Efficient Diagnosis of Rice Crop Disease Using Machine Learning
Year: 2025
Type: article
Abstract: Rice is a key staple crop, and productivity is highly impacted by numerous diseases. Conventional disease diagnosisapproaches are based on manual examination, which is cumbersome and subject to errors. The present work putsforward a framework for rice disease diagnosis using a machine learning technique based on convolutional NeuralNetworks (CNNs). The model was trained on a database of images of diseased and healthy rice leaves and reached84.8% classification accuracy. The system is developed to deployed as a web application to aid farmers in the earlydetection of disease and prescriptive treatment, hence enhancing crop yield. The use of cutting-edge technologies likemachine learning(ml) and deep learning (Dl) is capable of addressing these challenges through the early detection ofplant diseases.
Authors: Dr. VENKAT KRISHNA, D. ANURADHA, C. NISHITHA, A.SRI VARSHA, K. ANUSHA
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: hybrid
OpenAlex ID: W6911983127
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