Model for Effective Rice Disease Recognition Based on Deep Learning Techniques

Abstract

Iraq’s primary crop, crucial for both domestic consumption and exports, is rice. The prevalence of rice infections poses a significant challenge to farmers, impacting crop yield and resulting in substantial losses. Human identification of diseases relies on expertise, making early diagnosis crucial for sustaining rice plant health. To address the limited number of rice leaf images in the database, our approach incorporates augmentation and dilation rate. Integrating drone technology and machine learning algorithms offers a promising solution to efficiently diagnose rice leaf diseases. However, existing methods face challenges such as picture backgrounds, insufficient field image data, and symptom variations. This work introduces a robust methodology, leveraging a specialized Convolutional Neural Network (CNN) model for rice leaf photos, effectively enhancing disease classification accuracy. The proposed approach successfully identifies and diagnoses three distinct classes: leaf smut, brown spot, and bacterial leaf blight.