Tomato Plant Disease Classification Using Deep Learning Architectures: A Review

Shruthi, U; Nagaveni, V.; Arvind, C.; Sunil, G.

doi:10.1007/978-981-16-7389-4_15

Cited by 21 publications

(7 citation statements)

References 44 publications

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“…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%

“…Paymode et al [1] concentrated on the convolutional neural network (CNN) and transfer learning (TL) mechanism for the classification of multi-crop leaf disease images. Shruthi et al [2] focused on the review of deep learning (DL) architectures for the classification of tomato plant disease. Mohanty et al [3] concentrated on the ML mechanism for the recognition of tomato plant leaves disease.…”

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%

“…The vegetables can be classified as tomato, potato, cabbage, cauliflower, broccoli, pumpkin, bean, etc. However, vegetable plant leaf diseases [1][2][3][4][5][6] and other diseases greatly hamper the production of vegetables. Tomato leaf diseases are focused in this work.…”

Section: Introductionmentioning

confidence: 99%

“…Tomato leaf diseases are focused in this work. The tomato plant leaf diseases [1][2][3] can be classified as BS, EB, LB, LM, SLS, TMV, TYLCV, etc. So, it is very much essential to classify the TLDIs [4][5][6] such tomato leaf diseases.…”

Section: Introductionmentioning

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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