Plant leaf Disease Detection Using Convolutional Neural Network

Abstract

Agriculture is the backbone of civilization as well as It has equal importance like the technological growth. Agriculture is one of the world\'s most important sectors, providing food, raw materials, and economic benefits. Agriculture field has a high impact on our life. Agriculture is the most important sector of our Economy. Proper management leads to a profit in agricultural products. Farmers do not expertise in leaf disease so they produce less production. Plant leaf diseases detection is the important because profit and loss are depends on production. CNN is the solution for leaf disease detection and classification. Main aim of this research is to detect the apple, grape, corn, potato and tomato plants leaf diseases. Plant leaf diseases are monitoring of large fields of crops disease detection, and thus automatically detected the some feature of diseases as per that provide medical treatment. Proposed Deep CNN model has been compared with popular transfer learning. Plant leaf disease detection has wide range of applications available in various fields such as Biological Research and in Agriculture Institute. Plant leaf disease detection is the one of the required research topic as it may prove benefits in monitoring large fields of crops, and thus automatically detect the symptoms of diseases as soon as they appear on plant leaves.

Introduction

Plant disease detection applications, powered by AI and deep learning (especially Convolutional Neural Networks, CNNs), are transforming agriculture by enabling timely, accurate, and scalable monitoring of crop health. These technologies help reduce losses, minimize chemical use, and improve food security globally. Green space management also benefits from such disease detection to sustain urban environments.

The study focuses on detecting diseases in leaves of apple, grape, corn, potato, and tomato plants using deep learning on large datasets (over 70,000 images) categorized by specific disease types. CNNs are highlighted as effective for learning complex visual patterns in leaf disease identification.

A literature survey reviews various systems using CNNs and other machine learning models (SVM, KNN), showing CNNs generally achieve higher accuracy (up to 96%) in classifying leaf diseases.

The proposed system involves preprocessing images (resizing, normalization, augmentation), followed by training a CNN with convolutional, pooling, and fully connected layers. The model is optimized using Adam, with strategies to avoid overfitting. Evaluation uses metrics like accuracy, precision, recall, and F1-score.

Conclusion

We have studied about existing system feature based approach. It’s done by image processing technique in this we have studied steps like image Acquisition, image pre- processing, Image Segmentation, features extraction, classification.Proposed system to achieve this purpose, we have use CNN and get accuracy is 97.23%. In future we can add more classes of leavesand disease type and provide remedy for the disease that is detected.

References

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Copyright

Copyright © 2025 Priti Patel, Nensi Panchal. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.