Image detecting method for grading of tomatoes

Abstract

The invention relates to an image detecting method for grading of tomatoes. The detecting method comprises the following steps. The tomatoes continuously rotate to pass through an image collecting area, and at least three images of different surfaces are collected. The major area of the surface of each tomato is collected, an R channel image is extracted from collected RGB color space source images of the tomatoes, and average filtering is performed on the images. The processed images undergo threshold segmentation and binarization processing so as to obtain a binarization image, namely a target zone of the tomatoes. The edge of a target is the largest fruit diameter d of each tomato, and a color level value c in the target zone is calculated. Finally, the largest diameter d and the color level value c are compared with all set fruit diameters and color level value ranges of all grades, and the grade of each tomato is finally judged. According to the image detecting method for grading of the tomatoes, the calculating method is low in complexity, high in reliability, stable in standard, and good in application prospect.

Description

technical field [0001] The invention relates to an image detection method for grading tomatoes, in particular to a method for detecting and grading tomatoes based on machine vision. Background technique [0002] The planting of tomatoes is widely distributed in our country, and the post-harvest treatment of tomatoes has a huge role in promoting the quality and price of commodities. At present, tomato grading in my country is mainly done manually. The disadvantages of manual grading are: low efficiency, high cost, and the long-term grading process is not good for the circulation of fresh fruits and vegetables; the accuracy is poor, and the target can only be graded based on personal experience, and the target cannot be described quantitatively, resulting in different grading results from person to person ; Labor intensity is high, and manual grading is a tedious work, which is prone to visual fatigue and affects the grading accuracy. [0003] With the development of comput...

AI Technical Summary

Problems solved by technology

The disadvantages of manual grading are: low efficiency, high cost, and the long-term grading process is not good for the circulation of fresh fruits and vegetables; the accuracy is poor, and the target can only be graded based on personal experience, and the target cannot be described quantitatively, resulting in different grading results from person to person ;The labor intensity is high, manual grading is a tedious work, it is easy to cause visual fatigue and affect the grading accuracy

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