Autonomous navigation system with weight detection for autonomous loose fruit collector.

The oil palm industry heavily relies on foreign labour for harvesting, including the collection of fresh fruit bunches (FFB) and loose fruits (LF). Manual LF collection, prone to inefficiencies and worker injuries, prompted the development of an automated LF collector. This project introduces a comprehensive system, featuring an LF picker with a robot arm, an LF detector using image processing, a GPS-based human-follower vehicle, a back-to-home navigation system with weight detection, and an obstacle avoidance system. The automated LF collector aims to reduce workforce dependency, enhance LF collection productivity, and prevent worker injuries. The study discusses the motivation, challenges, and objectives of developing the system, emphasizing potential economic and societal benefits. Furthermore, the implementation of advanced image processing techniques, such as the Faster Objects More Objects (FOMO) neural network, is detailed for efficient LF detection. The section on Back-to-Home Navigation with Weight Detection introduces the concept of an autonomous collector robot vehicle, highlighting the importance of sensors and weight detection for navigation and load management. The integration of these technologies promises to revolutionize loose fruit collection in oil palm plantations, reducing labour dependence, increasing productivity, and mitigating economic losses. [ABSTRACT FROM AUTHOR]