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Identification of sweetpotato virus disease-infected leaves from field images using deep learning

文献类型： 外文期刊

作者： Ding, Ziyu 1 ; Zeng, Fanguo 1 ; Li, Haifeng 1 ; Zheng, Jianyu 1 ; Chen, Junzhi 1 ; Chen, Biao 1 ; Zhong, Wenshan 1 ; Li, Xuantian 1 ; Wang, Zhangying 2 ; Huang, Lifei 2 ; Yue, Xuejun 1 ;

作者机构： 1.South China Agr Univ, Coll Elect Engn, Coll Artificial Intelligence, Guangzhou, Peoples R China

2.Guangdong Acad Agr Sci, Crop Res Inst, Guangdong Prov Key Lab Crops Genet & Improvement, Guangzhou, Guangdong, Peoples R China

3.South China Agr Univ, Natl Key Lab Green Pesticides, Guangzhou, Peoples R China

关键词： sweetpotato; virus disease; deep learning; RGB image; semantic segmentation

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： 2024 年 15 卷

页码：

收录情况： SCI

摘要： Introduction Sweetpotato virus disease (SPVD) is widespread and causes significant economic losses. Current diagnostic methods are either costly or labor-intensive, limiting both efficiency and scalability.Methods The segmentation algorithm proposed in this study can rapidly and accurately identify SPVD lesions from field-captured photos of sweetpotato leaves. Two custom datasets, DS-1 and DS-2, are utilized, containing meticulously annotated images of sweetpotato leaves affected by SPVD. DS-1 is used for training, validation, and testing the model, while DS-2 is exclusively employed to validate the model's reliability. This study employs a deep learning-based semantic segmentation network, DeepLabV3+, integrated with an Attention Pyramid Fusion (APF) module. The APF module combines a channel attention mechanism with multi-scale feature fusion to enhance the model's performance in disease pixel segmentation. Additionally, a novel data augmentation technique is utilized to improve recognition accuracy in the edge background areas of real large images, addressing issues of poor segmentation precision in these regions. Transfer learning is applied to enhance the model's generalization capabilities.Results The experimental results indicate that the model, with 62.57M parameters and 253.92 Giga Floating Point Operations Per Second (GFLOPs), achieves a mean Intersection over Union (mIoU) of 94.63% and a mean accuracy (mAcc) of 96.99% on the DS-1 test set, and an mIoU of 78.59% and an mAcc of 79.47% on the DS-2 dataset.Discussion Ablation studies confirm the effectiveness of the proposed data augmentation and APF methods, while comparative experiments demonstrate the model's superiority across various metrics. The proposed method also exhibits excellent detection results in simulated scenarios. In summary, this study successfully deploys a deep learning framework to segment SPVD lesions from field images of sweetpotato foliage, which will contribute to the rapid and intelligent detection of sweetpotato diseases.

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