Automated Diagnostic Framework for Identification and Classification of Rice Leaf Diseases
Keywords:
Rice Leaf Disease, Deep Learning, CNN, Precision Agriculture, Food Security, Real-time Diagnosis.Abstract
Abstract
Purpose: Rice is a staple crop essential for global food security; however, disease outbreaks significantly threaten annual yields. This study develops an automated diagnostic framework for the identification and classification of rice leaf diseases using a custom Convolutional Neural Network (CNN).
Methodology: A diverse dataset of rice leaf imagery, comprising both healthy and symptomatic samples, was curated and subjected to rigorous preprocessing to ensure morphological consistency. The CNN architecture was designed to extract high-dimensional features and learn intricate patterns associated with various pathologies. To enhance practical utility, a user-friendly interface was integrated, allowing for real-time diagnostic feedback via image uploads.
Findings: The system demonstrates high accuracy in early-stage disease detection, significantly outperforming traditional visual inspection methods. By automating the diagnostic pipeline, the framework reduces subjectivity and the need for specialized personnel.
Originality: The integration of a continuous learning module ensures that the model evolves with new data, maintaining high precision in diverse environmental conditions. This provides a scalable, cost-effective tool for small-scale farmers to mitigate crop loss and improve agricultural sustainability.
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