Image-based Tomato Disease Identification Using Convolutional Neural Network

Birhanu Gardie; Kassahun Azezew; Smegnew Asemie

doi:10.17485/IJST/v14i42.1164

Article

Image-based Tomato Disease Identification Using Convolutional Neural Network

VIEWS 1914
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Indian Journal of Science and Technology

DOI: 10.17485/IJST/v14i42.1164

Year: 2021, Volume: 14, Issue: 42, Pages: 3126-3132

Original Article

Image-based Tomato Disease Identification Using Convolutional Neural Network

Birhanu Gardie^1*, Kassahun Azezew¹, Smegnew Asemie¹

¹School of Computing and informatics, Mizan-Tepi University, Ethiopia

*Corresponding Author
Email: [email protected], [email protected], [email protected]

Received Date:24 June 2021, Accepted Date:12 November 2021, Published Date:09 December 2021

This work is licensed under a Creative Commons Attribution 4.0 International License.

Abstract

Objectives: Agriculture is the main food source and farmers are challenging a great production loss annually due to plant leaf disease. Early identification of tomato plant diseases help farmers to take preventive measure to reduce production loss. As a result, to recognize tomato plant leaf diseases in its early stage, a deep learning approach is discussed. Methods: For tomato disease identification and classification a convolutional neural network model is used in this study. CNN is capable for fine-grained disease identification as a technique which avoids feature engineering and threshold segmentation through automatic feature extraction. Findings: In this experiment, we have used 22,930 leaf image dataset are taken from plant village dataset, some are collected from Awash Melkasa tomato cultivation area in various seasons. Image processing is conducted along with pixel with operations it enhance the image data followed with feature extraction of patterns of collected leaves to detect the leaf diseases. The extracted patterns are fit into the neural network model with 100 epochs, 80/20 splitting ratio, and 0.001 learning rate. Hence the tomato disease network model achieves an overall 98.3% accuracy performance. Novelty: In order to detect tomato leave disease, we performed image processing with pixel-wise operation to enhance the leaf images that can be followed by feature extraction to classify patterns. We extend, and adopt neural network using local images collected under challenging environment datasets and optimization is performed in Adam optimizer with categorical entropy as loss function.

Keywords: convolutional neural network; deep learning; leaf disease identification; ReLu

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Copyright

© 2021 Gardie et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Published By Indian Society for Education and Environment (iSee)