Intelligent Model to Detect Rice Leaves Diseases Using Transfer Learning

Abstract

Crop diseases can have a significant impact on global agriculture, reducing yields and causing financial losses. Early identification and monitoring of these diseases is crucial for protecting human health and maintaining food security.  However, detecting diseases in crops, particularly rice, can be a challenging task. Traditional methods for disease detection can be time-consuming and may not provide accurate results. In this study, we propose an intelligent system for the detection of rice leaves diseases using transfer learning. With the advancement of machine learning techniques, it is now possible to develop models that can accurately identify disease-affected rice leaves. We used transfer learning to fine-tune a pre-trained model on a dataset of images of rice leaves with different diseases and healthy leaves. The results of our study demonstrate that our proposed model can accurately detect rice leaf diseases with a 94.19% of accuracy and can be a useful tool for early disease identification and management in agriculture.