Potato Disease Detection Using Deep Learning

Abstract

The \"Potato Leaf Disease Detection\" project aims to solve the problem of accurately identifying and diagnosing diseases in potato plants, specifically focusing on late blight and early blight. Late blight is caused by the microorganism Phytophthora infestans, and early blight is caused by the fungus Alternaria solani,. These diseases can significantly impact potato yields, resulting in substantial economic losses for farmers. By developing a mobile application, we provide farmers with a tool to quickly and accurately diagnose these diseases, enabling timely and appropriate interventions to mitigate crop damage. The core functionality of the application involves capturing images of potato plant leaves and analyzing them to predict the presence of early blight or late blight. The application uses advanced machine learning (ML) techniques, mainly Convolutional Neural Networks (CNNs), to achieve high accuracy in disease detection. Users simply need to take a picture of a potato leaf, and the application will provide a diagnosis with confidence percentage, enabling farmers to make informed decisions about disease management. This project not only aims to provide immediate benefits to farmers by identifying specific diseases but also sets the foundation for future enhancements. By expanding the application to detect a wider range of plant diseases and incorporating additional features such as pest management tips, weather forecasts, and soil health analysis, the project envisions creating a comprehensive farming support tool. The ultimate goal is to improve crop productivity, reduce losses, and enhance the economic well-being of farmers through the effective use of technology.

Introduction

Potato, a crucial global crop, suffers significant yield losses due to diseases like late blight and early blight. Early and accurate disease detection is vital to reduce economic impact, but traditional methods are slow, costly, and require expert knowledge, making them impractical for many farmers, especially in remote areas. This research develops an intelligent, automated potato leaf disease detection system using deep learning and image segmentation. The system leverages Convolutional Neural Networks (CNNs) trained on a Plant Village dataset, achieving over 99% accuracy. Integrated into a user-friendly web application, it enables farmers without technical expertise to diagnose diseases early and accurately.

Bangladesh, a top potato producer, faces challenges with diseases affecting production and export. Effective disease management through AI-powered detection can improve yield and sustainability.

The literature review highlights various machine learning and image processing techniques previously used for potato disease detection, including light wavelength analysis, segmentation, transfer learning, and UAV-based data collection. These studies confirm the effectiveness of CNNs and transfer learning in improving disease classification accuracy.

The proposed approach involves preprocessing (resizing, noise filtering, color normalization), segmentation to isolate leaf regions and diseased spots, feature extraction (color, texture, structure), and CNN model training for classification into healthy or diseased categories (early blight, late blight, bacterial wilt). The model undergoes rigorous testing and validation, ensuring reliable performance. Final results can be displayed via web or mobile applications, supporting timely decision-making by farmers.

Conclusion

This project focuses on detecting potato diseases using CNN, as it has proven to be the most effective method, achieving 99% validation accuracy. With a large dataset, we worked hard to ensure accuracy and believe this project can greatly benefit the agricultural sector. Many farmers in Bangladeshi villages are unaware of disease detection methods, leading to crop destruction by insects and significant losses. Our work aims to change this by providing a simple and accessible solution. In the future, we plan to develop an Android app to detect diseases in various crops and offer proper solutions. Expanding our dataset will further enhance accuracy. By creating this system, farmers will receive instant advice and take quick action to protect their crops, improving agricultural productivity and reducing losses.

References

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Copyright

Copyright © 2025 Prof. SR Bhandari , Miss. Devyani Patil, Miss. Nikita Choudhari, Mr. Avdhut Ambre. 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.