MCD-Yolov5: Accurate, Real-Time Crop Disease and Pest Identification Approach Using UAVs

Li, Lianpeng; Zhao, Hui; Liu, Ning

doi:10.3390/electronics12204365

Cited by 1 publication

(1 citation statement)

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“…Hua et al 23 proposed a novel deep compressed sensing network model CSLSNet, which combines compressed sensing theory and traditional neural network technology, thus making the recognition accuracy of crop pests and diseases reache 90.08%. Li et al 24 proposed a fusion design MCD-Yolov5 model for accurate, efficient and real-time recognition of agricultural pests and diseases. Through verification on the UAV platform, the detection accuracy of MCD-Yolov5 for pests and diseases reaches 88.12%.…”

Section: Introductionmentioning

confidence: 99%

EResNet‐SVM: an overfitting‐relieved deep learning model for recognition of plant diseases and pests

Xiong,

Li,

Wang

et al. 2024

J Sci Food Agric

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BACKGROUNDThe accurate recognition and early warning for plant diseases and pests are a prerequisite of intelligent prevention and control for plant diseases and pests. Due to the phenotype similarity of the hazarded plant after plant diseases and pests occur as well as the interference of the external environment, traditional deep learning models often face the overfitting problem in phenotype recognition of plant diseases and pests, which leads to not only slow convergence speed of the network but also low recognition accuracy.RESULTSMotivated by the above problems, this paper proposes a deep learning model EResNet‐SVM to alleviate the overfitting for the recognition and classification of plant diseases and pests. First, the feature extraction capability of the model is improved by increasing feature extraction layers in the convolutional neural network (CNN). Second, the order‐reduced modules are embedded and a sparsely activated function is introduced to reduce model complexity and alleviate overfitting. Finally, a classifier fused by support vector machine (SVM) and fully connected layers (FC) are introduced to transforms the original nonlinear classification problem into a linear classification problem in high‐dimensional space to further alleviate the overfitting and improve the recognition accuracy of plant diseases and pests. The ablation experiments further demonstrate that the fused structure can effectively alleviate the overfitting and improve the recognition accuracy. The experimental recognition results for typical plant diseases and pests show that the proposed EResNet‐SVM model has 99.30% test accuracy for eight conditions (seven plant diseases and one normal), which is 5.90% higher than the original ResNet18. Compared with the classic AlexNet, GoogLeNet, Xception, SqueezeNet and DenseNet201 models, the accuracy of the EResNet‐SVM model has improved by 5.10%, 7%, 8.10%, 6.20% and 1.90%, respectively. The testing accuracy of the EResNet‐SVM model for 6 insect pests is 100%, which is 3.90% higher than that of the original ResNet18 model.CONCLUSIONThis research can provide useful references for alleviating the overfitting problem in deep learning, and it can also provide a theoretical and technical support for the intelligent detection and control of plant diseases and pests.This article is protected by copyright. All rights reserved.

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Section: Introductionmentioning

confidence: 99%