペクトル画像と新規セグメンテーションモデルにより、感染樹という植物の病害状態を直接推定する手法を開発・評価しており、フェノタイピング手法が中心である。

Code and data availability

The supplied blocks describe UAV multispectral imagery collection, qPCR labeling, DCGAN augmentation, and the MGA-UNet model, but contain no data availability statement, public repository deposit, or authors' public URL for the dataset, images, code, or trained model. The only URL present is the article DOI itself, and

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