MultiCrop Leaf Disease Detection System Using Machine Learning and Deep Learning Techniques

Abstract

The Multi-Crop Leaf Disease Detection System leverages deep learning and advanced image processing techniques to accurately identify and classify plant diseases. Traditional disease detection methods are labor-intensive, time-consuming, and often require expert intervention, making large-scale crop monitoring difficult. This research proposes an automated system that utilizes machine learning and deep learning models such as Convolutional Neural Networks (CNN), ResNet50, VGG, Artificial Neural Networks (ANN), K-Nearest Neighbors (KNN), and Support Vector Machines (SVM) to enhance the efficiency and accuracy of plant disease detection.The proposed system employs image preprocessing and segmentation techniques to analyze high-resolution leaf images and identify early symptoms of plant diseases. By extracting critical features such as color, texture, and shape, the system classifies different types of plant diseases with improved accuracy. Early detection enables farmers to take timely preventive actions, thereby reducing crop damage and minimizing the excessive use of pesticides.Furthermore, the automated classification approach ensures objective and consistent diagnosis compared to traditional manual inspections. The system focuses on feature extraction, image processing, and classification techniques to provide reliable disease prediction. This technology not only helps in identifying plant diseases but also assists in detecting nutrient deficiencies and pest infestations at an early stage.Overall, the proposed system contributes to improving crop health, enhancing agricultural productivity, and supporting sustainable farming practices. By enabling early disease detection and timely intervention, it helps farmers reduce crop losses and promotes food security.

Introduction

The text focuses on the challenge of plant diseases in agriculture, which reduce crop yield and cause economic losses. Traditional disease detection methods rely on manual inspection, which is time-consuming, requires expert knowledge, and is often inaccurate, especially in rural areas.

To address these issues, the proposed system uses artificial intelligence, machine learning, and computer vision to automatically detect plant diseases from leaf images. It analyzes visual symptoms such as spots and discoloration using image processing and classification techniques.

The system follows a structured methodology including image acquisition, preprocessing, segmentation, feature extraction, and disease classification using algorithms like SVM, KNN, ANN, and advanced deep learning models such as CNN, VGG, and ResNet50.

Results show that deep learning models, particularly CNN and ResNet50, provide higher accuracy and better performance compared to traditional methods. The system can accurately identify healthy and diseased leaves and provide treatment suggestions through a user-friendly interface.

Conclusion

In this study, a Multi-Crop Leaf Disease Detection System was developed using image processing, machine learning, and deep learning techniques to identify plant diseases from leaf images. The proposed system aims to assist farmers and agricultural experts in detecting plant diseases at an early stage and taking timely preventive measures. Traditional disease detection methods rely heavily on manual inspection, which is time-consuming, labor-intensive, and dependent on expert knowledge. The proposed automated system addresses these challenges by providing a fast, reliable, and accurate solution for plant disease identification. The system uses several machine learning and deep learning models, including CNN, ResNet50, VGG, ANN, SVM, and KNN, to classify plant diseases based on leaf image features such as color, texture, and shape. Experimental results show that deep learning models, particularly CNN and ResNet50, achieve higher accuracy compared to traditional machine learning algorithms. These models effectively learn complex image patterns and improve the overall performance of the disease detection system. The developed system also provides a user-friendly interface that allows users to upload leaf images and obtain disease predictions along with recommended treatments. This feature helps farmers make informed decisions regarding crop management and disease prevention. By enabling early disease detection, the system helps reduce crop losses, minimize excessive pesticide usage, and promote sustainable agricultural practices. Overall, the proposed Multi-Crop Leaf Disease Detection System contributes to improving crop health and agricultural productivity. The integration of artificial intelligence in agriculture can significantly enhance precision farming and support farmers in managing plant diseases more efficiently. In the future, the system can be further improved by incorporating larger datasets, real-time field monitoring, and mobile or IoT-based applications to provide more accessible and scalable solutions for smart agriculture.

References

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Copyright

Copyright © 2026 D. Dileep , B. Sanjay Kumar , B. Vamshi , Dr. BNV Madhu Babu . 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.