Watermelon maturity detection method and system based on acoustic analysis and machine learning

Abstract

The invention discloses a watermelon maturity detection method and system based on acoustic analysis and machine learning, and the method comprises the steps: obtaining knocking sound signals and weight of watermelon samples to form a data set, classifying the watermelon samples according to maturity, and dividing the data set into a training set and a test set; constructing a maturity detection model to calculate the maturity of the target watermelon sample; training the maturity detection model by using the training set, then testing the maturity detection model by using the test set, finally calculating and recording the accuracy, continuing to execute the step of training the maturity detection model by using the training set until the accuracy is the highest, and obtaining the trained maturity detection model; and obtaining a knocking sound signal and weight of the watermelon test sample, and inputting the knocking sound signal and weight into the trained maturity detection model to obtain the maturity of the watermelon test sample. According to the method, the requirement for watermelon maturity detection is met, the detection accuracy is improved, model training and testing can be completed in a small sample data set, and the applicability is improved.

Description

technical field [0001] The invention relates to the field of audio analysis, in particular to a watermelon maturity detection method and system based on acoustic analysis and machine learning. Background technique [0002] my country's watermelon production is large, but there are problems such as insufficient competitiveness, small export volume and low export unit price in the world market. The reason is that my country's watermelon maturity detection system is backward, resulting in mixed watermelons of various maturity levels, reducing the overall quality. [0003] At present, the detection method based on acoustic characteristics is mainly used for the detection of watermelon maturity, because the sound signal generated by tapping the watermelon is easier to obtain, which can reflect the watermelon pulp hardness, absorption and reflection characteristics, size, water content and other information, so that it can be judged Ripeness of watermelon. [0004] At present, m...

AI Technical Summary

Problems solved by technology

It is difficult to express the distribution of the acoustic features of the sample at a single frequency point, because it is only a maximum frequency component in the frequency domain, ignoring other frequency domain features, and completely missing the change of the sound signal in the time domain

At the same time, the size of the evaluation index is easily affected by the error of the calculation result of a single frequency point, because it is only obtained through a simple mathematical operation relationship

Therefore, the current detection method based on acoustic characteristics has poor applicability, is easily affected by the error of a single frequency point of the sample, and has low accuracy.

[0005] However, other watermelon ripening detection methods, such as methods based on infrared spectrum detection and machine vision detection, have disadvantages such as complex detection equipment, high cost, and difficulty in obtaining evaluation indicators.

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