Peanut leaf disease identification with deep learning algorithms

被引:0
作者
Laixiang Xu
Bingxu Cao
Shiyuan Ning
Wenbo Zhang
Fengjie Zhao
机构
[1] Hainan University,School of Information and Communication Engineering
[2] Luohe Vocational Technology College,Information Engineering Department
[3] China Electronics Technology Group Corporation 36th Research Institute,Department of Software Information
[4] Henan Sui Xian People’s Hospital,undefined
[5] The First Affiliated Hospital of Zhengzhou University,undefined
[6] Shangqiu First People’s Hospital,undefined
来源
Molecular Breeding | 2023年 / 43卷
关键词
Crop diseases; Peanut leaf; Deep learning; Generalization;
D O I
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中图分类号
学科分类号
摘要
Peanut is an essential food and oilseed crop. One of the most critical factors contributing to the low yield and destruction of peanut plant growth is leaf disease attack, which will directly reduce the yield and quality of peanut plants. The existing works have shortcomings such as strong subjectivity and insufficient generalization ability. So, we proposed a new deep learning model for peanut leaf disease identification. The proposed model is a combination of an improved X-ception, a parts-activated feature fusion module, and two attention-augmented branches. We obtained an accuracy of 99.69%, which was 9.67%–23.34% higher than those of Inception-V4, ResNet 34, and MobileNet-V3. Besides, supplementary experiments were performed to confirm the generality of the proposed model. The proposed model was applied to cucumber, apple, rice, corn, and wheat leaf disease identification, and yielded an average accuracy of 99.61%. The experimental results demonstrate that the proposed model can identify different crop leaf diseases, proving its feasibility and generalization. The proposed model has a positive significance for exploring other crop diseases’ detection.
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共 22 条
[1]  
Cravero A(2022)Challenges to use machine learning in agricultural big data: a systematic literature review Agronomy-Basel 12 10-2229
[2]  
Pardo S(2022)Detection of rice plant disease using AdaBoostSVM classifier Agron J 114 2213-532
[3]  
Sepulveda S(2022)Improved CNN method for crop pest identification based on transfer learning Comput Intell Neurosci 129 521-517
[4]  
Munoz L(2022)Early detection and identification of grape diseases using convolutional neural networks J Plant Dis Prot 14 508-undefined
[5]  
Kumar K(2022)Image processing based system for the detection, identification and treatment of tomato leaf diseases Multimed Tools Appl undefined undefined-undefined
[6]  
Kannan E(2022)Advanced multi-class deep learning convolution neural network approach for insect pest classification using TensorFlow Int J Environ Sci Technol undefined undefined-undefined
[7]  
Liu Y(2022)Smartphone imaging spectrometer for egg/meat freshness monitoring Anal Methods undefined undefined-undefined
[8]  
Zhang X(undefined)undefined undefined undefined undefined-undefined
[9]  
Gao Y(undefined)undefined undefined undefined undefined-undefined
[10]  
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