Computer Vision Algorithm Development for Classification of Palm Fruit Ripeness

摘要

This paper presents a novel computer vision algorithm that focus on detecting the ripeness of palm fruit and the developed computer vision algorithm is implemented into a low cost processor that can be integrated into a portable standalone device. The computer vision algorithm mainly consists of two major functions, which are segmenting out the section that consists of tree from the image and classifying the ripeness of palm fruit after locating the fresh fruit bunch (FFB) on the palm tree. A sliding window method is used to separate the image of a palm oil plantation into various sections. By retraining the Convolutional Neural Network (CNN), which is AlexNet, using fully labelled dataset, it is capable to identify the existence of palm tree in each segmented section. For the ripeness detection of palm fruit, it is achieved by analyzing dataset that consists of 100 palm fruit images from different category of ripeness. Those images are analyzed in Hue, Saturation and Value (HSV) color spaces. Palm fruit that belongs to the ripe category has a unique range of value that is not found in other categories. Therefore, the algorithm to classify the ripeness of palm fruit is developed accordingly. The novel computer vision algorithm is then converted to Python programming language which is compatible to run in Tinker Board. Tinker Board is one of the Single Board Computer (SBC) that consists of Graphical Processing Unit (GPU) that is vital in digital image processing field. A high definition camera is equipped with the Tinker Board to capture the image of palm oil plantation and palm fruit. The integrated device that consists of Tinker Board and camera provides mobility to end-user to classify the ripeness of palm fruit in the palm oil plantation. The proposed algorithm successfully yielded an accuracy of 85% as there were a total of 85 images which were correctly classified out of 100 images of palm fruit.