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
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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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