International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 13 Issue: 02 | Feb 2026
p-ISSN: 2395-0072
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Rice Leaf Disease Detection and Solution Recommendation System Dr. M. Dhasaratham1 , Vardhan.P2, PavanKumar.S3, Eshwar.K4, Pranay.K5 1Professor, Department of IT, TKR College of Engineering and Technology, Telangana, India 2,3,4,5B.Tech Students, Department of IT, TKR College of Engineering and Technology, Telangana, India
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Abstract - Agriculture plays a major role in India’s
complex patterns automatically. Convolutional Neural Networks (CNNs) have become one of the most widely used deep learning models for plant disease classification due to their ability to learn important features such as color variations, lesion shape, texture patterns, and infected regions. Similar research has been carried out in leaf disease detection for different crops using CNN-based architectures and deep learning strategies. Subbotin et al. demonstrated the effectiveness of CNN models for detecting apple leaf diseases, showing that automated classification improves accuracy compared to traditional approaches [1]. Bairwa et al. highlighted recent improvements in mango leaf disease detection using deep neural networks and emphasized the importance of robust training for handling image variations [2]. Revathi and Hemalatha presented image processingbased approaches for detecting cotton leaf spot disease and proved that automated detection can assist in early-stage identification [3]. Kottath and Bharathi reviewed preprocessing methods in deep learning-based disease prediction and concluded that resizing, normalization, and augmentation significantly enhance model performance [4]. Walia et al. proposed an optimized VGG16 model for cotton leaf disease classification and reported improved classification accuracy using transfer learning techniques [5].
economy, and rice is one of the most important staple food crops. However, rice cultivation is severely affected by leaf diseases such as Brown Spot, Leaf Smut, and Bacterial Leaf Blight, which reduce crop yield and quality. Early and accurate identification of these diseases is essential to prevent largescale crop loss. This paper presents a Rice Leaf Disease Detection and Solution Recommendation System using image processing and deep learning techniques. The proposed system uses a Convolutional Neural Network (CNN) and transfer learning models to classify rice leaf images into healthy or diseased categories. The input images are collected and preprocessed through resizing, normalization, and augmentation to improve model accuracy and robustness. The trained model automatically extracts features such as texture, color, and lesion patterns to predict the disease class. The system is implemented using Python and TensorFlow/Keras and is integrated with a Django-based web application that allows users to upload rice leaf images and receive instant disease predictions along with recommended treatments and preventive measures. This approach reduces dependency on manual inspection, provides fast and accurate diagnosis, and supports farmers in taking timely actions to improve crop productivity and promote sustainable agriculture. KEY WORDS : Rice Leaf Disease Detection, Convolutional Neural Network (CNN), Deep Learning, Transfer Learning, Image Classification
Motivated by these advancements, this project proposes a Rice Leaf Disease Detection and Solution Recommendation System that not only detects rice leaf diseases but also provides recommended treatments and preventive measures. The system is developed using deep learning models and is integrated into a Django-based web application, allowing users to upload leaf images and instantly receive disease predictions. This system reduces dependency on experts, improves decision-making speed, and supports farmers by offering actionable guidance for disease control.
1. INTRODUCTION Agriculture is one of the most important sectors in India, and it plays a major role in supporting the national economy and ensuring food security. Rice is one of the primary staple crops cultivated across India and many other Asian countries. However, rice production is highly affected by various plant diseases that reduce both yield and grain quality. Diseases such as Brown Spot, Leaf Smut, and Bacterial Leaf Blight can damage rice leaves, reduce photosynthesis, and ultimately lead to significant crop loss. Traditionally, rice leaf disease detection is performed manually by farmers or agricultural experts through visual inspection. This manual method is time-consuming, subjective, and often inaccurate, especially when multiple diseases exhibit similar symptoms at early stages.
1.1 Objectives The primary objective of this project is to develop an intelligent and automated rice leaf disease detection system using deep learning. The system aims to analyse rice leaf image datasets and understand the variations in healthy and diseased leaf patterns. It focuses on identifying and recommending suitable deep learning models such as CNN and transfer learning architectures including VGG16 and ResNet based on dataset characteristics. Another objective is to train and evaluate multiple models using standard
In recent years, deep learning and computer vision techniques have shown strong performance in agricultural disease detection by analysing leaf images and extracting
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