{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T00:45:11Z","timestamp":1784335511931,"version":"3.55.0"},"reference-count":61,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2020,3,13]],"date-time":"2020-03-13T00:00:00Z","timestamp":1584057600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001044","name":"Dairy Australia","doi-asserted-by":"publisher","award":["DairyBio"],"award-info":[{"award-number":["DairyBio"]}],"id":[{"id":"10.13039\/501100001044","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Nutritive value (NV) of forage is too time consuming and expensive to measure routinely in targeted breeding programs. Non-destructive spectroscopy has the potential to quickly and cheaply measure NV but requires an intermediate modelling step to interpret the spectral data. A novel machine learning technique for forage analysis, Cubist, was used to analyse canopy spectra to predict seven NV parameters, including dry matter (DM), acid detergent fibre (ADF), ash, neutral detergent fibre (NDF), in vivo dry matter digestibility (IVDMD), water soluble carbohydrates (WSC), and crude protein (CP). Perennial ryegrass (Lolium perenne) was used as the test crop. Independent validation of the developed models revealed prediction capabilities with R2 values and Lin\u2019s concordance values reported between 0.49 and 0.82, and 0.68 and 0.89, respectively. Informative wavelengths for the creation of predictive models were identified for the seven NV parameters. These wavelengths included regions of the electromagnetic spectrum that are usually excluded due to high background variation, however, they contain important information and utilising them to obtain meaningful signals within the background variation is an advantage for accurate models. Non-destructive field spectroscopy along with the predictive models was deployed infield to measure NV of individual ryegrass plants. A significant reduction in labour was observed. The associated increase in speed and reduction of cost makes targeting NV in commercial breeding programs now feasible.<\/jats:p>","DOI":"10.3390\/rs12060928","type":"journal-article","created":{"date-parts":[[2020,3,13]],"date-time":"2020-03-13T08:58:59Z","timestamp":1584089939000},"page":"928","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Machine Learning Algorithms to Predict Forage Nutritive Value of In Situ Perennial Ryegrass Plants Using Hyperspectral Canopy Reflectance Data"],"prefix":"10.3390","volume":"12","author":[{"given":"Chaya","family":"Smith","sequence":"first","affiliation":[{"name":"School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3086, Australia"},{"name":"Agriculture Victoria, Hamilton Centre, Hamilton, VIC 3300, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Senani","family":"Karunaratne","sequence":"additional","affiliation":[{"name":"Agriculture Victoria, Ellinbank Centre, 1301 Hazeldean Road, Ellinbank, VIC 3821, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pieter","family":"Badenhorst","sequence":"additional","affiliation":[{"name":"Agriculture Victoria, Hamilton Centre, Hamilton, VIC 3300, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noel","family":"Cogan","sequence":"additional","affiliation":[{"name":"School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3086, Australia"},{"name":"Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"German","family":"Spangenberg","sequence":"additional","affiliation":[{"name":"School of Applied Systems Biology, La Trobe University, Bundoora, VIC 3086, Australia"},{"name":"Agriculture Victoria, AgriBio, Centre for AgriBioscience, Bundoora, VIC 3083, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6828-7480","authenticated-orcid":false,"given":"Kevin","family":"Smith","sequence":"additional","affiliation":[{"name":"Agriculture Victoria, Hamilton Centre, Hamilton, VIC 3300, Australia"},{"name":"Faculty of Veterinary and Agricultural Sciences, The University of Melbourne, Melbourne, VIC 3010, Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"855","DOI":"10.1093\/jxb\/erl123","article-title":"Hyperspectral Remote Sensing of Plant Pigments","volume":"58","author":"Blackburn","year":"2007","journal-title":"J. Exp. Bot."