Apple Leaf Disease Recognition Method Based on Siamese Dilated Inception Network with Less Training Samples

Zhang, Shanwen; Wang, Dengwu; Yu, Chang-Qing

doi:10.2139/ssrn.4329507

Cited by 3 publications

(4 citation statements)

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“…This model combines coordinate attention modules and weight adaptive multi‐scale feature fusion, achieving a classification accuracy of 98.32% on the AppleLeaf9 datasets. Zhang et al 34 . proposed a siamese dilated inception network method based on small sample training, which utilizes multi‐scale feature extraction and cosine distance learning to achieve effective apple leaf disease recognition on small datasets.…”

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%

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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“…Chai et al [28] used a combination of step-based discrimination and Bayesian discrimination to select feature parameters and a combination of principal component analysis and Fischer discrimination to construct an identification model with a recognition rate of 98.32%. For example, In the study of Zhang et al [29], they used the SVM method to automatically distinguish three different diseases on apple leaves based on the color characteristics and histograms of apple leaf disease spots. This method can achieve 96% accuracy.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Tomato Leaf Diseases Based on DIMPCNET

Ding

Zhao

et al. 2023

Agronomy

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The identification of tomato leaf diseases is easily affected by complex backgrounds, small differences between different diseases, and large differences between the same diseases. Therefore, we propose a novel classification network for tomato leaf disease, the Dense Inception MobileNet-V2 parallel convolutional block attention module network (DIMPCNET). To begin, we collected a total of 1256 original images of 5 tomato leaf diseases and expanded them to 8190 using data enhancement techniques. Next, an improved bilateral filtering and threshold function (IBFTF) algorithm is designed to effectively remove noise. Then, the Dense Inception convolutional neural network module (DI) was designed to alleviate the problem of large intra-class differences and small inter-class differences. Then, a parallel convolutional block attention module (PCBAM) was added to MobileNet-V2 to reduce the impact of complex backgrounds. Finally, the experimental results show that the recognition accuracy and F1-score obtained by DIMPCNET are 94.44% and 0.9475. The loss is approximately 0.28%. This method is the most advanced and provides a new idea for the identification of crop diseases, such as tomatoes, and the development of smart agriculture.

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“…Lemons and grapefruits have broad, yellowish imperfections, but tangerines have elevated spots. Alternaria linariae causes early tomato blight 5 . Fruit‐wide lesions, diseased apple, yeast Septoria lycopersici causes tomato septoria leaf spot, which is characterized by numerous small, circular spots with beige centers and black margins on older leaves.…”

Section: Introductionmentioning

confidence: 99%

“…Alternaria linariae causes early tomato blight. 5 Fruit-wide lesions, diseased apple, yeast Septoria lycopersici causes tomato septoria leaf spot, which is characterized by numerous small, circular spots with beige centers and black margins on older leaves. Black spots generate spores.…”

Section: Introductionmentioning

confidence: 99%

Using a novel convolutional neural network for plant pests detection and disease classification

et al. 2023

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Background Early plant diseases and pests identification reduces social, economic, and environmental deficiencies entailing toxic chemical utilization on agricultural farms, thus posing a threat to global food security. Methodology An enhanced convolutional neural network (CNN) along with long short‐term memory (LSTM) using a majority voting ensemble classifier has been proposed to tackle plant pest and disease identification and classification. Within pre‐trained models, deep feature extractions have been obtained from connected layers. Deep features have been extracted and are sent to the LSTM layer to build a robust, enhanced LSTM‐CNN model for detecting plant pests and diseases. Experiments were carried out using a Turkey dataset, with 4447 apple pests and diseases categorized into 15 different classes. Results The study was evaluated in different CNNs using logistic regression (LR), LSTM, and extreme learning machine (ELM), focusing on plant disease detection problems. The ensemble majority voting classifier was used at the LSTM layer to detect and classify plant disease labels. Furthermore, an autonomous selection of the optimal LSTM layer network parameters was applied. Finally, the performance was validated based on sensitivity, F1 score, accuracy, and specificity using LSTM, ELM, and LR classifiers. Conclusion The presented model attained 99.2% accuracy compared to the cutting‐edge models on different classifiers such as LSTM, LR, and ELM, and performed better compared to transfer learning. Pre‐trained models, such as VGG19, VGG18, and AlexNet, demonstrated better accuracy when the fc6 layer was compared with other layers. © 2023 Society of Chemical Industry.

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Apple Leaf Disease Recognition Method Based on Siamese Dilated Inception Network with Less Training Samples

Cited by 3 publications

References 0 publications

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

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

Recognition of Tomato Leaf Diseases Based on DIMPCNET

Using a novel convolutional neural network for plant pests detection and disease classification

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