Optimized Deep Learning Algorithms for Tomato Leaf Disease Detection with Hardware Deployment

Tarek, Hesham; Aly, Hesham; Eisa, Saleh; Abul-Soud, M.

doi:10.3390/electronics11010140

Cited by 47 publications

(22 citation statements)

References 34 publications

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“…In DS-MENet, depthwise separable convolution is used to replace the traditional convolution in Dense Block to reduce network parameters and model running time; the new activation function ReMish is used to alleviate the neuron death problem caused by the original ReLU function and enhance the robustness of the model: the MCF method is used to enhance the backbone of the Dense Block, improve the network's ability to extract citrus disease features, and preserve detailed information to the greatest extent. Experimental results show that, compared with modern convolutional neural network models, such as AlexNet (Krizhevsky et al, 2012), ResNet50 (He et al, 2016), ResNeXt (Gp et al, 2020), InceptionV4 (Tian et al, 2021), MobileNetV3 (Tarek et al, 2022), EfficientNet (Chen et al, 2021), DenseNet121 (Huang et al, 2017), and EfficientNetV2 (Sunil et al, 2022), the method presented in this paper has a better recognition and classification effect on citrus diseases with similar characteristics.…”

Section: Related Workmentioning

confidence: 91%

DS-MENet for the classification of citrus disease

Liu

Zhou

et al. 2022

Front. Plant Sci.

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Affected by various environmental factors, citrus will frequently suffer from diseases during the growth process, which has brought huge obstacles to the development of agriculture. This paper proposes a new method for identifying and classifying citrus diseases. Firstly, this paper designs an image enhancement method based on the MSRCR algorithm and homomorphic filtering algorithm optimized by Laplacian (HFLF-MS) to highlight the disease characteristics of citrus. Secondly, we designed a new neural network DS-MENet based on the DenseNet-121 backbone structure. In DS-MENet, the regular convolution in Dense Block is replaced with depthwise separable convolution, which reduces the network parameters. The ReMish activation function is used to alleviate the neuron death problem caused by the ReLU function and improve the robustness of the model. To further enhance the attention to citrus disease information and the ability to extract feature information, a multi-channel fusion backbone enhancement method (MCF) was designed in this work to process Dense Block. We use the 10-fold cross-validation method to conduct experiments. The average classification accuracy of DS-MENet on the dataset after adding noise can reach 95.02%. This shows that the method has good performance and has certain feasibility for the classification of citrus diseases in real life.

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Section: Related Workmentioning

confidence: 91%

DS-MENet for the classification of citrus disease

Liu

Zhou

et al. 2022

Front. Plant Sci.

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“…In this work, a MI [19][20][21][22][23][24][25][26][27][28][29][30][31][32] based approach is proposed for the classification of TLDIs into BS, EB, LB, LM, SLS, TMV, TYLCV and HL types. The proposed approach is focused on the stacking of LRG, SVMN, RFS and NNT methods to carry out such classification.…”

Section: Methodsmentioning

confidence: 99%

“…Many research works have been accomplished related to the TLDIs processing and analysis [1][2][3][4][5][6][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]. Some of the works are mentioned as follows.…”

Section: Related Workmentioning

confidence: 99%

“…From the literature survey, it is observed that a single method may not be efficient enough to accomplish the classification process of TLDIs [1][2][3][4][5][6][13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31][32]] in all scenarios. A method which is working well in a scenario may not perform well in other scenarios.…”

Section: Research Gapmentioning

confidence: 99%

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A Machine Intelligent Based Approach for the Classification and Analysis of Tomato Leaf Disease Images

