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@article{206792,
        author = {Prajwal G and Holeppa Shekki and Rakshith E J and Yajnesh and Prof Sathish Kumar K},
        title = {Peeled And Raw Arecanut Segregation and Sorting Using Machine Learning Techniques},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {13},
        number = {no},
        pages = {453-457},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=206792},
        abstract = {This paper presents an automated arecanut segregation and sorting system using machine learning and image processing techniques. Traditional manual grading methods are time-consuming and prone to human error. The proposed system captures arecanut images using a camera and classifies them into Good, Medium, and Bad categories using machine learning algorithms such as Random Forest, Support Vector Machine (SVM), and Decision Tree. Image preprocessing and feature extraction are performed using OpenCV to improve classification accuracy. The classified output is integrated with Arduino-based hardware and servo motors for automated sorting. Experimental results show that the Random Forest model achieved the highest accuracy of 92.86%, demonstrating reliable and efficient performance. The system reduces manual effort, improves consistency, and provides a cost-effective solution for smart agricultural automation.},
        keywords = {Machine Learning, Arecanut Classification, Random Forest, SVM, Image Processing, Arduino, Automation.},
        month = {July},
        }