Fruit and vegetable images classifying and identifying method and system based on model integration

Abstract

The invention provides a fruit and vegetable images classifying and identifying method and system based on model integration. The method comprises the steps that the fruit and vegetable images to be identified are respectively input in each nerve network model of a plurality of nerve network models after training, so that each nerve network model outputs the identification result of the fruit andvegetable images to be identified; integrating the identification result output by each nerve network model, and obtaining the final identification result of the fruit and vegetable images to be identified. The invention is advantageous in that the classifying features of a plurality of network models can be fully developed, and the same sample can undergo multiple classifications of different network models, and finally a classifying identification result after optimization grouping can be generated, which is always more reliable and more accurate than the classifying identification result generated by a single network model.

Description

technical field [0001] The present invention relates to the technical field of image classification and recognition, and more specifically, to a method and system for fruit and vegetable image classification and recognition based on model fusion. Background technique [0002] With the development of Internet technology and various information technologies, my country's agricultural development model has begun to transform from the original traditional agriculture to modern smart agriculture, and the production of vegetables in agricultural products is also increasing. At present, most of the vegetable picking and sorting management is still done manually, which not only consumes a lot of labor, but also has low work efficiency, which seriously affects the development speed of commercialization of vegetable products. The classification and recognition of multiple types of vegetables has a relatively broad application value in practice, and also plays an important role in smar...

AI Technical Summary

Problems solved by technology

[0003] At present, a single neural network model is often used to classify and recognize fruit and vegetable images, and the recognition accuracy is usually not high enough. It is necessary to improve the recognition accuracy of the neural network model

If the neural network model is not modified mathematically, but simply adjusts the parameters to seek a neural network model with higher accuracy, this approach not only increases the complexity of calculation, but also makes the neural network model The accuracy rate reaches an upper limit during training, that is, it is possible to pay a huge price to adjust the parameters, but the accuracy rate has only a negligible improvement or no improvement or even a decline in overfitting

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