A real-time computer vision-based autonomous bitter gourd robot harvester

Bitter gourd (Momordica charantia) is an essential ingredient used across diverse industries, valued not only for culinary purposes but also recognized for its various health and medical benefits. This vine is known for its unique bitter taste. However, given the manual labor required to determine when the gourds are mature and to carefully harvest them, subjectivity and inconsistency are also inherent in the existing practice of relying on human judgment to determine the optimal time for harvest. This study suggests developing an autonomous, computer vision-based bitter gourd harvesting robot to close the gap in farming methods. The objective of this study is to build a computer vision-based autonomous bitter gourd robot harvester, specifically to implement real-time maturity detection utilizing the bitter gourd's physical characteristics such as size, color, and pattern, to integrate robotic arms that facilitate precise cutting and dropping of matured bitter gourds onto a basket, and to use criteria to assess the accuracy and reliability of the computer vision-based maturity detection system. This study follows the concept of a 3D printer that normally has X, Y, and Z axes, together with machine learning, that is object detection which is trained using the YOLOv8 nano model. It passed the manual testing and integration testing wherein overall; it resulted in an 82 percent success rate in deployment testing. The study achieved its objectives with the current approach of implementation of the researchers. However, it is recommended to consider alternative methods in testing and other approaches in doing the study.