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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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