Multi-model machine learning for automated identification of rice diseases using leaf image data.

ABSTRACT: Rice, a staple meal for about half of the world's population, is critical to global food security, especially in Asia. However, diseases have a severe impact on rice production, resulting in significant yield losses or outright crop failure. Traditional techniques of identifying rice diseases are time-consuming, labor-intensive, and rely heavily on specialist knowledge. As a result, a rapid, cost-effective, and automated method for detecting rice illnesses is critical for modernizing agricultural techniques and ensuring sustainable food production. This paper presents a novel hybrid deep-learning and machine-learning framework for automatically identifying rice plant diseases from leaf photos. We extracted deep features from rice leaf images using pre-trained CNN models-MobileNetV2, DarkNe

SUBMITTER: Tiwari R 

PROVIDER: S-EPMC12440219 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature