An intelligent method for detecting the maturity of Camellia oleifera fruit

Abstract

The invention relates to a method for intelligently detecting the maturity of camellia oleifera fruit, comprising: measuring the internal quality parameters and external phenotypic characteristics of the maturity of camellia oleifera fruit, and extracting them to obtain the key parameter maturity of the external phenotype characteristics between different varieties Based on the expression difference of maturity degree of different varieties, the dynamic mapping models from internal quality to external phenotypic characteristics were respectively established; using the dynamic mapping model as a training sample, a Camellia oleifera from internal quality parameters to external phenotypic characteristics was established. Fruit maturity quantitative standard and quantitative detection model, and clarified the mechanism of quantitative and quantitative representation of ripeness. The present invention constructs the dynamic mapping of camellia oleifera fruit from internal quality parameters to external phenotypic features through deep convolutional neural network technology in artificial intelligence deep learning, realizes the quantitative characterization of camellia oleifera fruit maturity, and then reveals the quantitative characterization mechanism of camellia oleifera fruit maturity.

Description

technical field [0001] The invention relates to the technical field of intelligent agriculture, in particular to an intelligent method for detecting the maturity of camellia oleifera fruit. Background technique [0002] Camellia oleifera (Camellia oleifera Abel), as an important woody edible oil species unique to my country, has extremely high nutritional value and health care functions. In 2020, the planting area of ​​camellia oleifera nationwide will reach 68 million mu, the output of camellia oleifera will be 627,000 tons, and the total output value of camellia oleifera industry will reach 116 billion yuan. Although the planting area of ​​camellia oleifera continues to expand, the average yield per mu has not increased significantly, which is far from the expected goal of the "National Camellia oleifera Industry Development Plan (2009-2020)". Putting aside the problems in the cultivation of Camellia oleifera seedlings, the main reason for analysis is that Camellia oleife...

AI Technical Summary

Problems solved by technology

In addition, comprehensive studies on camellia oleifera and maturity detection have found that the research on mechanized picking technology of camellia oleifera is in progress; research on intelligent picking of camellia oleifera is rarely involved

Most of the studies on maturity detection use external phenotypic characteristics, and there are few reports on the maturity of camellia oleifera from internal quality parameters to external phenotypic characteristics combined with artificial intelligence technology.

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