Tomato leaf disease classification by exploiting transfer learning and feature concatenation

Abstract

Tomato is one of the most important vegetables worldwide. It is considered a mainstay of many countries’ economies. However, tomato crops are vulnerable to many diseases that lead to reducing or destroying production, and for this reason, early and accurate diagnosis of tomato diseases is very urgent. For this reason, many deep learning models have been developed to automate tomato leaf disease classification. Deep learning is far superior to traditional machine learning with loads of data, but traditional machine learning may outperform deep learning for limited training data. The authors propose a tomato leaf disease classification method by exploiting transfer learning and features concatenation. The authors extract features using pre‐trained kernels (weights) from MobileNetV2 and NASNetMobile; then, they concatenate and reduce the dimensionality of these features using kernel principal component analysis. Following that, they feed these features into a conventional learning algorithm. The experimental results confirm the effectiveness of concatenated features for boosting the performance of classifiers. The authors have evaluated the three most popular traditional machine learning classifiers, random forest, support vector machine, and multinomial logistic regression; among them, multinomial logistic regression achieved the best performance with an average accuracy of 97%.

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

“…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.…”

“…Mohanty et al [3] concentrated on the ML mechanism for the recognition of tomato plant leaves disease. Al-gaashani et al [4] concentrated on TL and feature concatenation process for the classification of tomato leaf disease. Wadadare et al [5] focused on DL based CNN for the recognition of tomato leaves disease with the inception process.…”

“…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.…”

“…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.…”

“…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. ML [7][8][9][10][11] can be considered as a solution for the classification of TLDIs into several categories.…”