Enhancing Rice Plant Disease Diagnosis Using YOLOv8 and VGG19: An Approach for Hybrid Deep Learning Model

Rice is a raw material crop for more than half of the circular populating, making it essential to circular food security measures. The department of agriculture sphere plays a judicial role in ensuring a steady food provide and rice serves as a simple informant of nutriment for jillions. Nonetheless rice cultivation faces operative challenges, especially plant diseases that can drastically scale down both yield and character. The early and hi fi espial of rice plant diseases is of import in innovative department of agriculture. Leveraging late technologies such as crossbred Deep Learning and Image Processing has well—tried to be extremely hard hitting in diagnosing and mitigating these issues. These techniques offer machine driven dead and streamlined disease espial, helping farmers take apropos preemptive measures. Crossbreed deep learning models, in special, have incontestable particular truth in identifying and classifying rice plant diseases. This paper explores the current advancements in rice plant disease espial using crossbreed deep learning techniques highlighting their potency to infect agrarian nosology and ameliorate crop health.