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

2025-01-06

International journal of science and business

AI Summary

To address delayed plant leaf disease identification in Bangladeshi agriculture—causing yield loss and food security risks—this work proposes a lightweight, crop-adapted CNN model tailored to three locally prevalent crops (green chili, tomato, potato) and their 14 common diseases. Methodologically, we integrate the Kaggle Multi-Crop Disease dataset (17,430 annotated images), domain-specific data augmentation, transfer learning, and end-to-end fine-tuning, while optimizing architectural parameters (e.g., depth, channel count) for efficient edge deployment. Experimental results demonstrate superior cross-crop disease classification accuracy compared to generic baseline models (e.g., MobileNetV2, EfficientNet-B0). To our knowledge, this is the first lightweight, deployable intelligent disease recognition system explicitly designed for Bangladeshi agricultural conditions. It delivers real-time, low-cost, high-availability plant protection support for resource-constrained settings, bridging the gap between AI research and on-farm applicability.

Technology Category

• Machine Learning: Transfer, Domain Adaptation, Multi-Task Learning
• Computer Vision: Learning & Optimization for CV
• Application Domains: Internet of Things, Sensor Networks & Smart Cities

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search

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.

Problem

• Agricultural Disease Identification
• Crop Damage
• Food Security

Innovation

• Advanced Plant Disease Identification Model
• Bangladesh Agricultural Context
• Efficient Crop Disease Management