Jena

2022

IJHSP

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Purpose: Vegetable production plays a vital role for the existence of human society. It is very much essential for the proper care of vegetable plants for better production of vegetables. However, vegetable plant leaf disease is a major concern in the current scenario. Tomato leaf disease is one of them. So, preventive measures should be taken to avoid the rise of tomatoes and other leaf diseases at the earliest for better production of vegetables. Approach: In this work, a machine intelligent (MI) based approach is proposed for the classification of tomato leaf disease images (TLDIs) into the bacterial spot (BS), early blight (EB), late blight (LB), leaf mold (LM), septoria leaf spot (SLS), tomato mosaic virus (TMV), tomato yellow leaf curl virus (TYLCV) and healthy (HL) types. The proposed approach is focused on the stacking (hybridization) of Logistic Regression (LRG), Support Vector Machine (SVMN), Random Forest (RFS) and Neural Network (NNT) methods to carry out such classification. The proposed method is compared with other machine learning (ML) based methods such as LRG, SVMN, RFS, NNT, Decision Tree (DTR), AdaBoost (ADB), Naïve Bayes (NBY), K-Nearest Neighbor (KNNH) and Stochastic Gradient Descent (SGDC) for performance analysis. Result: The proposed method and other ML based methods have been implemented using Python based Orange 3.26.0. In this work, 1600 TLDIs having 200 numbers of each type such as BS, EB, LB, LM, SLS, TMV, TYLCV and HL are taken from the Kaggle source. The performance of all the methods is assessed using the performance parameters such as classification accuracy (CA), F1, Precision (PR) and Recall (RC). From the results, it is found that the proposed method is capable of providing better classification results in terms of CA, F1, PR and RC as compared to other ML based methods such as LRG, SVMN, RFS, NNT, DTR, ADB, NBY, KNNH and SGD. Originality: In this work, a MI based approach is proposed by focusing on the stacking of LRG, SVMN, RFS and NNT methods to carry out the classification of TLDIs into several types such as BS, EB, LB, LM, SLS, TMV, TYLCV and HL. The proposed approach performs better in terms of CA, F1, PR and RC as compared to LRG, SVMN, RFS, NNT, DTR, ADB, NBY, KNNH and SGDC methods. Paper Type: Conceptual Research.

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“…Tomato is an important horticultural crop and is popular in smart farms in many countries. Many types of research related to tomatoes are made to increase the tomato quality and production, such as leaf disease detection [ 2 , 3 , 4 , 5 ]. These researchers used different deep convolution neural networks to classify the tomato leaf images to different types of diseases.…”

Section: Introductionmentioning

confidence: 99%

Fast Detection of Tomato Sucker Using Semantic Segmentation Neural Networks Based on RGB-D Images

Giang

Khai

et al. 2022

Sensors

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Tomato sucker or axillary shoots should be removed to increase the yield and reduce the disease on tomato plants. It is an essential step in the tomato plant care process. It is usually performed manually by farmers. An automated approach can save a lot of time and labor. In the literature review, we see that semantic segmentation is a process of recognizing or classifying each pixel in an image, and it can help machines recognize and localize tomato suckers. This paper proposes a semantic segmentation neural network that can detect tomato suckers quickly by the tomato plant images. We choose RGB-D images which capture not only the visual of objects but also the distance information from objects to the camera. We make a tomato RGB-D image dataset for training and evaluating the proposed neural network. The proposed semantic segmentation neural network can run in real-time at 138.2 frames per second. Its number of parameters is 680, 760, much smaller than other semantic segmentation neural networks. It can correctly detect suckers at 80.2%. It requires low system resources and is suitable for the tomato dataset. We compare it to other popular non-real-time and real-time networks on the accuracy, time of execution, and sucker detection to prove its better performance.

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Optimized Deep Learning Algorithms for Tomato Leaf Disease Detection with Hardware Deployment

Cited by 47 publications

References 34 publications

DS-MENet for the classification of citrus disease

DS-MENet for the classification of citrus disease

A Machine Intelligent Based Approach for the Classification and Analysis of Tomato Leaf Disease Images

Fast Detection of Tomato Sucker Using Semantic Segmentation Neural Networks Based on RGB-D Images

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