Predicting Forage Quality of Warm-Season Legumes by Near Infrared Spectroscopy Coupled with Machine Learning Techniques

Warm-season legumes have been receiving increased attention as forage resources in the southern United States and other countries. However, the near infrared spectroscopy (NIRS) technique has not been widely explored for predicting the forage quality of many of these legumes. The objective of this r...

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Bibliographic Details
Published in:	Sensors (Basel, Switzerland) Vol. 20; no. 3; p. 867
Main Authors:	Baath, Gurjinder S, Baath, Harpinder K, Gowda, Prasanna H, Thomas, Johnson P, Northup, Brian K, Rao, Srinivas C, Singh, Hardeep
Format:	Journal Article
Language:	English
Published:	Switzerland MDPI AG 06-02-2020 MDPI
Subjects:	Biomass Calibration Crops Experiments Gaussian process gaussian processes guar Infrared spectroscopy Laboratories Legumes Machine learning Methods Near infrared radiation partial least square Peas pigeon pea Pigeonpeas Random variables soybean Soybeans Spectrum analysis Summer support vector machine Support vector machines tepary bean Macedon United States > US soybean support vector machine Gaussian processes tepary bean pigeon pea partial least square guar
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Summary:	Warm-season legumes have been receiving increased attention as forage resources in the southern United States and other countries. However, the near infrared spectroscopy (NIRS) technique has not been widely explored for predicting the forage quality of many of these legumes. The objective of this research was to assess the performance of NIRS in predicting the forage quality parameters of five warm-season legumes-guar ( ), tepary bean ( ), pigeon pea ( ), soybean ( ), and mothbean ( )-using three machine learning techniques: partial least square (PLS), support vector machine (SVM), and Gaussian processes (GP). Additionally, the efficacy of global models in predicting forage quality was investigated. A set of 70 forage samples was used to develop species-based models for concentrations of crude protein (CP), acid detergent fiber (ADF), neutral detergent fiber (NDF), and in vitro true digestibility (IVTD) of guar and tepary bean forages, and CP and IVTD in pigeon pea and soybean. All species-based models were tested through 10-fold cross-validations, followed by external validations using 20 samples of each species. The global models for CP and IVTD of warm-season legumes were developed using a set of 150 random samples, including 30 samples for each of the five species. The global models were tested through 10-fold cross-validation, and external validation using five individual sets of 20 samples each for different legume species. Among techniques, PLS consistently performed best at calibrating (R = 0.94-0.98) all forage quality parameters in both species-based and global models. The SVM provided the most accurate predictions for guar and soybean crops, and global models, and both SVM and PLS performed better for tepary bean and pigeon pea forages. The global modeling approach that developed a single model for all five crops yielded sufficient accuracy (R /R = 0.92-0.99) in predicting CP of the different legumes. However, the accuracy of predictions of in vitro true digestibility (IVTD) for the different legumes was variable (R /R = 0.42-0.98). Machine learning algorithms like SVM could help develop robust NIRS-based models for predicting forage quality with a relatively small number of samples, and thus needs further attention in different NIRS based applications.
Bibliography:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	1424-8220 1424-8220
DOI:	10.3390/s20030867