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This invention discloses a rapeseed quality detection method based on feature wavelength optimization and multi-model fusion. The method involves collecting full-band near-infrared diffuse reflectance spectral data of rapeseed samples and simultaneously measuring the true values ​​of physicochemical indicators to construct an original dataset. Based on the data distribution characteristics of each physicochemical indicator, the original dataset is divided into a training set and a test set. Preprocessing algorithms are selected for each physicochemical indicator to process the full-band near-infrared diffuse reflectance spectral data. Dimensionality reduction algorithms are selected for each physicochemical indicator to extract feature wavelengths from the preprocessed spectral data. Predictive models for each physicochemical indicator are established based on the training set, and the performance of the predictive models is verified using the test set to determine the optimal algorithm combination for each physicochemical indicator. The variable importance projection algorithm is used to calculate the comprehensive contribution score of each wavelength point in the full-band spectrum to the four physicochemical indicators. A multi-indicator comprehensive threshold is set, and the core light source wavelength of the portable device is determined from the high-score region.