Data processing to remove outliers and inliers: A systematic literature study

Alves, Fernando; Souza, Eduardo G. de; Sobjak, Ricardo; Bazzi, Claudio L.; Hachisuca, Antonio M. M.; Mercante, Erivelto

doi:10.1590/1807-1929/agriambi.v28n9e278672

Fernando Alves

Systematic literature study

Resources

Formal analysis

Writing - original draft

Instituto Federal do Paraná/Departamento de Ciência da Computação, Cascavel, PR, Brazil

https://orcid.org/0000-0001-7928-180X

Eduardo G. de Souza ^** Corresponding author - E-mail: eduardo.souza@unioeste.br

Conceptualization

Methodology

Writing - review & editing

Universidade Estadual do Oeste do Paraná/Centro de Ciências Exatas e Tecnológicas/Pós-Graduação em Engenharia Agrícola, Cascavel, PR, Brazil

https://orcid.org/0000-0003-3029-8939

Ricardo Sobjak

Formal analysis

Writing - review & editing

Universidade Tecnológica Federal do Paraná/Departamento de Ciência da Computação, Medianeira, PR, Brazil

https://orcid.org/0000-0001-5652-5840

Claudio L. Bazzi

Methodology

Writing - review & editing

Universidade Tecnológica Federal do Paraná/Departamento de Ciência da Computação, Medianeira, PR, Brazil

https://orcid.org/0000-0002-9009-5562

Antonio M. M. Hachisuca

Resources

Investigation

Universidade Estadual do Oeste do Paraná/Centro de Ciências Exatas e Tecnológicas/Pós-Graduação em Engenharia Agrícola, Cascavel, PR, Brazil

https://orcid.org/0000-0003-2386-8059

Erivelto Mercante

Resources

Writing - review & editing

Universidade Estadual do Oeste do Paraná/Centro de Ciências Exatas e Tecnológicas/Pós-Graduação em Engenharia Agrícola, Cascavel, PR, Brazil