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1007\/s11119-012-9260-y","article-title":"Multi-spectral radiometry to Estimate Pasture Quality Components","volume":"13","author":"Pullanagari","year":"2012","journal-title":"Int. J. Adv. Precis. Agric."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/S0065-2113(01)71012-7","article-title":"Breeding Forage Crops for Increased Nutritional Value","volume":"71","author":"Casler","year":"2001","journal-title":"Adv. Agron."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"589","DOI":"10.1016\/j.agsy.2011.06.001","article-title":"Pasture and Forage Crop Systems for Non-irrigated Dairy Farms in Southern Australia: 3. Estimated Economic Value of Additional Home-grown Feed","volume":"104","author":"Chapman","year":"2011","journal-title":"Agric. Syst."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1111\/j.1365-2494.1997.tb02347.x","article-title":"An Assessment of the Relative Importance of Specific Traits for the Genetic Improvement of Nutritive Value in Dairy Pasture","volume":"52","author":"Smith","year":"1997","journal-title":"Grass Forage Sci."},{"key":"ref_6","unstructured":"Mueller-Sim, T., Jenkins, M., Abel, J., and Kantor, G. (June, January 29). The Robotanist: A Ground-based Agricultural Robot for High-throughput Crop Phenotyping. Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (IEEE), Singapore."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"12","DOI":"10.2135\/cropsci1999.0011183X003900010003x","article-title":"Accomplishments and Impact from Breeding for Increased Forage Nutritional Value","volume":"39","author":"Casler","year":"1999","journal-title":"Crop Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"722","DOI":"10.2135\/cropsci1990.0011183X003000030050x","article-title":"Cultivar and Cultivar \u00d7 Environment Effects on Relative Feed Value of Temperate Perennial Grasses","volume":"30","author":"Casler","year":"1990","journal-title":"Crop Sci."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1122","DOI":"10.1139\/x05-037","article-title":"Quantitative Reflectance Spectroscopy as an Alternative to Traditional Wet Lab Analysis of Foliar Chemistry: Near-infrared and Mid-infrared Calibrations Compared","volume":"35","author":"Richardson","year":"2005","journal-title":"Can. J. For. Res."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"927","DOI":"10.2135\/cropsci2005.0258","article-title":"Development of Canopy Reflectance Algorithms for Real-Time Prediction of Bermudagrass Pasture Biomass and Nutritive Values","volume":"46","author":"Starks","year":"2006","journal-title":"Crop Sci."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Araus, J.L., and Cairns, J.E. (2013). Field High-throughput Phenotyping: The New Crop Breeding Frontier. Trends Plant Sci., 19.","DOI":"10.1016\/j.tplants.2013.09.008"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"143","DOI":"10.1071\/FP16163","article-title":"Field Scanalyzer: An Automated Robotic Field Phenotyping Platform for Detailed Crop Monitoring","volume":"44","author":"Virlet","year":"2017","journal-title":"Funct. Plant Biol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1186\/s13007-015-0078-2","article-title":"Unmanned Aerial Platform-based Multi-spectral Imaging for Field Phenotyping of Maize","volume":"11","author":"Vergara","year":"2015","journal-title":"Plant Methods"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1249","DOI":"10.1109\/JSTARS.2014.2298752","article-title":"Toward a Semiautomatic Machine Learning Retrieval of Biophysical Parameters","volume":"7","author":"Caicedo","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"288","DOI":"10.1016\/j.talanta.2013.12.038","article-title":"Limitations and Current Applications of Near Infrared Spectroscopy for Single Seed Analysis","volume":"121","author":"Hurburgh","year":"2014","journal-title":"Talanta"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"217","DOI":"10.1016\/j.talanta.2006.10.022","article-title":"A Consensus Least Squares Support Vector Regression (LS-SVR) for Analysis of Near-infrared Spectra of Plant Samples","volume":"72","author":"Li","year":"2007","journal-title":"Talanta"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1016\/j.compag.2019.03.038","article-title":"Estimation of Yield and Quality of Legume and Grass Mixtures Using Partial Least Squares and Support Vector Machine Analysis of Spectral Data","volume":"162","author":"Zhou","year":"2019","journal-title":"Comput. Electron. Agric."