Abstract

The Coconut Maturity Detection Project revolutionizes coconut farming by integrating photo refining, machine learning, and Internet of Things (IoT) technologies. Using image processing techniques like the Gray-Level Co-occurrence Matrix (GLCM) algorithm and classification algorithms, the system accurately distinguishes between immature and mature coconut bunches from images captured by end-users. Precise identification of coconut maturity is crucial for optimizing harvest timing and ensuring product quality. This innovative solution offers a cost-effective and efficient method for coconut farmers and agribusinesses to make informed decisions about harvest planning and improve product quality. By maximizing harvest timing, the work boosts yield while minimizing resource waste, promoting sustainability in coconut farming. Moreover, it encourages prudent resource utilization by enabling data-driven decisions on water and fertilizer usage. With an impressive 90% accuracy rate, the machine learning model provides farmers with valuable insights to plan harvests effectively and negotiate better prices. The Coconut Maturity Detection Project marks a significant step in automating agricultural processes and enhancing the sustainability of coconut farming. Future efforts may focus on real-time implementation and integration with agricultural machinery for on-field use, further enhancing practical applicability and impact. In conclusion, this project showcases the transformative potential of photo refining, machine learning, and IoT technologies in coconut farming, promising increased productivity, sustainability, and economic growth in tropical agricultural economies.