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StripeRust-Pocket:A mobile-based Deep Learning Application for Efficient Disease Severity Assessment of Wheat Stripe Rust

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Plant Phenomics 2024年第3期6卷 771-786页

作者： Weizhen Liu Yuxi Chen Zhaoxin Lu Xiaoyu Lu Ze Wu Ziyao Zheng Yongqiang Suo Caixia Lan Xiaohui Yuan School of Computer Science and Artificial Intelligence Wuhan University of TechnologyWuhanHubei 430070China Wuhan University of Technology Chongqing Research Institute Chongqing 401120China Sanya Science and Education Innovation Park of Wuhan University of Technology SanyaHainan 572025China Hubei Hongshan Laboratory College of Plant Science and TechnologyHuazhong Agricultural UniversityWuhan 430070HubeiChina Engineering Research Centre of Chinese Ministry of Education for Edible and Medicinal Fungi Jilin Agricultural UniversityChangchunJilin 130118China

Wheat stripe rust poses a marked threat to global wheat *** and effective disease severity assessments are crucial for disease resistance breeding and timely management of field *** this study,we propose a practical solution using mobile-based deep learning and model-assisted ***-Pocket,a user-friendly mobile application developed based on deep learning models,accurately quantifies disease severity in wheat stripe rust leaf images,even under complex ***,StripeRust-Pocket facilitates image acquisition,result storage,organization,and *** underlying model employed by StripeRust-Pocket,called StripeRustNet,is a balanced lightweight 2-stage *** first stage utilizes mobileNetV2-DeepLabV3+for leaf segmentation,followed by ResNet50-DeepLabV3+in the second stage for lesion *** severity is estimated by calculating the ratio of the lesion pixel area to the leaf pixel *** achieves 98.65%mean intersection over union(MIoU)for leaf segmentation and 86.08%MIoU for lesion *** using an additional 100 field images demonstrated a mean correlation of over 0.964 with 3 expert visual *** address the challenges in manual labeling,we introduce a 2-stage labeling pipeline that combines model-assisted labeling,manual correction,and spatial *** apply this pipeline to our self-collected dataset,reducing the annotation time from 20 min to 3 min per *** method provides an efficient and practical solution for wheat stripe rust severity assessments,empowering wheat breeders and pathologists to implement timely disease *** also demonstrates how to address the"last mile"challenge of applying computer vision technology to plant phenomics.

关键词： application learning pocket deep assessment disease efficient mobile-based severity striperust

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