Authors - Sneha Visveswaran, Tanmay Praveen, Vidula Gurudutta, Yamini Sridhar, Chaithra T S5 Abstract - Arecanut crop management has traditionally depended on manual inspection for disease identification and harvest readiness assessment, a method that is both time-consuming and susceptible to human error. This study introduces an automated, image-based system designed to address two primary tasks: disease classification and ripeness assessment. The proposed pipeline initiates with data preparation, including resizing, normalization, and augmentation of arecanut images to enhance model robustness. A convolutional neural network architecture, incorporating additional feature extraction and optimization layers, is utilized to detect disease symptoms. A comparable deep-learning model is trained to classify ripeness stages based on visual characteristics. Model performance is evaluated using accuracy, precision, recall, and F1-score metrics to ensure reliability. The system is implemented via a user-friendly web interface, which allows real-time image uploads and immediate predictions, thereby facilitating practical application for farmers and agricultural stakeholders. This integrated solution provides a scalable and cost-effective approach to improving crop monitoring and supporting data-driven decision-making in arecanut cultivation.
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