ArticleSensors (Basel, Switzerland)2020
Predicting Forage Quality of Warm-Season Legumes by Near Infrared Spectroscopy Coupled with Machine Learning Techniques.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- Sensor-Based Pasture Quality Monitoring: Supporting Grazing Management and Preventing Nutritional and Metabolic Disorders in Ruminants.Sensors (Basel, Switzerland) · 2026Review
- Real-Time Dry Matter Prediction in Whole-Plant Corn Forage and Silage Using Portable Near-Infrared Spectroscopy.Animals : an open access journal from MDPI · 2025Article
- Article
- Rapid Determination of Crude Protein Content in Alfalfa Based on Fourier Transform Infrared Spectroscopy.Foods (Basel, Switzerland) · 2024Article
- Rapid analysis of hydrogen cyanide in fresh cassava roots using NIRSand machine learning algorithms: Meeting end user demand for low cyanogenic cassava.The plant genome · 2024Article
- Measurement of Volatile Fatty Acids in Silage through Odors with Nanomechanical Sensors.Biosensors · 2023Article
- Forages and pastures symposium: an update on in vitro and in situ experimental techniques for approximation of ruminal fiber degradation.Journal of animal science · 2023Article
- Measurements of Chemical Compositions in Corn Stover and Wheat Straw by Near-Infrared Reflectance Spectroscopy.Animals : an open access journal from MDPI · 2021Article
- Machine Learning in Agriculture: A Comprehensive Updated Review.Sensors (Basel, Switzerland) · 2021Review
- Editorial: Special Issue "Emerging Sensor Technology in Agriculture".Sensors (Basel, Switzerland) · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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 (
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Registered trials
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