Research on the Maturity Detection Method of Korla Pears Based on Hyperspectral Technology

Abstract

In this study, hyperspectral imaging technology with a wavelength range of 450 to 1000 nanometers was used to collect spectral data from 160 Korla pear samples at various maturity stages (immature, semimature, mature, and overripe). To ensure high-quality data, multiple preprocessing techniques such as multiplicative scatter correction (MSC), standard normal variate (SNV), and normalization were employed. Based on these preprocessed data, a custom convolutional neural network model (CNN-S) was constructed and trained to achieve precise classification and identification of the maturity stages of Korla pears. Additionally, a BP neural network model was used to determine the characteristic wavelengths for maturity assessment based on the sugar content feature wavelengths. The results demonstrated that the BP model, based on sugar content feature wavelengths, effectively discriminated the maturity stages of the pears. Specifically, the comprehensive recognition rates for the training, testing, and validation sets were 98.5%, 93.5%, and 90.5%, respectively. Furthermore, the combination of hyperspectral imaging technology and the custom CNN-S model significantly enhanced the detection performance of pear maturity. Compared to traditional CNN models, the CNN-S model improved the accuracy of the test set by nearly 10%. Moreover, the CNN-S model outperformed existing techniques based on partial least squares discriminant analysis (PLS-DA) and support vector machine (SVM) in capturing hyperspectral data features, showing superior generalization capability and detection efficiency. The superior performance of this method in practical applications further supports its potential in smart agriculture technology, providing a more efficient and accurate solution for agricultural product quality detection. Additionally, it plays a crucial role in the development of smart agricultural technology.