Rice Leaf Disease Detection Using CNN

• Python
• Machine Learning

Rice is one of the most important food crops worldwide, and its yield is often threatened by various diseases. Early detection and accurate diagnosis of these diseases are crucial to ensure optimal crop management and reduce yield losses. Rice leaf diseases, caused by bacteria, viruses, or fungi, have a significant negative impact on rice production. To meet global rice demand, it is crucial to accurately recognize these diseases. However, current recognition methods are limited by image backgrounds and capture conditions. Convolutional Neural Network (CNN) models are a popular research topic for rice leaf disease recognition. However, existing CNN models often suffer from low recognition rates when tested on independent datasets and struggle with learning large-scale network parameters. Recent advancements in Deep Learning have demonstrated the potential benefits of CNN models in Automatic Image Recognition systems. CNN classification algorithms are primarily used for analyzing 2D and 3D images and offer numerous built-in functions and algorithms. In this case, a dataset from Kaggle is utilized, consisting of four categories representing specific rice leaf diseases: Brown Spot, Leaf blight, Leaf blast, and Healthy. In this project, we propose a novel framework for rice plant disease prediction using CNN and transfer learning. The proposed method is based on a pre-trained CNN model, which is fine-tuned using a dataset of rice plant images with different disease symptoms. The performance of the proposed method is evaluated using various evaluation metrics, and the results demonstrate that it can accurately detect and classify rice plant diseases with high accuracy. The proposed method has the potential to serve as an effective tool for automated disease detection and diagnosis in rice crops, enabling farmers to take prompt action to prevent the spread of diseases and minimize yield losses.