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"246","DOI":"10.1080\/10408347.2010.515468","article-title":"A Tutorial on Near Infrared Spectroscopy and Its Calibration","volume":"40","author":"Agelet","year":"2010","journal-title":"Crit. Rev. Anal. Chem."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1016\/j.chemolab.2011.02.008","article-title":"Waveband Selection for NIR Spectroscopy Analysis of Soil Organic Matter Based on SG Smoothing and MWPLS Methods","volume":"107","author":"Chen","year":"2011","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1002","DOI":"10.1017\/S1751731110002697","article-title":"NIRS Prediction of the Feed Value of Temperate Forages: Efficacy of Four Calibration Strategies","volume":"5","author":"Andueza","year":"2011","journal-title":"Animal"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1007\/s11119-011-9251-4","article-title":"In-field Hyperspectral Proximal Sensing for Estimating Quality Parameters of Mixed Pasture","volume":"13","author":"Pullanagari","year":"2012","journal-title":"Precis. Agric."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Smith, C., Cogan, N., Badenhorst, P., Spangenberg, G., and Smith, K. (2019). Field Spectroscopy to Determine Nutritive Value Parameters of Individual Ryegrass Plants. Agronomy, 9.","DOI":"10.3390\/agronomy9060293"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.aca.2018.04.004","article-title":"Near infrared spectroscopy: A Mature Analytical Technique with New Perspectives\u2014A Review","volume":"1026","author":"Pasquini","year":"2018","journal-title":"Anal. Chim. Acta"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.compag.2015.11.018","article-title":"Wheat Yield Prediction Using Machine Learning and Advanced Sensing techniques","volume":"121","author":"Pantazi","year":"2016","journal-title":"Comput. Electron. Agric."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"239","DOI":"10.1007\/s11119-014-9372-7","article-title":"A Review of Advanced Machine Learning Methods for the Detection of Biotic Stress in Precision Crop Protection","volume":"16","author":"Behmann","year":"2015","journal-title":"An International J. Adv. Precis. Agric."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Holmes, G., Hall, M., and Prank, E. (1999). Generating Rule Sets from Model Trees. Australasian Joint Conference on Artificial Intelligence, Springer.","DOI":"10.1007\/3-540-46695-9_1"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Vapnik, V. (2006). Estimation of Dependences Based on Empirical Data, Springer Science & Business Media.","DOI":"10.1007\/0-387-34239-7"},{"key":"ref_28","unstructured":"Kuhn, M., Weston, S., Keefer, C., and Coulter, N. (2012). Cubist Models for Regression, CRAN. R package Vignette R package version 0.0 2012, 18."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1111\/j.1365-2389.2012.01495.x","article-title":"Predicting Soil Properties from the Australian Soil Visible\u2013near Infrared Spectroscopic Database","volume":"63","author":"Rossel","year":"2012","journal-title":"Eur. J. Soil Sci."},{"key":"ref_30","unstructured":"Minasny, B., McBratney, A.B., Stockmann, U., and Hong, S.Y. (2013). Cubist, a Regression Rule Approach for use in Calibration of NIR Spectra. Picking Up Good Vib., 630."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"79","DOI":"10.5194\/soil-5-79-2019","article-title":"Using Deep Learning for Digital Soil Mapping","volume":"5","author":"Padarian","year":"2019","journal-title":"SOIL"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"104185","DOI":"10.1016\/j.catena.2019.104185","article-title":"Near Infrared Diffuse Reflectance Spectroscopy for Rapid and Comprehensive Soil Condition Assessment in Smallholder Cacao Farming Systems of Papua New Guinea","volume":"183","author":"Singh","year":"2019","journal-title":"Catena"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1627","DOI":"10.1021\/ac60214a047","article-title":"Smoothing and Differentiation of Data by Simplified Least Squares Procedures","volume":"36","author":"Savitzky","year":"1964","journal-title":"Anal. Chem."