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Research Article

New spectral indicator Potato Productivity Index based on Sentinel-2 data to improve potato yield prediction: a machine learning approach

ORCID Icon, , &
Pages 3426-3444 | Received 06 May 2020, Accepted 28 Nov 2020, Published online: 11 Feb 2021

References

  • Aien, A., S. Khetarpal, and M. Pal. 2011. “Photosynthetic Characteristics of Potato Cultivars Grown under High Temperature.” American–Eurasian Journal of Agricultural and Environmental Sciences 11 (5): 633–639.
  • Akhand, K., M. Nizamuddin, L. Roytman, and F. Kogan. 2016. “Using Remote Sensing Satellite Data and Artificial Neural Network for Prediction of Potato Yield in Bangladesh: In Remote Sensing and Modeling of Ecosystems for Sustainability.” International Society for Optics and Photonics XIII. 9975, 997508.
  • Al-Gaadi, K. A., A. A. Hassaballa, E. Tola, A. G. Kayad, R. Madugundu, B. Alblewi, and F. Assiri. 2016. “Prediction of Potato Crop Yield Using Precision Agriculture Techniques.” PloS One 11 (9): e0162219. doi:10.1371/journal.pone.0162219.
  • Allaire, S. E., A. N. Cambouris, J. A. Lafond, S. F. Lange, B. Pelletier, and P. Dutilleul. 2014. “Spatial Variability of Potato Tuber Yield and Plant Nitrogen Uptake Related to Soil Properties.” Agronomy Journal 106 (3): 851–859. doi:10.2134/agronj13.0468.
  • .
  • Bala, S. K., and A. S. Islam. 2009. “Correlation between Potato Yield and MODIS‐derived Vegetation Indices.” International Journal of Remote Sensing 30 (10): 2491–2507. doi:10.1080/01431160802552744.
  • Berkeley University 1997. Accessed at 05/ 12/2019 https://ucmp.berkeley.edu/glossary/gloss3/pigments.html
  • Blum, A. L., and P. Langley. 1997. “Selection of Relevant Features and Examples in Machine Learning.” Artificial Intelligence 97 (1–2): 245–271. doi:10.1016/S0004-3702(97)00063-5.
  • Bojacá, C. R., S. J. García, and E. Schrevens. 2011. “Analysis of Potato Canopy Coverage as Assessed through Digital Imagery by Nonlinear Mixed Effects Models.” Potato Research 54 (3): 237. doi:10.1007/s11540-011-9189-y.
  • Breiman, L. 2001. “Random Forests.” Machine Learning 45 (1): 5–32. doi:10.1023/A:1010933404324.
  • Brotosudarmo, T. H. P., L. Limantara, and R. D. Chandra. 2018. “Chloroplast Pigments: Structure, Function, Assembly and Characterization.” In Plant Growth and Regulation-Alterations to Sustain Unfavorable Conditions. IntechOpen. 3. doi:10.5772/intechopen.75672).
  • Brownlee, J. 2018. “A Gentle Introduction to k-Fold Cross-Validation.” Available online: accessed on 2 November 2018 https://machinelearningmastery.com/k-fold-cross-validation/
  • Chlingaryan, A., S. Sukkarieh, and B. Whelan. 2018. “Machine Learning Approaches for Crop Yield Prediction and Nitrogen Status Estimation in Precision Agriculture: A Review.” Computers and Electronics in Agriculture 151: 61–69. doi:10.1016/j.compag.2018.05.012.
  • Climate Data, 2020. Accessed at 10-12-2020 https://es.climate-data.org
  • Core Team, R. 2017. “R: A Language and Environment for Statistical Computing.” R Foundation for Statistical Computing, Vienna, Austria. Accessed at 04/ 05/2019 https://www.R-project.org/
  • Crafts-Brandner, S. J., and M. E. Salvucci. 2002. “Sensitivity of Photosynthesis in a C4 Plant, Maize, to Heat Stress.” Plant Physiology 129 (4): 1773–1780. doi:10.1104/pp.002170.
  • Croft, H., and J. M. Chen. 2017. “Leaf Pigment Content.” In Reference Module in Earth Systems and Environmental Sciences, 1–22. Elsevier Inc, Oxford .
