Plant Leaf Disease Detection and Classification Using Deep Learning: A Review and A Proposed System on Bangladesh’s Perspective

Md. Jalal Uddin Chowdhury, Zumana Islam Mou, Rezwana Afrin and Shafkat Kibria
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Zumana Islam Mou: Department of CSE, Leading University, Sylhet, Bangladesh.
Rezwana Afrin: Department of CSE, Leading University, Sylhet, Bangladesh.
Shafkat Kibria: Department of CSE, Leading University, Sylhet, Bangladesh.

International Journal of Science and Business, 2023, vol. 28, issue 1, 193-204

Abstract: A very crucial part of Bangladeshi people’s employment, GDP contribution, and mainly livelihood is agriculture. It plays a vital role in decreasing poverty and ensuring food security. Plant diseases are a serious stumbling block in agricultural production in Bangladesh. At times, humans can’t detect the disease from an infected leaf with the naked eye. Using inorganic chemicals or pesticides in plants when it’s too late leads in vain most of the time, deposing all the previous labor. The deep-learning technique of leaf-based image classification, which has shown impressive results, can make the work of recognizing and classifying all diseases trouble-less and more precise. In this paper, we’ve mainly proposed a better model for the detection of leaf diseases. Our proposed paper includes the collection of data on three different kinds of crops: bell peppers, tomatoes, and potatoes. For training and testing the proposed CNN model, the plant leaf disease dataset collected from Kaggle, is used which has 17430 images. The images are labeled with 14 separate classes of damage. The developed CNN model performs efficiently and could successfully detect and classify the tested diseases. The proposed CNN model may have great potency in crop disease management.

Keywords: Disease Detection; Agriculture of Bangladesh; Classification; Proposed System; Convolutional Neural Network (search for similar items in EconPapers)
Date: 2023
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