Tomato grading based on Machine vision D-S multi-feature fusion algorithm

Abstract

Aiming at the problem that the detection speed is slow and the classification standard cannot be comprehensively rated when grading tomato quality at present, this paper proposes an improved Sobel edge detection algorithm and multi-feature fusion classification model. Firstly, by combining fast median filtering and morphological operation method, and improving the calculation of the gradient direction of Sobel operator. It can better extract the edge image features of tomato, and provide better image information for subsequent size, maturity and defect detection. Then, a multi-feature fusion algorithm based on D-S evidence theory was proposed. The algorithm process carried out the classification results by D-S evidence theory to calculate a new belief function to assign different weights, and realized a multi-feature fusion classification model. Finally, through the experimental verification, the online detection speed reaches 6 /s, and the average accuracy of tomato quality classification detection is 91%. Compared with the single feature classification, it is found that the multi-feature fusion classification is relatively high in speed and accuracy, which realizes the rapid detection and comprehensive classification of tomato quality classification.

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