  • Dabrowska-Zielinska, K., M. Bartold, R. Gurdak, M. Gatkowska, W. Kiryla, Z. Bochenek, and A. Malinska 2018. “Crop Yield Modelling Applying Leaf Area Index Estimated from Sentinel-2 and Proba-V Data at JECAM Site in Poland.” IGARSS 2018-2018 IEEE International Geoscience and Remote Sensing Symposium5382–5385. IEEE, Valencia (Spain).
  • Dahal, K., X. Q. Li, H. Tai, A. Creelman, and B. Bizimungu. 2019. “Improving Potato Stress Tolerance and Tuber Yield under a Climate Change Scenario–a Current Overview.” Frontiers in Plant Science 10: 563. doi:10.3389/fpls.2019.00563.
  • Das, B., G. R. Mahajan, and R. Singh. 2018. “Hyperspectral Remote Sensing: Use in Detecting Abiotic Stresses in Agriculture.” In Advances in Crop Environment Interaction, 317–335. Springer, Singapur.
  • Devaux, A., P. Kromann, and O. Ortiz. 2014. “Potatoes for Sustainable Global Food Security.” Potato Res 57 (3–4): 185–199. doi:10.1007/s11540-014-9265-1.
  • ESA -European Space Agency-, 2016. “MultiSpectral Instrument (MSI) Overview.” Sentinel Online. Accessed at 07- 07-2020 https://earth.esa.int/web/sentinel/technical-guides/sentinel-2-msi/msi-instrument
  • ESA -European Space Agency-, 2020. Accessed at 10-12-2020 https://scihub.copernicus.eu
  • ESRI 2014. “ArcGIS Desktop: Release 10.4” Environmental Systems Research Institute, Redlands, CA, USA.
  • European Space Agency - ESA. “Mission Sentinel 2.” overview. 2016. Accessed at 04/ 05/2019 https://sentinel.esa.int/web/sentinel/missions/sentinel-2
  • Eurostat, 2019. https://ec.europa.eu/eurostat/statistics-explained/index.php/The_EU_potato_sector_statistics_on_production,_prices_and_trade; https://ec.europa.eu/eurostat/statistics-explained/pdfscache/49931.pdf ( Accessed at 01/12/2019)
  • Fleisher, D. H., B. Condori, R. Quiroz, A. Alva, S. Asseng, C. Barreda, … P. M. Govindakrishnan. 2017. “A Potato Model Intercomparison across Varying Climates and Productivity Levels.” Global Change Biology 23 (3): 1258–1281. doi:10.1111/gcb.13411.
  • Frampton, W. J., J. Dash, G. Watmough, and E. J. Milton. 2013. “Evaluating the Capabilities of Sentinel-2 for Quantitative Estimation of Biophysical Variables in Vegetation.” ISPRS Journal of Photogrammetry and Remote Sensing 82: 83–92. doi:10.1016/j.isprsjprs.2013.04.007.
  • Gandhi, N., L. J. Armstrong, O. Petkar, and A. K. Tripathy 2016. “Rice Crop Yield Prediction in India Using Support Vector Machines.” 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE). Khon Kaen, Thailand, IEEE, 1–5.
  • Gitelson, A. A., G. P. Keydan, and M. N. Merzlyak. 2006. “Three-band Model for Noninvasive Estimation of Chlorophyll, Carotenoids, and Anthocyanin Contents in Higher Plant Leaves.” Geophysical Research Letters 33 (11). doi:10.1029/2006GL026457.
  • Gitelson, A. A., M. N. Merzlyak, and O. B. Chivkunova. 2001. “Optical Properties and Nondestructive Estimation of Anthocyanin Content in Plant Leaves.” Photochemistry and Photobiology 74 (1): 38–45. doi:10.1562/0031-8655(2001)074<0038:OPANEO>2.0.CO;2.
  • Gitelson, A. A., O. B. Chivkunova, and M. N. Merzlyak. 2009. “Nondestructive Estimation of Anthocyanins and Chlorophylls in Anthocyanic Leaves.” American Journal of Botany 96 (10): 1861–1868. doi:10.3732/ajb.0800395.
  • Gitelson, A. A., Y. J. Kaufman, and M. N. Merzlyak. 1996. “Use of a Green Channel in Remote Sensing of Global Vegetation from EOS-MODIS.” Remote Sensing of Environment 58 (3): 289–298. doi:10.1016/S0034-4257(96)00072-7.
