Improving Quality of Apple Using Computer Vision&Image Processing Based Grading System

With increased expectations for fruit products of high quality and safety standards, the need for accurate, fast and objective quality determination of these characteristics in fruit products continues to grow. Computer vision provides one alternative for an automated, non-destructive and cost-effective technique to accomplish these requirements. This inspection approach based on image analysis and processing has found a variety of different applications in the fruit industry. Automated inspection of apple quality involves computer recognition of good apples and blemished apples based on geometric or statistical features derived from apple images. This project presents the recent developments of image processing and machine vision system in an automated fruit quality measurement system. In agricultural sector the efficiency and the accurate grading process is very essential to increase the productivity of produce. Everyday high quality fruits are exported to other countries and generate a good income. That is why the grading process of the fruit is important to improve the quality of fruits. However, fruit grading by humans in agricultural industry is not sufficient, requires large number of labors and causes human errors. Automatic grading system not only speeds up the process but also gives accurate results. Therefore, there is a need for an efficient fruits grading or classification methods to be developed. Fruits color, size, weight, component texture, ripeness are important features for accurate classification and sorting of fruits such as oranges, apples, mangoes etc. Objective of this paper is to emphasize on recent work reported on an automatic fruit quality detection system. This project presents the image processing techniques for feature extraction and classification for fruit quality measurement system.