AI BASED ARECANUT PLANT DISEASE CLASSIFICATION SYSTEM

AI BASED ARECANUT PLANT DISEASE CLASSIFICATION SYSTEM

Likhitha M U 1 , Dr. Geetha M 2
[1] Student, Department Of MCA BIET Davangere

[2] Associate Professor, Department Of MCA, BIET, Davangere

ABSTRACT
Arecanut, commonly known as betel nut, is a vital cash crop in many tropical regions, contributing significantly to the agricultural economy. However, like other crops, arecanut plants are susceptible to various diseases that can severely impact yield and quality. Early detection and accurate classification of these diseases are crucial for timely intervention and effective disease management. In this study, we propose an AI-based arecanut plant disease classification system that leverages deep learning techniques to automatically identify and classify different diseases affecting arecanut plants. Convolutional neural networks (CNNs) are employed for feature extraction and disease classification, with transfer learning techniques used to fine-tune pre-trained models on the specific task of arecanut disease recognition.

Keywords: Arecanut, betel nut, plant disease classification, deep learning, transfer learning, agricultural AI, disease management. convolutional neural network (CNN).