  • Goel, P. K., S. O. Prasher, J. A. Landry, R. M. Patel, A. A. Viau, and J. R. Miller. 2003. “Estimation of Crop Biophysical Parameters through Airborne and Field Hyperspectral Remote Sensing.” Transactions of the ASAE 46 (4): 1235. doi:10.13031/2013.12942.
  • Gold, K. M., P. A. Townsend, I. Herrmann, and A. J. Gevens. 2019. “Investigating Potato Late Blight Physiological Differences across Potato Cultivars with Spectroscopy and Machine Learning.” Plant Science, 295, 110316.
  • Gómez, D., P. Salvador, J. Sanz, and J. L. Casanova. 2019. “Potato Yield Prediction Using Machine Learning Techniques and Sentinel 2 Data.” Remote Sensing 11 (15): 1745. doi:10.3390/rs11151745.
  • Gonçalves, J. F. D. C., R. A. Marenco, and G. Vieira. 2001. “Concentration of Photosynthetic Pigments and Chlorophyll Fluorescence of Mahogany and Tonka Bean under Two Light Environments.” Revista Brasileira De Fisiologia Vegetal 13 (2): 149–157. doi:10.1590/S0103-31312001000200004.
  • Gregory, and Marshall. 2012. “Attribution of Climate Change: A Methodology to Estimate the Potential Contribution to Increases in Potato Yield in Scotland since 1960.” Glob. Chang. Biol 18: 1372–1388. doi:10.1111/j.1365-2486.2011.02601.x.
  • Groten, S. M. E. 1993. “NDVI-crop Monitoring and Early Yield Assessment of Burkina Faso.” International Journal of Remote Sensing 14 (8): 1495–1515. doi:10.1080/01431169308953983.
  • Guyon, I., J. Weston, S. Barnhill, and V. Vapnik. 2002. “Gene Selection for Cancer Classification Using Support Vector Machines.” Machine Learning 46 (1–3): 389–422. doi:10.1023/A:1012487302797.
  • Harvard Forest 2019. https://harvardforest.fas.harvard.edu/leaves/pigment ( Accessed at 03/12/2019)
  • Haverkort, A. J. 2007. “Potato Crop Response to Radiation and Daylength.” In Potato Biology and Biotechnology, 353–365. Elsevier Science BV.
  • Hijmans, R. J., and J. van Etten. 2014. “Raster: Geographic Data Analysis and Modeling.” R Package Version 2 (8). Accessed at https://rdrr.io/cran/raster/f/inst/doc/rasterfile.pdf https://rdrr.io/cran/raster/f/inst/doc/rasterfile.pdf
  • Hsiao, T. C., E. Fereres, E. Acevedo, and D. W. Henderson. 1976. “Water Stress and Dynamics of Growth and Yield of Crop Plants.” In Water and Plant Life, 281–305. Berlin, Heidelberg: Springer.
  • Islam, A. S., and S. K. Bala. 2008. “Assessment of Potato Phenological Characteristics Using MODIS-derived NDVI and LAI Information.” GIScience & Remote Sensing 45 (4): 454–470. doi:10.2747/1548-1603.45.4.454.
  • ItaCyL, 2012. http://suelos.itacyl.es/visor_datos ( Accessed at 03- 07-2020)
  • JCyL—Junta de Castilla y Leon. 2015. Available online: accessed on 31 October 2018 October 31 http://www.jcyl.es/web/jcyl/AgriculturaGanaderia/es/Plantilla100Detalle/1246464862173/_/1284142623007/Comunicacion?plantillaObligatoria=PlantillaContenidoNoticiaHome
  • Jeong, J. H., J. P. Resop, N. D. Mueller, D. H. Fleisher, K. Yun, E. E. Butler, … S. H. Kim. 2016. “Random Forests for Global and Regional Crop Yield Predictions.” PLoS One 11 (6): e0156571. doi:10.1371/journal.pone.0156571.
  • Joachims, T. 1998. “Text Categorization with Support Vector Machines: Learning with Many Relevant Features.” European conference on machine learning, Springer, Berlin, Heidelberg,137–142.
  • Karanja, A. M., C. Shasanya, and G. Makokha. 2014. “Analysis of Rainfall Variability on Potato Production in Kenya: A Case of Oljoro-orok Division.” Asian Journal of Applied Sciences 2 (4): 447-456ISSN: 2321–0893.
