Markerless feature-registration-based image processing framework for oil palm fresh fruit bunch robotic harvester

Malaysia's oil palm industry faces many challenges in sustaining manual oil palm harvesting operations. This work investigates an effective oil palm Fresh Fruit Bunch (FFB) image processing method for robot harvesting automation. This research explores the proposed image processing method that first detects the Fresh Fruit Bunch (FFB) category which involves 6 different categories of FFB growth stages and then detects its 6D pose estimation for harvesting. Next, this research proposes a novel image processing framework that utilises the convolutional neural network deep learning classification and is followed by markerless feature-registration-based oil palm FFB for 6D pose estimation with the public FFB dataset. Furthermore, this work introduced view obstruction to the public FFB dataset as noise for practical robot harvester applications in plantation field operation. Moreover, the experiment results show the proposed model can maintain a high F1 score performance up until 70% of view obstruction before the F1 score performance is reduced.