Strawberry maturity rapid discrimination method based on machine learning

A machine learning and discrimination method technology, applied in nuclear methods, instruments, scientific instruments, etc., can solve the problems of time-consuming and cost, and achieve the effect of simple method, strong practicability, and simplified maturity identification.

Pending Publication Date: 2021-08-24
FUZHOU UNIVERSITY
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Manual grading of strawberries based on human visual perception has traditionally been time-consuming and costly
Furthermore, fruit class

Method used

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  • Strawberry maturity rapid discrimination method based on machine learning
  • Strawberry maturity rapid discrimination method based on machine learning
  • Strawberry maturity rapid discrimination method based on machine learning

Examples

Experimental program
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Example Embodiment

[0033] In order to make this patent, the characteristics and advantages of this patent can be more obvious, and the following is the following examples, as follows:

[0034] This embodiment provides a rapid recognition method based on machine-learning strawberry, such as Figure 1 As shown, including the following steps:

[0035] (1) On the raspberry listing, fresh strawberries are now picking fresh strawberries as experimental samples.

[0036] In this example, strawberry varieties are brandy, entrusting experienced fruit farmers to help sort, as the original data set category.

[0037] (2) Place the strawberries of different maturity in the dark box, as the original data set.

[0038] In the present embodiment, the ripest of strawberry includes: green ripe, white ripe period, tomography, and ripening period. (According to industry standards, the maturity of strawberry fruit is divided into 4 phases based on coloring area, namely green ripe period (25%), white ripe period (50%), t...

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Abstract

The invention provides a strawberry maturity rapid discrimination method based on machine learning. The strawberry maturity rapid discrimination method comprises the following steps: using strawberries with different maturity as experimental samples; placing strawberries of different maturity degrees in a camera obscura to be photographed to serve as an original data set; segmenting the original image of the strawberry to realize background and target separation; extracting color features of the preprocessed strawberry sample image; based on the extracted color features, establishing a machine learning classification model for predicting the strawberry maturity; and based on the established machine learning classification model, performing maturity discrimination on a strawberry sample to be detected. The method is beneficial to simply and quickly identifying the maturity of an unknown strawberry sample.

Description

technical field [0001] The invention belongs to the technical field of food detection, and in particular relates to a method for quickly judging the ripeness of strawberries based on machine learning. Background technique [0002] Strawberry is one of the seven major fruits in the world. It is full of fruit fragrance, excellent taste, rich in nutrients and has the reputation of "Queen of Fruits". The content of vitamin A in strawberry is very high, which makes it have the effect of alleviating night blindness to a certain extent. At the same time, it is rich in carotene, which can effectively maintain the health of epithelial tissue, improve eyesight and nourish the liver, promote growth and development, improve constipation, and prevent acne and intestinal cancer happened. Due to the fresh and tender pulp, strawberries are extremely perishable and difficult to store. Usually, once strawberries need long-distance transportation or storage, fruit farmers will pick them when...

Claims

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Application Information

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IPC IPC(8): G01N21/84G06K9/00G06K9/34G06K9/46G06K9/62G06N20/10
CPCG01N21/84G06N20/10G01N2021/8466G06V20/00G06V10/267G06V10/56G06F18/2135G06F18/2411
Inventor 罗芳付琪潘嘉勋卢荟霖林振宇郭隆华邱彬
Owner FUZHOU UNIVERSITY
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