  • Kasampalis, D. A., T. K. Alexandridis, C. Deva, A. Challinor, D. Moshou, and G. Zalidis. 2018. “Contribution of Remote Sensing on Crop Models: A Review.” Journal of Imaging 4 (4): 52. doi:10.3390/jimaging4040052.
  • Khan, Z., V. Rahimi-Eichi, S. Haefele, T. Garnett, and S. J. Miklavcic. 2018. “Estimation of Vegetation Indices for High-throughput Phenotyping of Wheat Using Aerial Imaging.” Plant Methods 14 (1): 20. doi:10.1186/s13007-018-0287-6.
  • Kuhn, M. 2008. “Building Predictive Models in R Using the Caret Package.” Journal of Statistical Software 28(5): 1–26. Available online: http://www.math.chalmers.se/Stat/Grundutb/GU/MSA220/S18/caret-JSS.pdf ( accessed on 10.18637/jss.v028.i05
  • Kuwata, K., and R. Shibasaki, 2015. “Estimating Crop Yields with Deep Learning and Remotely Sensed Data.”, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS) Milan, Italy, 858–861.
  • Liaghat, S., and S. K. Balasundram. 2010. “A Review: The Role of Remote Sensing in Precision Agriculture.” American Journal of Agricultural and Biological Sciences 5 (1): 50–55. doi:10.3844/ajabssp.2010.50.55.
  • Liakos, K. G., P. Busato, D. Moshou, S. Pearson, and D. Bochtis. 2018. “Machine Learning in Agriculture: A Review.” Sensors 18 (8): 2674. doi:10.3390/s18082674.
  • Lobell, D. B., and M. B. Burke. 2010. “On the Use of Statistical Models to Predict Crop Yield Responses to Climate Change.” Agricultural and Forest Meteorology 150 (11): 1443–1452. doi:10.1016/j.agrformet.2010.07.008.
  • Louis, J., V. Debaecker, B. Pflug, M. Main-Knorn, J. Bieniarz, U. Mueller-Wilm, … F. Gascon 2016. “Sentinel-2 Sen2cor: L2a Processor for Users.” Proceedings of the Living Planet Symposium, Prague, Czech Republic, 9–13. https://elib.dlr.de/107381/1/LPS2016_sm10_3louis.pdf ( Accessed at 15/ 04/2019)
  • MAPA - Spanish Ministry of Agriculture, Fishery and Food, 2009 Accessed at 10- 10-2015. https://www.mapa.gob.es/es/agricultura/publicaciones/Publicaciones-fertilizantes.aspx
  • Milavec, M., M. Kovac, and M. Ravnikar. 1999. “. Igor after Primary Infection with Potato Virus Y^ N^ T^ N.” PHYTON-HORN- 39 (3): 265–270.
  • Mkhabela, M. S., P. Bullock, S. Raj, S. Wang, and Y. Yang. 2011. “Crop Yield Forecasting on the Canadian Prairies Using MODIS NDVI Data.” Agricultural and Forest Meteorology 151 (3): 385–393. doi:10.1016/j.agrformet.2010.11.012.
  • Morales, D., P. Rodríguez, J. Dell’Amico, E. Nicolas, A. Torrecillas, and M. J. Sánchez-Blanco. 2003. “High-temperature Preconditioning and Thermal Shock Imposition Affects Water Relations, Gas Exchange and Root Hydraulic Conductivity in Tomato.” Biologia Plantarum 47 (2): 203. doi:10.1023/B:BIOP.0000022252.70836.fc.
  • Mulla, D. J. 2013. “Twenty Five Years of Remote Sensing in Precision Agriculture: Key Advances and Remaining Knowledge Gaps.” Biosystems Engineering 114 (4): 358–371. doi:10.1016/j.biosystemseng.2012.08.009.
  • Muthoni, J., D. N. Nyamongo, and M. Mbiyu. 2017. “Climatic Change, Its Likely Impact on Potato (Solanum Tuberosum L.) Production in Kenya and Plausible Coping Measures.” International Journal of Horticulture 7. doi:10.5376/ijh.2017.07.0014.
