Real-Time Leaf Disease Detection and Fertilizer Recommendation System

A. V. Nageswara Rao, Pranathi Avutu, Sai Preethi Bijjala, Peddi Lakshmi Aishwarya

2025

Abstract

This project highlights the importance of leaf disease detection in precision agriculture, enabling early identification and timely intervention to protect crops from various diseases. This project presents a novel approach to detecting leaf diseases using deep learning (DL) and machine learning (ML) techniques. The study employs four powerful convolutional neural network (CNN) architectures: VGG16, VGG19, Inception v3, and Inception v6 to train a comprehensive leaf image data set, enabling robust disease classification. VGG-based models are used to extract features, which are then input into a support vector machine (SVM) classifier for disease classification. This hybrid DL-ML framework improves both the accuracy and efficiency of the system in distinguishing between healthy and diseased leaves. An interactive interface was developed, allowing users to upload leaf images for real-time disease detection, while an IoT camera system was integrated for automated leaf disease analysis in the field. The proposed solution demonstrates significant potential to improve crop management practices and advance automated agricultural systems, offering an innovative tool for early-stage disease diagnosis and management.

Paper Citation

in Harvard Style

Rao A., Avutu P., Bijjala S. and Aishwarya P. (2025). Real-Time Leaf Disease Detection and Fertilizer Recommendation System. In Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25; ISBN 978-989-758-777-1, SciTePress, pages 862-874. DOI: 10.5220/0013982100004919

in Bibtex Style

@conference{icrdicct`2525,
author={A. Rao and Pranathi Avutu and Sai Bijjala and Peddi Aishwarya},
title={Real-Time Leaf Disease Detection and Fertilizer Recommendation System},
booktitle={Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25},
year={2025},
pages={862-874},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013982100004919},
isbn={978-989-758-777-1},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Research and Development in Information, Communication, and Computing Technologies - ICRDICCT`25
TI - Real-Time Leaf Disease Detection and Fertilizer Recommendation System
SN - 978-989-758-777-1
AU - Rao A.
AU - Avutu P.
AU - Bijjala S.
AU - Aishwarya P.
PY - 2025
SP - 862
EP - 874
DO - 10.5220/0013982100004919
PB - SciTePress