https://orcid.org/0000-0002-0744-5286

* Corresponding author - E-mail: eduardo.souza@unioeste.br

Editors: Toshik Iarley da Silva & Carlos Alberto Vieira de Azevedo

Technique	Studies
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Organic farming and greenhouse gas emissions: A longitudinal U.S. state-level study. Journal of Cleaner Production, v.192, p.30-42, 2018. https://doi.org/10.1016/j.jclepro.2018.04.160 https://doi.org/10.1016/j.jclepro.2018.0... ); (Sanches et al., 2018); (Altdorff et al., 2018Altdorff, D.; Galagedara, L.; Nadeem, M.; Cheema, M.; Unc, A. Effect of agronomic treatments on the accuracy of soil moisture mapping by electromagnetic induction. Catena, v.164, p.96-106, 2018. https://doi.org/10.1016/j.catena.2017.12.036 https://doi.org/10.1016/j.catena.2017.12... ); (Behera et al., 2018Behera, S. K.; Mathur, R. K.; Shukla, A. K.; Suresh, K.; Prakash, C. Spatial variability of soil properties and delineation of soil management zones of oil palm plantations grown in a hot and humid tropical region of southern India. 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Spatial variability in orchards after land transformation: Consequences for precision agriculture practices. Science of The Total Environment , v.635, p.343-352, 2018. https://doi.org/10.1016/j.scitotenv.2018.04.153 https://doi.org/10.1016/j.scitotenv.2018... ); (Hong et al., 2019Hong, Y.; Liu, Y. Y. Y.; Chen, Y.; Liu, Y. Y. Y.; Yu, L.; Liu, Y. Y. Y.; Cheng, H. Application of fractional-order derivative in the quantitative estimation of soil organic matter content through visible and near-infrared spectroscopy. Geoderma , v.337, p.758-769, 2019. https://doi.org/10.1016/j.geoderma.2018.10.025 https://doi.org/10.1016/j.geoderma.2018.... ); (Barca et al., 2019Barca, E.; De Benedetto, D.; Stellacci, A. M. Contribution of EMI and GPR proximal sensing data in soil water content assessment by using linear mixed effects models and geostatistical approaches. 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S. G. Computational routines for the automatic selection of the best parameters used by interpolation methods to create thematic maps. Computers and Electronics in Agriculture, v.157, p.49-62, 2019. https://doi.org/10.1016/j.compag.2018.12.004 https://doi.org/10.1016/j.compag.2018.12... ); (González-Fernández et al., 2019González-Fernández, A. B.; Rodríguez-Pérez, J. R.; Sanz-Ablanedo, E.; Valenciano, J. B.; Marcelo, V. Delineating vineyard zones by fuzzy k-means algorithm based on grape sampling variables. Scientia Horticulturae, v.243, p.559-566, 2019. https://doi.org/10.1016/j.scienta.2018.09.012 https://doi.org/10.1016/j.scienta.2018.0... ); (Liu et al., 2019Liu, X.; Guanter, L.; Liu, L.; Damm, A.; Malenovský, Z.; Rascher, U.; Peng, D.; Du, S.; Gastellu-Etchegorry, J.-P. Downscaling of solar-induced chlorophyll fluorescence from canopy level to photosystem level using a random forest model. Remote Sensing of Environment , v.231, 110772, 2019. https://doi.org/10.1016/ j.rse.2018.05.035 https://doi.org/10.1016/ j.rse.2018.05.0... ); (Gavioli et al., 2019Gavioli, A.; De Souza, E. G.; Bazzi, C. L.; Schenatto, K.; Betzek, N. M. Identification of management zones in precision agriculture: An evaluation of alternative cluster analysis methods. Biosystems Engineering , v.181, p.86-102, 2019. https://doi.org/10.1016/j.biosystemseng.2019.02.019 https://doi.org/10.1016/j.biosystemseng.... ); (Araujo et al., 2019Araujo, V.; Mitra, K.; Saguna, S.; Åhlund, C. Performance evaluation of FIWARE: A cloud-based IoT platform for smart cities. Journal of Parallel and Distributed Computing, v.132, p.250-261, 2019. https://doi.org/10.1016/j.jpdc.2018.12.010 https://doi.org/10.1016/j.jpdc.2018.12.0... ); (Ali et al., 2019Ali, M.; Deo, R. C.; Maraseni, T.; Downs, N. J. Improving SPI-derived drought forecasts incorporating synoptic-scale climate indices in multi-phase multivariate empirical mode decomposition model hybridized with simulated