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An Efficient Approach to Detect and Predict the Tomato Leaf Disease Using Enhance Segmentation Neural Network

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Abstract

The agricultural sector plays a crucial role in the Indian economy. According to the Economic Survey of India 2020–21 report, production in FY21 was recorded at 303.34 million tonnes. India is the world’s second-largest producer of tomatoes, both in terms of area and production. In India, tomato is one of the most important and highly consumed vegetable crops. Tomatoes are highly nutritional and thus good for human health. It makes a substantial contribution to the GDP. Being economically valuable, it is produced in large quantities. Because of adverse environmental conditions, several diseases are caused by bacteria, fungi and viruses in various parts of tomato plants. Diseases result in the loss of production and thus farmers have to face huge economic losses. In order to help the farmers, early detection of diseases is needed to be done using the latest and most accurate methods. In this paper, the ES-CNN model(Enhancement Seg- mentation CNN model) is proposed in which mean filtering and CLAHE are performed for the image enhancement step and K-means clustering is used for the image segmentation. ResNet-50, a CNN architecture, has been used for feature extraction and classification of images.

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Data Availability

Data used in the implementation of the proposed approach will be made available on request after publication.

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Authors and Affiliations

  1. Rajkiya Engineering College, Kannauj, Uttar Pradesh, India

    Abhishek Bajpai & Titiksha Tyagi

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  1. Abhishek Bajpai
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  2. Titiksha Tyagi
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Correspondence to Abhishek Bajpai.

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This article is part of the topical collection “Machine Learning in Pattern Analysis” guest edited by Reinhard Klette, Brendan McCane, Gabriella Sanniti di Baja, Palaiahnakote Shivakumara and Liang Wang.

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Bajpai, A., Tyagi, T. An Efficient Approach to Detect and Predict the Tomato Leaf Disease Using Enhance Segmentation Neural Network. SN COMPUT. SCI. 4, 795 (2023). https://doi.org/10.1007/s42979-023-02262-6

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