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

TL;DR

This paper reviews deep learning techniques for plant leaf disease detection and proposes a CNN-based system trained on a diverse dataset, demonstrating effective classification for crops like bell peppers, tomatoes, and potatoes to aid agricultural management in Bangladesh.

Contribution

It introduces a new CNN model trained on a comprehensive dataset for accurate leaf disease detection in Bangladesh's key crops, enhancing disease management strategies.

Findings

The CNN model achieved high accuracy in classifying 14 disease classes.

The dataset included 17,430 images across three crop types.

The proposed system shows potential for practical agricultural disease management.

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.