Convolutional Neural Network-Based Models for Near-Infrared Prediction of Nutritional Quality in Multi-Product Animal Feeds.

Xueping Yang, Zhengling Liu, Fuyu Yang, Yanli Lin, Paolo Berzaghi, Salvador Castillo-Girones

Abstract read

In one paragraph

Article in Animals : an open access journal from MDPI, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Xueping YangCollege of Grassland Science, China Agricultural University, Beijing 100193, China.

Zhengling LiuCollege of Grassland Science, China Agricultural University, Beijing 100193, China.

Fuyu YangCollege of Grassland Science, China Agricultural University, Beijing 100193, China.

Yanli LinCollege of Grassland Science, China Agricultural University, Beijing 100193, China.

Paolo BerzaghiDepartment of Animal Medicine, Production and Health, University of Padova, 35020 Legnaro, Italy.

Salvador Castillo-GironesCranfield Environment Centre, Cranfield University, Bedfordshire MK43 0AL, UK.ORCID 0000-0002-0778-179X

Funding

National Key Research and Development Program of China 2021YFD1300300

6 · The paper itself

Abstract

Near-infrared spectroscopy (NIRS) is widely used for rapid and non-destructive evaluation of feed nutritional quality, but robust calibration remains challenging for heterogeneous multi-product feed datasets. This study evaluated convolutional neural network (CNN)-based models for predicting crude protein (CP) and acid detergent fiber (ADF) using a previously published NIR database containing forage and grain-based feeds. A one-dimensional CNN and two hybrid models, CNN combined with partial least squares regression (CNN+PLS) and XGBoost (CNN+XGBoost), were developed and compared with conventional PLSR calibration models based on either the pooled multi-product dataset or product-specific subsets. Model performance was assessed using an independent internal hold-out test set generated within the same database. For CP prediction, CNN-based models achieved strong performance on the hold-out test set, with testing R

Indexed as

animal feedconvolutional neural networkGrad-CAMnear-infrared spectroscopyXGBoost algorithm

Identifiers

PMID42278109

DOI10.3390/ani16111676

PMCPMC13255911

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