Predicting potato dry matter content by incorporating spectral statistical features

Wang, G.; Zhang, Y.; Zhang, N.; Xu, M.; Kear, P.; Qi, J.; Zhang, Q.

Predicting potato dry matter content by incorporating spectral statistical features

Autores

Editor

Elsevier

Descripción

Accurate and high-throughput prediction of crop quality parameters is essential for intelligent agricultural production and automated grading. Dry matter content (DMC) is a key indicator of potato quality and processing performance. To address the limitations of hyperspectral modeling in capturing sample heterogeneity and localized spectral features, this study presents a multi-source modeling approach by integrating near-infrared hyperspectral reflectance (780–2000 nm) with spectral statistical features, Spectral Dispersion (SD) and Spectral Smoothness (SS). A dataset of 442 potato samples was analyzed using a triple optimization strategy, which includes sample subset optimization, key wavelength selection, and statistical feature fusion. The resulting models demonstrated robust predictive performance, with coefficients of determination for the prediction (R2P) of 0.80 and 0.75, root mean square errors of prediction (RMSEP) of 1.32% and 1.44%, and ratio of performance to deviation (RPD) values > 2. These results confirm the effectiveness of the proposed fusion strategy for DMC prediction, providing a scalable and transferable approach for rapid quality assessment of crop biochemical properties and supports intelligent quality assessment in precision agriculture.