Embedded apple maturity detection system and method

Abstract

The invention belongs to the technical field of agricultural product nondestructive testing, and discloses an embedded apple maturity detection system and method, and the method comprises the steps: replacing original high-order data with new low-dimensional data for spectral clustering analysis, and extracting the characteristic wavelength of apple maturity; dividing a training set and a prediction set of data samples to obtain a penalty parameter c and a kernel function parameter g in a support vector machine model; constructing a classification model of apple maturity; obtaining sample apple characteristic spectral data, and constructing an apple maturity classification model; taking the apple maturity classification model as a core, writing a control program fused with the apple maturity classification model, and realizing nondestructive prediction of apple maturity. According to the invention, indirect quantity prediction is reduced, the working complexity is reduced, and the working efficiency and precision are improved; a miniaturized printed circuit board is designed, an embedded operating system is implanted, and small portable apple maturity rapid classification equipmentis designed.

Description

technical field [0001] The invention belongs to the technical field of non-destructive testing of agricultural products, and in particular relates to an embedded apple maturity testing system and method. Background technique [0002] At present, apples are rich in varieties and widely planted. It is the largest fruit in my country, and its planting area and output account for about 50% of the world's total. China's apple production is huge, but there is still a certain gap in quality compared with foreign apples. The reason is that the harvesting time of apples for different purposes is unreasonable. In short, apples are immature. Picking in advance not only affects the quality of apples but also causes waste of apples. Apple maturity can determine the picking time of the fruit and evaluate the quality of the fruit after harvest, and is one of the important indicators for evaluating the quality of apples. Harvesting maturity is the key factor to determine the taste and fla...

AI Technical Summary

Problems solved by technology

[0005] (1) At present, the judgment of apple maturity is based on the spectrum prediction of apple quality index factors, and the use of index factors to judge the maturity of apples is cumbersome. Accuracy

[0006] (2) The current prediction method uses the full spectrum to establish a maturity prediction model, which contains more redundant information, and the calculation speed is slow, so it is not easy to popularize

[0007] (3) The existing maturity testing equipment is large in size, has high requirements on the testing environment, and has a long testing cycle

[0008] Difficulty in solving the above technical problems: There are many advantages in using characteristic spectra to establish an apple maturity classification model, but the selection of characteristic bands is a difficult point. It is not a thousand-dimensional array, but a vector with only a dozen elements; the quality of the model based on the support vector machine depends largely on the input parameters. The input parameters are slightly different, and the results obtained are very different. Therefore, how to Choosing the right deterministic input parameters is another difficulty

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