Study On Fast Acquisition And Processing Technology For Computer Vision Information In Apple Automatic Grading

The production of high qualitative and unpolluted farm produce is one of the important developing trends of agriculture in the new century. High quality means high profit in international market. However, the farm produce with different varieties and different quality have cause tremendous losses in economy due to lacking the post- harvest inspecting standards and measures in China, and this problem will become more grave with China entering the WTO. So it is urgent to carry out the researches on agricultural produce quality measurement and chemical deposits detection. The inspection items for fruit external quality involve size. shape. color and surface defects. Although some quality inspection operations such as color, size, and shape are performed by automated system in western countries, the automation of the defect sorting process is still a challenge subject due to the complexity of the problem. In China, the researches for fruit automatic grading based on computer vision were started in 1990. However, these researches were only concentrated on methodology development for static fruit images. Up to now no automatic grading system is put into practical use in fruit packinghouse in China due to the limitation of processing speed. The main objective of this paper is to design and construct a on-line fruit sorting experimental hardware system based on computer vision, and develop the corresponding image processing and recognition algorithms suitable for real-time apple grading operation. Based on this goal, the following research achievements have been fulfilled: (1) Some fast image preprocessing methods suitable for on-line apple automatic grading including image smoothing .. enhancement were determined through investigations and experiments. Moreover, a new edge detection method with higher resistance to noise was developed and a fast segmentation method for on-line apple grading was also proposed. (2) Real-time fruit surface defect inspection and recognition is still a challenging subject due to its complexity. Based on a reference image of apple, a novel defect segmentation algorithm was developed. The method can compensate the reflectance intensity gradient on curved objects and provide flexibility to cope with the variations in brightness and size of fruit The test results demonstrated the effectiveness of the proposed algorithm for defect segmentation on apples. This method can be applied to other fruits with curved shape. Moreover, an effective approach for box-dimension estimation based on a dual-pyramid date structure was developed. Utilizing traditional fractal dimension and 4 oriented fractal dimensions as input values, a BP neural network is designed for identifying fruit defect area and stem, calyx concave area. Experimental results show that the approach is effective for real-time defect extraction and identification. (3) Based on correlative theory a fast and effective shape judgement method and a size calculating method have been developed. Genetic algorithm was used to reduce the processing time for this method. The test results demonstrate the effectiveness of the proposed algorithm for on-line fruit shape grading. (4) A method using HIS color feature and neural network techniques for fruit color inspection was developed. A GA-based training algorithm was introduced to find optimal structure and connection weights of artificial neural network. This approach can overcome the drawbacks of the traditional designing method of artificial neural network in which the structure was predetermined. The...