Citrus quality classification method and system based on random forest model and fuzzy clustering

A random forest model and fuzzy clustering technology, applied in the field of citrus quality classification methods and systems, can solve problems such as fuzzy boundaries of citrus quality classification, and achieve the effect of improving experience, improving accuracy and efficiency, and strong generalization ability

Pending Publication Date: 2022-07-01
CHONGQING UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0003] In order to solve the above existing problems in the prior art, the present invention proposes a citrus quality classification method based on random forest model and fuzzy clustering. Blur samples to improve the robustness of sample quality

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  • Citrus quality classification method and system based on random forest model and fuzzy clustering
  • Citrus quality classification method and system based on random forest model and fuzzy clustering
  • Citrus quality classification method and system based on random forest model and fuzzy clustering

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[0046] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, but not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0047] A citrus quality classification method based on random forest model and fuzzy clustering, such as figure 1 As shown, the method includes: acquiring the citrus image to be identified, inputting the acquired citrus image into a trained random forest model to obtain an initial fuzzy sample, and then clustering through the improved SSFCM to obtain a quality classification result; Quality classification results The citrus is classif...

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Abstract

The invention belongs to the technical field of computer vision application, and particularly relates to a citrus quality classification method and system based on a random forest model and fuzzy clustering. The method comprises the following steps: acquiring a to-be-identified citrus image, and inputting the acquired citrus image into a trained random forest model to obtain an initial fuzzy sample; clustering data in the initial fuzzy sample by adopting an improved semi-supervised fuzzy algorithm SSFCM to obtain a quality classification result; marking quality labels on the oranges according to a quality classification result; according to the method, the quality of the citrus is classified by adopting a machine learning method, so that the classification efficiency and accuracy are improved.

Description

technical field [0001] The invention belongs to the technical field of computer vision applications, in particular to a method and system for classifying citrus quality based on a random forest model and fuzzy clustering. Background technique [0002] Citrus is a fruit that can be seen everywhere in daily life. It has a fragrant smell, delicious taste and rich nutrition, and is very popular among consumers. But now the major citrus planting parks mainly rely on manual selection of fruit saplings and planting of fruit trees, pruning branches regularly every year, ploughing the park, and cleaning up insect-eaten young fruit. Waste a lot of time and energy and increase human and material costs. When citrus matures and is about to enter the sales link, relying on manual classification of citrus fruit quality based on experience is labor-intensive, time-consuming, low-efficiency, and inaccurate. SUMMARY OF THE INVENTION [0003] In order to solve the above problems existing i...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06V10/764G06V10/762
CPCG06F18/23213G06F18/24323
Inventor 李腊全赖宗萱沙霖张桂铭胡景怡余海燕邵亚斌
Owner CHONGQING UNIV OF POSTS & TELECOMM
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