Potato defect detection and recognition system design based on machine vision

Abstract

The invention discloses a potato defect detection and recognition system design based on machine vision. The potato defect detection and recognition system design is characterized in that defected potatoes are identified and classified on a ZYNQ platform by utilizing a machine vision library Open CV of an embedded Linux system; characteristic factors of the defected potatoes with green peels, dry rot, crust and mechanical damages are extracted and R, G and B discrete degrees of variable defect factors are analyzed to realize detection and recognition of surface defects of the potatoes, and the algorithm precision is greatly improved. Wavelet transform is applied to analysis and detection of potato shapes of the potatoes, and ellipse radiuses of the potatoes are extracted and are subjected to normalization processing; grading is carried out through a RBF (Radial Basis Function) neural network, so that the efficiency and precision of recognizing the defected potatoes by grades are improved; potato images are pre-processed by utilizing an FPGA (Field Programmable Gate Array) and an algorithm in the Open CV is subjected to accelerated processing; a calculation speed and the algorithm efficiency are remarkably improved. A testing result shows that compared with an existing defected potato recognition and classification technology based on software image processing, an image processing algorithm is innovated and optimized by a novel method based on a hardware structure platform, and the processing speed and the algorithm efficiency are greatly improved; theories and experiments show that the design has relatively ideal detection efficiency and speed on the recognition and classification of the defected potatoes in an actual process. The design has very great significance on a potato processing industry.

Description

technical field [0001] The invention relates to a design of a detection and recognition system based on machine vision, in particular to a design of a detection and recognition system for potato defects based on machine vision, and belongs to the technical field of machine vision. Background technique [0002] Potatoes are high in yield and rich in nutrition, and are the fourth largest food crop in the world. Potatoes are grown in more than half of the countries and regions in the world. According to the grading standard of "Chinese Agricultural Standard Potato Grading Specifications (NY / T1066.2006)", the potato shape, external defects, internal defects and other characteristics of potatoes are important indicators for grading the internal and external quality of potatoes. During the deep processing, storage and breeding of potatoes, the inclusion of defective potatoes in qualified potatoes will seriously affect the quality of subsequent potato products and reduce the econom...

AI Technical Summary

Problems solved by technology

The typical potato defect classification methods mentioned above are mostly implemented based on software methods. The detection and classification speed is slow, the efficiency is low, the accuracy is low and the cost is high, and none of them can achieve satisfactory results.

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