Fruit classification analysis method based on spectrum recognition and depth learning

Abstract

The invention provides a fruit classification analysis method based on spectrum recognition and depth learning. The method is characterized by collecting image samples by utilizing a mobile robot, andcarrying out establishment of an LMDB data source and OpenCV image preprocessing; carrying out configuration of network parameters under a Caffe framework; constructing a standard model by utilizinga CNN convolution neural network algorithm, so that classification and optimization of the fruits can be realized; and drawing a LOSS curve and judging the curve fitting degree. The fruit classification analysis method based on spectrum recognition and depth learning realizes classification of fruit sizes and colors, reduces manual fruit sorting labor intensity and improves sorting efficiency.

Description

technical field [0001] The invention relates to the field of image recognition of agricultural products, in particular to an analysis method for fruit classification based on map recognition and deep learning. Background technique [0002] my country is the largest fruit-producing country in the world, and its planting area and fruit output have always ranked first in the world. At the same time, the quality and industrialization level of fruit products have also been greatly improved. At present, the fruit industry has become the third largest agricultural planting industry after grain and vegetables. It is an advantage with broad domestic and foreign markets and strong international competitiveness. The agricultural industry is also a highlight of economic development in many places and one of the pillar industries for farmers to get rich. [0003] The traditional artificial fruit classification is mainly based on two aspects of color and shape. Its disadvantages are low ...

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Problems solved by technology

[0004] However, both foreign experts and domestic researchers use a single classifier, in other words, a single classifier, which makes it difficult to effectively improve the accuracy of class discrimination

In addition, at present, most of the fruit grading process in our country adopts manual grading, and the automatic grading system is seldom used.

The shortcomings of artificial fruit grading are: it takes a long time, the result is unstable, it is easy to be disturbed by human factors, and it consumes a lot of manpower and material resources. It is not suitable for the development of fruit deep processing industry, and also restricts the competition of my country's fruit industry in the international market. force

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