Palm Oil Counter: State-of-the-Art Deep Learning Models for Detection and Counting in Plantations

Abstract

Traditional palm oil production methods for evaluating fruit bunches (FFBs) are inefficient, costly, and have limited coverage. This study evaluates the performance of various YOLO models and other state-of-the-art object detection models using a novel dataset of oil palm fresh fruit bunches in plantations, captured in the plantation regions of Central Kalimantan Province, Indonesia. The dataset includes five ripeness classes (abnormal, ripe, underripe, unripe, and flower) and presents challenges such as partially visible objects, low contrast scenes, occluded and small objects, and blurry images. The proposed YOLOv8s Depthwise model was compared with other YOLO models, including YOLOv6s, YOLOv6l, YOLOv7 Tiny, YOLOv7l, YOLOv8s, and YOLOv8l. YOLOv8s Depthwise demonstrated a balanced performance, with a compact size (10.6 MB), fast inference time (0.027 seconds), and strong detection accuracy (mAP50 at 0.75, mAP50-95 at 0.481). Its rapid convergence and low training loss highlighted its efficiency, completing training in the shortest time of 2 hours, 18 minutes, and 30 seconds. Furthermore, it achieved low Mean Absolute Error (MAE) of 0.164 and Root Mean Square Error (RMSE) of 0.4, indicating precise counting capability. Hyperparameter tuning revealed that the YOLOv8s Depthwise model achieved optimal performance using the SGD optimizer with a batch size of 16 and a learning rate of 0.001, showing the best balance between accuracy and training efficiency. Data augmentation positively impacted model performance, resulting in improved performance metrics across various models. When evaluated against other state-of-the-art models on the same dataset, including Faster RCNN, SSD MobileNetV2, YOLOv4, and YOLOv9, YOLOv8s Depthwise surpassed other state-of-the-art models, including Faster R-CNN, SSD MobileNetV2, YOLOv4, and EfficientDet-D0 from previous research, in terms of speed, accuracy, and efficiency, making it ideal for real-time palm oil harvesting applications.

Keywords

• Deep learning
• object counting
• palm oil ripeness
• real-time object detection
• YOLO