  • Newton, I. H., A. M. T. Islam, A. S. Islam, G. T. Islam, A. Tahsin, and S. Razzaque. 2018. “Yield Prediction Model for Potato Using Landsat Time Series Images Driven Vegetation Indices.” Remote Sensing in Earth Systems Sciences 1 (1–2): 29–38. doi:10.1007/s41976-018-0006-0.
  • Peñuelas, J., I. Filella, C. Biel, L. Serrano, and R. Save. 1993. “The Reflectance at the 950–970 Nm Region as an Indicator of Plant Water Status.” International Journal of Remote Sensing 14 (10): 1887–1905. doi:10.1080/01431169308954010.
  • Peñuelas, J., I. Filella, L. Serrano, and R. Save. 1996. “Cell Wall Elasticity and Water Index (R970 nm/R900 Nm) in Wheat under Different Nitrogen Availabilities.” International Journal of Remote Sensing 17 (2): 373–382. doi:10.1080/01431169608949012.
  • Pobereżny, J., E. Wszelaczyńska, D. Wichrowska, and D. Jaskulski. 2015. “Content of Nitrates in Potato Tubers Depending on the Organic Matter, Soil Fertilizer, Cultivation Simplifications Applied and Storage.” Chilean Journal of Agricultural Research 75 (1): 42–49.
  • Pu, R., S. Ge, N. M. Kelly, and P. Gong. 2003. “Spectral Absorption Features as Indicators of Water Status in Coast Live Oak (Quercus Agrifolia) Leaves.” International Journal of Remote Sensing 24 (9): 1799–1810. doi:10.1080/01431160210155965.
  • Ray, S. S., G. Das, J. P. Singh, and S. Panigrahy. 2006. “Evaluation of Hyperspectral Indices for LAI Estimation and Discrimination of Potato Crop under Different Irrigation Treatments.” International Journal of Remote Sensing 27 (24): 5373–5387. doi:10.1080/01431160600763006.
  • Raymundo, R., S. Asseng, D. Cammarano, and R. Quiroz. 2014. “Potato, Sweet Potato, and Yam Models for Climate Change: A Review.” Field Crops Research 166: 173–185. doi:10.1016/j.fcr.2014.06.017.
  • Reynolds, M. P., E. E. Ewing, and T. G. Owens. 1990. “Photosynthesis at High Temperature in Tuber-bearing Solanum Species: A Comparison between Accessions of Contrasting Heat Tolerance.” Plant Physiology 93 (2): 791–797. doi:10.1104/pp.93.2.791.
  • Romero, A. P., A. Alarcón, R. I. Valbuena, and C. H. Galeano. 2017. “Physiological Assessment of Water Stress in Potato Using Spectral Information.” Frontiers in Plant Science 8: 1608. doi:10.3389/fpls.2017.01608.
  • Rouse, J. W., R. H. Haas, J. A. Schell, and D. W. Deering, 1974. “Monitoring Vegetation Systems in the Great Plains with ERTS” In Third Earth Resources Technology Satellite–1 Symposium. Volume I: Technical Presentations edited by S. C. Freden, E. P. Mercanti, and M. Becker, 309–317. Washington, D.C.: NASA SP-351, NASA.
  • Saeed, U., J. Dempewolf, I. Becker-Reshef, A. Khan, A. Ahmad, and S. A. Wajid. 2017. “Forecasting Wheat Yield from Weather Data and MODIS NDVI Using Random Forests for Punjab Province, Pakistan.” International Journal of Remote Sensing 38 (17): 4831–4854. doi:10.1080/01431161.2017.1323282.
  • Saue, T., and J. Kadaja. 2014. “Water Limitations on Potato Yield in Estonia Assessed by Crop Modelling.” Agricultural and Forest Meteorology 194: 20–28. doi:10.1016/j.agrformet.2014.03.012.
  • Serrano, J., S. Shahidian, and J. Marques da Silva. 2019. “Evaluation of Normalized Difference Water Index as a Tool for Monitoring Pasture Seasonal and Inter-annual Variability in a Mediterranean Agro-silvo-pastoral System.” Water 11 (1): 62. doi:10.3390/w11010062.
  • Slatyer, R. O., and D. K. Markus. 1968. “Plant-water Relationships.” Soil Science 106 (6): 478. doi:10.1097/00010694-196812000-00020.
  • Souza, R., R. Grasso, M. T. Peña-Fleitas, M. Gallardo, R. B. Thompson, and F. M. Padilla. 2020. “Effect of Cultivar on Chlorophyll Meter and Canopy Reflectance Measurements in Cucumber.” Sensors 20 (2): 509. doi:10.3390/s20020509.
  • Šutić, D. D., and J. B. Sinclair 1991. Anatomy and Physiology of Diseased Plants. CRC press, Boca Raton, Florida. ISBN: 0849348064
  • Statista. 2017. Available online: accessed on 2 May 2019 May 2 https://es.statista.com/estadisticas/510906/produccion-de-patatas-en-espana-por-comunidad-autonoma/
  • Sudmanns, M., D. Tiede, S. Lang, H. Bergstedt, G. Trost, H. Augustin, … T. Blaschke. 2020. “Big Earth Data: Disruptive Changes in Earth Observation Data Management and Analysis?” International Journal of Digital Earth, 13(7), 832–850.
  • Tarpley, J. D., S. R. Schneider, and R. L. Money. 1984. “Global Vegetation Indices from the NOAA-7 Meteorological Satellite.” Journal of Climate and Applied Meteorology 23 (3): 491–494. doi:10.1175/1520-0450(1984)023<0491:GVIFTN>2.0.CO;2.
  • Thenkabail, P. S., R. B. Smith, and E. De Pauw. 2000. “Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics.” Remote Sensing of Environment 71 (2): 158–182. doi:10.1016/S0034-4257(99)00067-X.
  • Urban, J., M. Ingwers, M. A. McGuire, and R. O. Teskey. 2017. “Stomatal Conductance Increases with Rising Temperature.” Plant Signaling & Behavior 12 (8): e1356534. doi:10.1080/15592324.2017.1356534.
  • Van Oort, P. A. J., B. G. H. Timmermans, H. Meinke, and M. K. Van Ittersum. 2012. “Key Weather Extremes Affecting Potato Production in the Netherlands.” European Journal of Agronomy 37 (1): 11–22. doi:10.1016/j.eja.2011.09.002.
  • Vapnik, V. 2000. The Nature of Statistical Learning Theory. Springer-Verlag New York. doi: 10.1007/978-1-4757-3264-1
  • Whelan, B., and J. Taylor. 2013. Precision Agriculture for Grain Production Systems. Csiro publishing, Clayton, Victoria. doi:10.1071/9780643107489.
  • Wurr, D. C. E., J. R. Fellows, J. R. Lynn, and E. J. Allen. 1993. “The Impact of Some Agronomic Factors on the Variability of Potato Tuber Size Distribution.” Potato Research 36 (3): 237–245. doi:10.1007/BF02360532.
  • Xie, Q., J. Dash, A. Huete, A. Jiang, G. Yin, Y. Ding, … H. Ye. 2019. “Retrieval of Crop Biophysical Parameters from Sentinel-2 Remote Sensing Imagery.” International Journal of Applied Earth Observation and Geoinformation 80: 187–195. doi:10.1016/j.jag.2019.04.019.
  • Zarco-Tejada, P. J., J. A. Berni, L. Suárez, and E. Fereres (2008). “A New Era in Remote Sensing of Crops with Unmanned Robots.” SPIE Newsroom, 2–4.
  • Zhang, P., B. Anderson, B. Tan, M. Barlow, and R. Myneni 2010. “Monitoring Crop Yield in USA Using a Satellite-based Climate-variability Impact Index.” 2010 IEEE International Geoscience and Remote Sensing Symposium, IEEE, Honolulu, Hawaii 1815–1818.
  • Zhou, X., W. Huang, W. Kong, H. Ye, Y. Dong, and R. Casa. 2017. “Assessment of Leaf Carotenoids Content with a New Carotenoid Index: Development and Validation on Experimental and Model Data.” International Journal of Applied Earth Observation and Geoinformation 57: 24–35. doi:10.1016/j.jag.2016.12.005.
  • Zhou, Z., J. Morel, D. Parsons, S. V. Kucheryavskiy, and A. M. Gustavsson. 2019. “Estimation of Yield and Quality of Legume and Grass Mixtures Using Partial Least Squares and Support Vector Machine Analysis of Spectral Data.” Computers and Electronics in Agriculture 162: 246–253. doi:10.1016/j.compag.2019.03.038.

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