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TomSevNet: a hybrid CNN model for accurate tomato disease identification with severity level assessment

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Abstract

Tomato diseases are a major challenge for tomato growers, leading to significant yield losses and reduced quality of produce. Manual diagnosis of tomato diseases can be time-consuming and error-prone, making automated disease diagnosis an attractive solution. In this work, we develop a hybrid convolutional neural network (CNN) model using self-regulated layers and inception layer named as TomSevNet (Tom-Tomato disease Sev-Severity Net-Network) for accurate and efficient diagnosis of tomato diseases with severity levels. Our approach involves training a TomSevNet model on Plant Village dataset of tomato diseases segregated into different categories with their severity levels. The TomSevNet model is trained on a dataset containing 30 different classes belonging to nine tomato diseases of three severity levels, a healthy class, and two negative classes. Negative classes are included in the dataset to avoid misclassification problem. The TomSevNet classifier with Adadelta optimizer has performed extremely well and has attained the highest testing accuracy of 96.91% and the F1-score is 0.97. We also performed a comparison with other bench marked reference models, and the TomSevNet model outperformed them in terms of accuracy as well as F1-score.

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Data availability

Plant Village datasets are available as open source on the Mendeley Data https://data.mendeley.com/datasets/tywbtsjrjv/1.

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Acknowledgements

We extend our appreciation to the domain experts in the Department of Horticulture, Government of Karnataka for assisting us with the identification of severity level of diseases. We would like to express our sincere gratitude to Dr. A Srinivas, Dean Research and Development, RNS Institute of Technology for his invaluable assistance and support in the process of writing this research paper.

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Authors and Affiliations

  1. Acharya Institute of Technology, Affiliated to Visvesvaraya Technological University, Bengaluru, India

    U. Shruthi & V. Nagaveni

  2. RNS Institute of Technology, Bengaluru, India

    U. Shruthi

  3. AMC College of Engineering, Bengaluru, India

    V. Nagaveni

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  1. U. Shruthi
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  2. V. Nagaveni
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Correspondence to U. Shruthi.

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Shruthi, U., Nagaveni, V. TomSevNet: a hybrid CNN model for accurate tomato disease identification with severity level assessment. Neural Comput & Applic 36, 5165–5181 (2024). https://doi.org/10.1007/s00521-023-09351-w

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