外文摘要：The protein content (PC) and wet gluten content (WGC) are crucial indicators determining the quality of wheat, playing a pivotal role in evaluating processing and baking performance. Original reflectance (OR), wavelet feature (WF), and color index (CI) were extracted from hyperspectral and RGB sensors. Combining Pearsoncompetitive adaptive reweighted sampling (CARs)-variance inflation factor (VIF) with four machine learning (ML) algorithms were used to model accuracy of PC and WGC. As a result, three CIs, six ORs, and twelve WFs were selected for PC and WGC datasets. For single-modal data, the back-propagation neural network exhibited superior accuracy, with estimation accuracies (WF > OR > CI). For multi-modal data, the random forest regression paired with OR + WF + CI showed the highest validation accuracy. Utilizing the Gini impurity, WF outweighed OR and CI in the PC and WGC models. The amalgamation of MLs with multimodal data harnessed the synergies among various remote sensing sources, substantially augmenting model precision and stability.
外文关键词：machine learning;wheat;protein content;Wet gluten content;Multi-modal data;Pearson-CARs-VIF
作者：Feng, Wei;Zhang, Shaohua;He, Li;Guo, Tiancai;Qi, Xinghui;Gao, Mengyuan;Dai, Changjun;Yin, Guihong;Ma, Dongyun
作者单位：Heilongjiang Acad Agr Sci;Henan Agr Univ;Natl Wheat Technol Innovat Ctr
期刊名称：FOOD CHEMISTRY
期刊影响因子：0.0
出版年份：2024
出版刊次：448
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台