Oil Palm Loose Fruit Detection Using YOLOv4 for an Autonomous Mobile Robot Collector

Abstract

This study researches the usage of YOLOv4 for real-time loose fruit detection in oil palm plantations as the first step in implementing automation in the collection of loose fruits. Our system leverages high-resolution video data (4K and 1080p) from various plantation settings. To address the challenges of detecting small and numerous loose fruits, we introduced an image preprocessing technique called 'image tiling' into the vision system workflow. We studied the effects this has on the performance of the detection model. This involves slicing the image into smaller sections (i.e., tiles) for individual processing by YOLOv4 and YOLOv4-tiny models, enhancing detection accuracy. Refined models (YOLOv4-tiling and YOLOv4-tiny-tiling) are then evaluated. YOLOv4 achieved the highest precision (97%) and F1-score (86.3%), while YOLOv4-tiling offered a slight improvement in recall (80.8%). Notably, YOLOv4-tiny, initially underperforming (precision: 37.2%, recall: 20.9%, F1-score: 25%), showed significant improvement with tiling (precision: 90.5%, recall: 67.1%, F1-score: 73.8%). Also, replacing the SPP layer in YOLOv4 with SPP-Fast resulted in increased precision (92.6%) and a significantly improved F1 score of 91.4%. This vision system was then integrated with a custom-designed Loose Fruit Collector Robot through the Robot Operating System (ROS).