Rapid prediction of potato leaf moisture content

Xufeng Yu; Wei Zhuo; Xinting Li; Feng Zhang; Shengwei Li; Jie Feng

doi:10.1117/12.2575051

10 October 2020 Rapid prediction of potato leaf moisture content

Xufeng Yu, Wei Zhuo, Xinting Li, Feng Zhang, Shengwei Li, Jie Feng

Proceedings Volume 11559, Infrared, Millimeter-Wave, and Terahertz Technologies VII; 115590Y (2020) https://doi.org/10.1117/12.2575051
Event: SPIE/COS Photonics Asia, 2020, Online Only

Conference Poster

Abstract

When using near infrared spectroscopy to predict the moisture content of potato leaves, a large amount of spectral data needs to be processed, resulting in a time-consuming and labor-intensive calculation process. This paper proposes to use a variety of feature wavelength extraction methods to reduce the amount of calculation of near-infrared spectral data, and according to the comparison of prediction results, the feature wavelength extraction method with the best extraction effect is obtained. First, the spectral reflectance information of 110 fresh potato leaves in the 900~2100nm band is collected, and then the Regression Coefficient (RC), Principal Component Analysis (PCA), first-order derivative correlation extraction are used respectively Method, extract the characteristic wavelengths from the full-band spectral data, and finally establish a BP neural network prediction model according to the characteristic wavelengths extracted in three different ways, and compare the prediction results to obtain the optimal characteristic wavelength extraction method. The results show that the BP neural network model established by the characteristic wavelength extracted by the Regression Coefficient (RC) has the best prediction effect, the prediction set decision coefficient (R2) is 0.9698, and the root mean square error (RMSE) is 0.3177. In this experiment, on the basis of reducing the amount of near infrared spectroscopy data by more than 90%, a good prediction effect was achieved, and the purpose of quickly and concisely predicting the moisture content of potato leaves was achieved.

Conference Presentation

Citation Download Citation

Xufeng Yu, Wei Zhuo, Xinting Li, Feng Zhang, Shengwei Li, and Jie Feng "Rapid prediction of potato leaf moisture content", Proc. SPIE 11559, Infrared, Millimeter-Wave, and Terahertz Technologies VII, 115590Y (10 October 2020); https://doi.org/10.1117/12.2575051

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