Study on citrus fruit image using fisher linear discriminant analysis

In automatic fruit harvesting system, the method of fruit identification by using machine vision has been researched underway for years. The ultimate objective of this project is to extend a ripeness study on the citrus fruit image data and the identification methodologies by multispectral analysis for fruit picking robot. To acquire the combination of the citrus fruit image data, a cold mirror acquisition system has been prototyped to align two CCD cameras with a classical cold mirror on a custom built fixture. The use of the cold mirror system is an attempt to capture both images without registration at the same view by triggering and synchronizing two cameras. With flexible interchangeability, some physical optical filters have been interchanged on the cameras to capture the combination of the citrus image data. In this part of study, Fisher linear discriminate analysis has been used on the natural citrus image data to discuss the probability of the identification on the image by modifying the image data. In the process, the component of the visible image is selected as the dominating component based on the spectral contrast between the ripe colored citrus fruit and the background. The second component from certain near infrared spectral area is selectable to be combined with the visible component by convolution. In FLDA, the major eigenvector is found as the projection direction uniquely from the data sets of the fruit and the background. On top of the information from FLDA, the classification on the fruit set and the background set is performed by the nearest neighbor estimation in the lower dimensional space on different schemes of the image data. By comparing with some other color indices methods, the overall outcome by FLDA gives a better identification result on all sampling image data with small estimation error. Refereed/Peer-reviewed