Computer-Vision-Based Detection and Monitoring System for Mature Coconut Fruits with a Web Dashboard Visualization Platform

摘要

The Philippines is the second largest producer of coconut products in the world with 347 million trees planted in 3.6 million hectares of land across the country. Traditionally, harvesting coconuts is a labor-intensive process in the Philippines that involves manual climbing and chopping fruits, which carries a high risk of harm or even death. Hence, the number of expert coconut climbers has decreased as a result. In response, current research has concentrated on creating robot harvesters. However, classifying the mature coconut fruit is a major problem in the harvesting process that calls for a great deal of experience, patience, and work. Studies employing Convolutional Neural Networks (CNNs) have shown great accuracy in detecting coconut ripeness, although these efforts have been limited to detection without practical integration with harvesting equipment. Moreover, the present research lacks a comprehensive solution that allows real-time data display and monitoring, such as the maturation stage of coconuts, via a web-based dashboard. This discrepancy emphasizes the requirement for systems that can not only identify the age of coconuts but also work with harvesting technologies and provide intuitive user interfaces for data display and decision-making. In order to fill these gaps, this study presents a computer-vision-based system that monitors and detects coconut fruit maturity, with an emphasis on mature coconuts, by utilizing the YOLOv8 model. With a Mean Average Precision (mAP50) of 99.5%, mAP50-95 of 89.5%, precision of 99.5%, and recall of 99.9%, the system demonstrated great accuracy. A web-based dashboard is also integrated into the system to provide monitoring and visualization of detected coconut fruits, along with notifications for fully ripe fruits.