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"255","DOI":"10.2307\/2532051","article-title":"A Concordance Correlation Coefficient to Evaluate Reproducibility","volume":"45","author":"Lin","year":"1989","journal-title":"Biometrics"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.fcr.2017.02.001","article-title":"How Plant Structure Impacts the Biochemical Leaf Traits Assessment from In-field Hyperspectral Images: A Simulation Study Based on Light Propagation Modeling in 3D Virtual Wheat Scenes","volume":"205","author":"Makdessi","year":"2017","journal-title":"Field Crop. Res."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"12247","DOI":"10.3390\/rs61212247","article-title":"Extraction of Plant Physiological Status from Hyperspectral Signatures Using Machine Learning Methods","volume":"6","author":"Doktor","year":"2014","journal-title":"Remote Sens."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Malmir, M., Tahmasbian, I., Xu, Z., and Farrar, M. (2019). Prediction of Macronutrients in Plant Leaves Using Chemometric Analysis and Wavelength Selection. J. Soils Sediments, 1\u201311.","DOI":"10.1007\/s11368-019-02418-z"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1917","DOI":"10.2135\/cropsci2008.11.0653","article-title":"Development of Canopy Reflectance Models to Predict Forage Quality of Legume-grass Mixtures. (Research) (Author abstract) (Report)","volume":"49","author":"Biewer","year":"2009","journal-title":"Crop Sci."},{"key":"ref_39","unstructured":"Thulin, S.M. (2008). Hyperspectral Remote Sensing of Temperate Pasture Quality. Science, Engineering and Technology Portfolio, School of Mathematical and Geospatial Sciences, RMIT University Melbourne."},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Wessman, C.A. (1990). Evaluation of Canopy Biochemistry. Remote Sensing of Biosphere Functioning, Springer.","DOI":"10.1007\/978-1-4612-3302-2_7"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1321","DOI":"10.1177\/0003702816654056","article-title":"Near-infrared Spectroscopy Calibrations Performed on Oven-dried Green Forages for the Prediction of Chemical Composition and Nutritive Value of Preserved Forage for Ruminants","volume":"70","author":"Andueza","year":"2016","journal-title":"Appl. Spectrosc."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"363","DOI":"10.4081\/ijas.2004.363","article-title":"Quality Evaluation of Regional Forage Resources by Means of Near Infrared Reflectance Spectroscopy","volume":"3","author":"Danieli","year":"2004","journal-title":"Ital. J. Anim. Sci."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.biombioe.2018.04.016","article-title":"Using Remote Sensing to Estimate Forage Biomass and Nutrient Contents at Different Growth Stages","volume":"115","author":"Zeng","year":"2018","journal-title":"Biomass Bioenergy"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1002\/jsfa.2740410304","article-title":"Near Infra-red Analysis of Grass Silage by Principal Component Analysis of Transformed Reflectance Data","volume":"41","author":"Downey","year":"1987","journal-title":"J. Sci. Food Agric."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1002\/cem.1180030207","article-title":"Dried Grass Silage Analysis by NIR Reflectance Spectroscopy\u2014A Comparison of Stepwise Multiple Linear and Principal Component Techniques for Calibration Development on Raw and Transformed Spectral Data","volume":"3","author":"Downey","year":"1989","journal-title":"J. Chemom."},{"key":"ref_46","first-page":"99","article-title":"Spectral Indicators of Forage Quality in West Africa\u2019s Tropical Savannas","volume":"41","author":"Ferner","year":"2015","journal-title":"Int. J. Appl. Earth Obs. Geoinf."},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Jin, J., and Wang, Q. (2019). Evaluation of Informative Bands Used in Different PLS Regressions for Estimating Leaf Biochemical Contents from Hyperspectral Reflectance. Remote Sens., 11.","DOI":"10.3390\/rs11020197"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/j.foodchem.2018.08.075","article-title":"Evaluation of Near-infrared (NIR) and Fourier transform mid-infrared (ATR-FT\/MIR) Spectroscopy Techniques Combined with Chemometrics for the Determination of Crude Protein and Intestinal Protein Digestibility of Wheat","volume":"272","author":"Shi","year":"2019","journal-title":"Food Chem."