Fresh Fruit Bunch (FFB) is the main ingredient in palm oil production. Harvesting FFB from oil palm trees at its peak ripeness stage is crucial to maximise the oil extraction rate (OER) and quality. In current harvesting practices, misclassification of FFB ripeness can occur due to human error, resulting in OER loss. Therefore, a vision-based ripe FFB detection system is proposed as the first step in a robotic FFB harvesting system. In this work, live camera input is fed into a Convolutional Neural Network (CNN) model known as YOLOv4 to detect the presence of ripe FFBs on the oil palm trees in real-time. Once a ripe FFB is detected on the tree, a signal is transmitted via ROS to the robotic harvesting mechanism. To train the YOLOv4 model, a large number of ripe FFB images were collected using an Intel Realsense Camera D435 with a resolution of 1920×10801920\times 1080 . During data acquisition, a subject matter expert assisted in classifying the FFBs in terms of ripe or unripe. During the testing phase, the result of the mean Average Precision (mAP) and recall are 87.9 % and 82 % as the detection fulfilled the Intersect over Union (IoU) with more than 0.5 after 2000 iterations and the system operated at the real-time speed of roughly 21 Frame Per Second (FPS).

中文翻译：

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基于YOLOv4的成熟油棕鲜果串实时检测


新鲜果束（FFB）是棕榈油生产的主要成分。在油棕树成熟度最高的阶段收获鲜果串对于最大限度地提高油提取率 (OER) 和质量至关重要。在当前的采收实践中，由于人为错误，可能会发生鲜果串成熟度的错误分类，从而导致 OER 损失。因此，基于视觉的成熟FFB检测系统被提议作为机器人FFB收获系统的第一步。在这项工作中，实时摄像头输入被输入到称为 YOLOv4 的卷积神经网络 (CNN) 模型中，以实时检测油棕榈树上是否存在成熟的 FFB。一旦在树上检测到成熟的鲜果束，信号就会通过 ROS 传输到机器人收割机构。为了训练YOLOv4模型，使用分辨率为1920×10801920×1080的英特尔实感摄像头D435收集了大量成熟的FFB图像。在数据采集过程中，主题专家协助对新鲜果串进行成熟或未成熟的分类。测试阶段，平均精度（mAP）和召回率结果分别为 87.9% 和 82%，检测满足 2000 次迭代后超过 0.5 的交并交（IoU），且系统实时运行速度约为每秒 21 帧 (FPS)。