Characterization of rice starch changes in saline and alkaline area under different fertilization conditions based on Raman spectral recognition technology.

ABSTRACT: Starch content in rice is one of the important parameters in characterizing the nutritional quality of rice, and the starch content of rice produced in saline soils under different fertilization conditions varies. In this study, Raman spectroscopy combined with three machine learning models, support vector machine (SVM), feedforward neural network, and k-nearest neighbor classification, was used to classify and evaluate the effect of different fertilizer treatments on rice. The collected rice spectral data were normalized before machine learning, then preprocessed with multiple scattering correction (MSC), standard normal variable, and Savitzky-Golay filtering algorithms to improve the quality and reliability of the data. The evaluation indexes such as the confusion matrix and the receiver

SUBMITTER: Li Z 

PROVIDER: S-EPMC11920442 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature