| Abstrakt: |
Mango is one of the most popular fruits in the world, with a wide variety of types that exhibit significant differences in texture, taste, color, and other aspects. In order to meet the simultaneous demands of the consumers and protect their rights, the identification of mango varieties is particularly important. Near-infrared spectroscopy (NIRS) technology has been widely used in agricultural product identification due to its simplicity, rapidity, efficiency, and environmental friendliness. Moreover, artificial intelligence technology is more conducive to improve the accuracy and efficiency of mango variety identification. This study proposes a convolutional neural network (CNN) model based on channel attention mechanism (MCNN), which can adaptively learn channel weights and can more effectively extract near-infrared spectroscopy features of mangoes by incorporating parallel networks. Compared with traditional deep learning and machine learning models, the MCNN model achieved better performance in mango variety identification with an accuracy of 98.67%. The results indicate that the MCNN model can quickly and accurately recognize mango categories without preprocessing and feature extraction. This method not only enables the identification of mango varieties, but also lays a theoretical foundation for the identification of other agricultural products and related products. In this study, the combination of near-infrared technology and MCNN model provides a new approach for intelligent modeling of spectra. In addition, this method provides a theoretical basis for establishing a fast, non-destructive, and high-precision near-infrared qualitative analysis model, promoting the information processing of agricultural product quality appraisal. [ABSTRACT FROM AUTHOR] |
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