Banana mature period identification method and apparatus based on deep convolution neural network

Abstract

The invention discloses a banana mature period identification method and apparatus based on a deep convolution neural network. The banana mature period identification method based on a deep convolution neural network includes the steps: acquiring the banana images, to be detected, in different mature periods, during the agricultural production and food quality detection process; performing data cleaning and data extension preprocessing on the acquired banana images according to the corresponding mature period; combining with the characteristics of the banana mature period images to be identified, and accordingly designing the corresponding deep convolution neural network structure; taking the preprocessed banana images as the training data, utilizing the deep convolution neural network structure, and training the banana mature period identification model; and utilizing the trained banana mature period identification model to perform an accuracy test on the banana images to be identified, and if the test accuracy does not achieve the application standard, re-training the banana mature period identification model until achieving the application standard. The banana mature period identification method and apparatus based on a deep convolution neural network can avoid errors caused by human factors, can provide guarantee for banana quality detection, and can save a lot of manpowerand material resources.

Description

technical field [0001] The present invention relates to an image processing method, in particular to a method and device for identifying ripeness of bananas based on a deep convolutional neural network. Background technique [0002] In recent years, deep learning techniques, especially convolutional neural networks, have been widely used in image recognition tasks such as image classification, object detection, and image segmentation. At the same time, with the advancement of society and the continuous deepening of automation, the use of computer-aided agricultural production can save manpower and production costs on the one hand, and improve agricultural production efficiency on the other hand. [0003] In the process of banana production and sales, the ripening period of bananas plays a decisive role in the quality inspection of bananas, ensuring that the color and appearance of bananas conform to the calibrated ripening period of bananas. The classification of banana mat...

AI Technical Summary

Problems solved by technology

This method aims at the problem of automatic detection of banana maturity stage in agricultural production, combining computer technology and deep learning technology, especially using deep convolutional neural network algorithm, using computer to learn banana images that need to be carried out in agricultural production detection, and can obtain Quickly and accurately identify deep learning models at different stages of maturity, and use this model to guide agricultural production, which can improve production efficiency and save production costs

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