Recent advances in plant disease severity assessment using convolutional neural networks

In modern agricultural production, the severity of diseases is an important factor that directly affects the yield and quality of plants. In order to effectively monitor and control the entire production process of plants, not only the type of disease, but also the severity of the disease must be cl...

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Vydané v:Scientific reports Ročník 13; číslo 1; s. 2336 - 13
Hlavní autori: Shi, Tingting, Liu, Yongmin, Zheng, Xinying, Hu, Kui, Huang, Hao, Liu, Hanlin, Huang, Hongxu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: London Nature Publishing Group UK 09.02.2023
Nature Publishing Group
Nature Portfolio
Predmet:
631/449/447
639/705/117
Agricultural production
Comparative analysis
Deep learning
Humanities and Social Sciences
Image Processing, Computer-Assisted - methods
multidisciplinary
Neural networks
Neural Networks, Computer
Patient Acuity
Plant diseases
Science
Science (multidisciplinary)
Segmentation
ISSN:2045-2322, 2045-2322
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Shrnutí:In modern agricultural production, the severity of diseases is an important factor that directly affects the yield and quality of plants. In order to effectively monitor and control the entire production process of plants, not only the type of disease, but also the severity of the disease must be clarified. In recent years, deep learning for plant disease species identification has been widely used. In particular, the application of convolutional neural network (CNN) to plant disease images has made breakthrough progress. However, there are relatively few studies on disease severity assessment. The group first traced the prevailing views of existing disease researchers to provide criteria for grading the severity of plant diseases. Then, depending on the network architecture, this study outlined 16 studies on CNN-based plant disease severity assessment in terms of classical CNN frameworks, improved CNN architectures and CNN-based segmentation networks, and provided a detailed comparative analysis of the advantages and disadvantages of each. Common methods for acquiring datasets and performance evaluation metrics for CNN models were investigated. Finally, this study discussed the major challenges faced by CNN-based plant disease severity assessment methods in practical applications, and provided feasible research ideas and possible solutions to address these challenges.
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ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-023-29230-7