Automated Crop Disease Detection and Fertilizer Recommendation System

Aashish Sugadev, T. Yuvaprasath, J. P. Hemanaath, T. Shynu, Mykhailo Paslavskyi Department of Computer Science Engineering in AIML, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, India. Department of Research and Development, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India. Department of Computer Science, Ukrainian National Forestry University, Lviv, Lviv Oblast, Ukraine.

Abstract: This paper focuses on developing a web-based system for automated crop disease detection using image processing and deep learning techniques. The main goal is to help farmers and users quickly and easily identify plant diseases by analyzing images of crop leaves. Early detection of diseases can play an important role in improving crop health and increasing agricultural productivity. The system uses a Convolutional Neural Network (CNN) trained on a large dataset of plant leaf images, including both healthy and diseased samples. Users can upload an image via a simple, user-friendly interface, and the system processes it to predict the type of disease present. Along with the prediction, the system also provides a confidence score to indicate the reliability of the result. The application's backend is developed with FastAPI, which efficiently handles the model and prediction processes. The frontend is built with HTML, CSS, and JavaScript to ensure a smooth, interactive user experience. This paper demonstrates how modern machine learning techniques can be applied in agriculture to develop practical, accessible solutions. It also provides a strong foundation for future improvements such as enhanced accuracy, real-time detection, and integration with smart farming systems.

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Keywords: Web-Based System; Image Processing; Deep Learning Techniques; Convolutional Neural Network (CNN); Plant Leaf Images; Confidence Score; Agricultural Productivity; Smart Farming Systems.

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Received on: 29/06/2025Revised on: 04/09/2025Accepted on: 13/11/2025Published on: 07/03/2026

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