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

• URL: http://arxiv.org/abs/2501.03305v1
• Date: Mon, 06 Jan 2025 17:43:59 GMT
• Title: Plant Leaf Disease Detection and Classification Using Deep Learning: A Review and A Proposed System on Bangladesh's Perspective
• Authors: Md. Jalal Uddin Chowdhury, Zumana Islam Mou, Rezwana Afrin, Shafkat Kibria,
• Abstract summary: Plant diseases are a serious stumbling block in agricultural production in Bangladesh. In this paper, we've mainly proposed a better model for the detection of leaf diseases. The proposed CNN model performs efficiently and could successfully detect and classify the tested diseases.
• Score: 0.0
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• 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 17,430 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.

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