Video based oil palm ripeness detection model using deep learning

Research on oil palm detection has been carried out for years, but there are only a few research that have conducted research using video datasets and only focus on development using non-sequential image. The use of the video dataset aims to adjust to the detection conditions carried out in real time so that it can automatically harvest directly from oil palm trees to increase efficiency in harvesting. To solve this problem, in this research, we develop an object detection model using a video dataset in training and testing. We used the 3 series YOLOv4 architecture to develop the model using video. Model development is done by means of hyperparameter tuning and frozen layer with data augmentation consisting of photometric and geometric augmentation experiment. To validate the outcomes of the YOLOv4 model development, a comparison of SSD-MobileNetV2 FPN and EfficientDet-D0 was performed. The results obtained show that YOLOv4-Tiny 3L is the most suitable architecture for use in real time object detection conditions with an mAP of 90.56% for single class category detection and 70.21% for multi class category detection with a detection speed of almost 4× faster than YOLOv4-CSPDarknet53, 5× faster than SSD-MobileNetV2 FPN, and 9× faster than EfficientDet-D0.