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Shorten, P.R., Leath, S.R., Schmidt, J., and Ghamkhar, K. (2019). Predicting the Quality of Ryegrass Using Hyperspectral Imaging. (Report). Plant Methods, 15.","DOI":"10.1186\/s13007-019-0448-2"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.chemolab.2007.04.006","article-title":"Comparison of Linear and Nonlinear Calibration Models Based on Near Infrared (NIR) Spectroscopy Data for Gasoline Properties Prediction","volume":"88","author":"Balabin","year":"2007","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"2792","DOI":"10.3390\/ijgi4042792","article-title":"Estimating Plant Traits of Grasslands from UAV-acquired Hyperspectral Images: A Comparison of Statistical Approaches","volume":"4","author":"Capolupo","year":"2015","journal-title":"ISPRS Int. J. Geo Inf."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1016\/j.rse.2005.07.008","article-title":"Vegetation Water Content Estimation for Corn and Soybeans Using Spectral Indices Derived from MODIS Near- and Short-wave Infrared Bands","volume":"98","author":"Chen","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_53","unstructured":"Da Silva, C.R., Centeno, J.A.S., and Aranha, S.R. (2008, January 12\u201315). Reduction of the Dimensionality of Hyperspectral Data for the Classification of Agricultural Scenes. Proceedings of the 13th Symposium Deformation Measurements and Analysis, Lisbon, Portugal."},{"key":"ref_54","unstructured":"Burns, D.A., and Ciurczak, E.W. (2008). Application of NIR Spectroscopy to Agricultural Products. Handbook of Near-Infrared Analysis, CRC Press."},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1255\/jnirs.869","article-title":"Aquaphotomics: Dynamic spectroscopy of aqueous and biological systems describes peculiarities of water","volume":"17","author":"Tsenkova","year":"2009","journal-title":"J. Near Infrared Spectrosc."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"1955","DOI":"10.3168\/jds.S0022-0302(88)79766-0","article-title":"Potential of Near Infrared Reflectance Spectroscopy for Analysis of Silage Composition1,2,3","volume":"71","author":"Abrams","year":"1988","journal-title":"J. Dairy Sci."},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1111\/j.1469-8137.2010.03536.x","article-title":"Sources of Variability in Canopy Reflectance and the Convergent Properties of Plants","volume":"189","author":"Ollinger","year":"2011","journal-title":"New Phytol."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1255\/nirn.1079","article-title":"Aquaphotomics: The Extended Water Mirror Effect Explains Why Small Concentrations of Protein in Solution can be Measured with Near Infrared Light","volume":"19","author":"Tsenkova","year":"2008","journal-title":"Nir News"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Wijesingha, J., Astor, T., Schulze-Br\u00fcninghoff, D., Wengert, M., and Wachendorf, M. (2020). Predicting Forage Quality of Grasslands Using UAV-Borne Imaging Spectroscopy. Remote Sens., 12.","DOI":"10.3390\/rs12010126"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"7","DOI":"10.1255\/jnirs.134","article-title":"Prediction of phenolics and tannins in forage legumes by near infrared reflectance","volume":"6","author":"Goodchild","year":"1998","journal-title":"J. Near Infrared Spectrosc."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"452","DOI":"10.2111\/04-17.1","article-title":"Hyperspectral one-meter-resolution remote sensing in Yellowstone National Park, Wyoming: I","volume":"58","author":"Mirik","year":"2005","journal-title":"Forage nutritional values. Rangel. Ecol. Manag."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/6\/928\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:06:39Z","timestamp":1760173599000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/12\/6\/928"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,13]]},"references-count":61,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2020,3]]}},"alternative-id":["rs12060928"],"URL":"https:\/\/doi.org\/10.3390\/rs12060928","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,13]]}}}