annealing and kernel ridge regression algorithms. Journal of Hydrology, v.576, p.164-184, 2019. https://doi.org/10.1016/j.jhydrol.2019.06.032 https://doi.org/10.1016/j.jhydrol.2019.0... ); (Moura-Bueno et al., 2019Moura-Bueno, J. M.; Dalmolin, R. S. D.; Ten Caten, A.; Dotto, A. C.; Demattê, J. A. M. M. Stratification of a local VIS-NIR-SWIR spectral library by homogeneity criteria yields more accurate soil organic carbon predictions. Geoderma , v.337, p.565-581, 2019. https://doi.org/10.1016/j.geoderma.2018.10.015 https://doi.org/10.1016/j.geoderma.2018.... ); (Prasad et al., 2019Prasad, R.; Deo, R. C.; Li, Y.; Maraseni, T. Weekly soil moisture forecasting with multivariate sequential, ensemble empirical mode decomposition and Boruta-random forest hybridizer algorithm approach. Catena , v.177, p.149-166, 2019. https://doi.org/10.1016/j.catena.2019.02.012 https://doi.org/10.1016/j.catena.2019.02... ); (Shaddad et al., 2019Shaddad, S. M.; Buttafuoco, G.; Elrys, A.; Castrignanò, A. Site-specific management of salt-affected soils: A case study from Egypt. Science of The Total Environment , v.688, p.153-161, 2019. https://doi.org/10.1016/j.scitotenv.2019.06.214 https://doi.org/10.1016/j.scitotenv.2019... ); (Sanches et al., 2019Sanches, G. M.; Graziano Magalhães, P. S.; Junqueira Franco, H. C. Site-specific assessment of spatial and temporal variability of sugarcane yield related to soil attributes. Geoderma , v.334, p.90-98, 2019. https://doi.org/10.1016/j.geoderma.2018.07.051 https://doi.org/10.1016/j.geoderma.2018.... ); (Uribeetxebarria et al., 2019Uribeetxebarria, A.; Martínez-Casasnovas, J. A.; Tisseyre, B.; Guillaume, S.; Escolà, A.; Rosell-Polo, J. R.; Arnó, J. Assessing ranked set sampling and ancillary data to improve fruit load estimates in peach orchards. Computers and Electronics in Agriculture , v.164, 104931, 2019. https://doi.org/10.1016/j.compag.2019.104931 https://doi.org/10.1016/j.compag.2019.10... ); (Mura et al., 2019Mura, S.; Cappai, C.; Greppi, G. F.; Barzaghi, S.; Stellari, A.; Cattaneo, T. M. P. Vibrational spectroscopy and Aquaphotomics holistic approach to determine chemical compounds related to sustainability in soil profiles. Computers and Electronics in Agriculture , v.159, p.92-96, 2019. https://doi.org/10.1016/j.compag.2019.03.002 https://doi.org/10.1016/j.compag.2019.03... ); (Vega et al., 2019Vega, A.; Córdoba, M.; Castro-Franco, M.; Balzarini, M. Protocol for automating error removal from yield maps. Precision Agriculture , v.20, p.1030-1044, 2019. https://doi.org/10.1007/s11119-018-09632-8 https://doi.org/10.1007/s11119-018-09632... )
BSB	(Arslan & Colvin, 2002Arslan, S.; Colvin, T. S. Grain yield mapping: yield sensing, yield reconstruction, and errors. Precision Agriculture, v.3, p.135-154, 2002. https://doi.org/10.1023/A:1013819502827 https://doi.org/10.1023/A:1013819502827... ); (Yield et al., 2004); (Menegatti & Molin, 2004Menegatti, L. A. A.; Molin, J. P. Remoção de erros em mapas de produtividade via filtragem de dados brutos. Revista Brasileira de Engenharia Agrícola e Ambiental , v.8, p.126-134, 2004. https://doi.org/10.1590/s1415-43662004000100019 https://doi.org/10.1590/s1415-4366200400... ); (Amidan et al., 2005Amidan, B. G.; Ferryman, T. A.; Cooley, S. K. Data outlier detection using the Chebyshev theorem. IEEE Aerospace Conference Proceedings, v.2005, p.3-8, 2005. https://doi.org/10.1109/AERO.2005.1559688 https://doi.org/10.1109/AERO.2005.155968... ); (Ping & Dobermann, 2005Ping, J. L.; Dobermann, A. Processing of yield map data. Precision Agriculture , v.6, p.193-212, 2005. https://doi.org/10.1007/s11119-005-1035-2 https://doi.org/10.1007/s11119-005-1035-... ); (Dray et al., 2006Dray, S.; Legendre, P.; Peres-Neto, P. R. Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (PCNM). Ecological Modelling, v.196, p.483-493, 2006. https://doi.org/10.1016/j.ecolmodel.2006.02.015 https://doi.org/10.1016/j.ecolmodel.2006... ); (Taylor et al., 2007Taylor, J. A. A.; McBratney, A. B. B.; Whelan, B. M. M. Establishing management classes for broadacre agricultural production. Agronomy Journal, v.99, p.1366-1376, 2007. https://doi.org/10.2134/agronj2007.0070 https://doi.org/10.2134/agronj2007.0070... ); (Sudduth & Drummond, 2007Sudduth, K. A.; Drummond, S. T. Yield Editor: Software for Removing Errors from Crop Yield Maps. Agronomy Journal, v.99, p.1471-1482, 2007. https://doi.org/10.2134/agronj2006.0326 https://doi.org/10.2134/agronj2006.0326... ); (Maldaner & Molin, 2020Maldaner, L. F.; Molin, J. P. Data processing within rows for sugarcane yield mapping. Scientia Agricola, v.77, p.1-8, 2020. https://doi.org/10.1590/1678-992x-2018-0391 https://doi.org/10.1590/1678-992x-2018-0... ); (Paccioretti et al., 2020Paccioretti, P.; Córdoba, M.; Balzarini, M. FastMapping: Software to create field maps and identify management zones in precision agriculture. Computers and Electronics in Agriculture , v.175, 105556, 2020. https://doi.org/10.1016/j.compag.2020.105556 https://doi.org/10.1016/j.compag.2020.10... )
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Technique	N	Studies
μ ± 1.5σ	1	(Gerstmann et al., 2016Gerstmann, H.; Doktor, D.; Glässer, C.; Möller, M. Phase: A geostatistical model for the kriging-based spatial prediction of crop phenology using public phenological and climatological observations. Computers and Electronics in Agriculture , v.127, p.726-738, 2016. https://doi.org/10.1016/j.compag.2016.07.032 https://doi.org/10.1016/j.compag.2016.07... )
μ ± 2σ	4	(Pei et al., 2010Pei, T.; Qin, C.-Z.; Zhu, A.-X.; Yang, L.; Luo, M.; Li, B.; Zhou, C. Mapping soil organic matter using the topographic wetness index: A comparative study based on different flow-direction algorithms and kriging methods. Ecological Indicators, v.10, p.610-619, 2010. https://doi.org/10.1016/j.ecolind.2009.10.005 https://doi.org/10.1016/j.ecolind.2009.1... ); (Gianquinto et al., 2011Gianquinto, G.; Orsini, F.; Fecondini, M.; Mezzetti, M.; Sambo, P.; Bona, S. A methodological approach for defining spectral indices for assessing tomato nitrogen status and yield. European Journal of Agronomy, v.35, p.135-143, 2011. https://doi.org/10.1016/j.eja.2011.05.005 https://doi.org/10.1016/j.eja.2011.05.00... ); (Chung et al., 2013Chung, S. O.; Sudduth, K. A.; Motavalli, P. P.; Kitchen, N. R. Relating mobile sensor soil strength to penetrometer cone index. Soil and Tillage Research , v.129, p.9-18, 2013. https://doi.org/10.1016/j.still.2012.12.004 https://doi.org/10.1016/j.still.2012.12.... ); (Rosemary et al., 2017Rosemary, F.; Vitharana, U. W. A.; Indraratne, S. P.; Weerasooriya, R.; Mishra, U. Exploring the spatial variability of soil properties in an Alfisol soil. Catena , v.150, p.53-61, 2017. https://doi.org/10.1016/j.catena.2016.10.017 https://doi.org/10.1016/j.catena.2016.10... )
μ ± 2.5σ	6	(Squalli & Adamkiewicz, 2018Squalli, J.; Adamkiewicz, G. Organic farming and greenhouse gas emissions: A longitudinal U.S. state-level study. Journal of Cleaner Production, v.192, p.30-42, 2018. https://doi.org/10.1016/j.jclepro.2018.04.160 https://doi.org/10.1016/j.jclepro.2018.0... ); (Castrignanò et al., 2012Castrignanò, A.; Wong, M. T. F. T. F.; Stelluti, M.; De Benedetto, D.; Sollitto, D. Use of EMI, gamma-ray emission and GPS height as multi-sensor data for soil characterisation. Geoderma , v.175, p.78-89, 2012. https://doi.org/10.1016/j.geoderma.2012.01.013 https://doi.org/10.1016/j.geoderma.2012.... ); (Cavallo et al., 2016Cavallo, G.; De Benedetto, D.; Castrignanò, A.; Quarto, R.; Vonella, A. V.; Buttafuoco, G. Use of geophysical data for assessing 3D soil variation in a durum wheat field and their association with crop yield. Biosystems Engineering, v.152, p.28-40, 2016. https://doi.org/10.1016/j.biosystemseng.2016.07.002 https://doi.org/10.1016/j.biosystemseng.... ); (Uribeetxebarria et al., 2018Uribeetxebarria, A.; Daniele, E.; Escolà, A.; Arnó, J.; Martínez-Casasnovas, J. A. Spatial variability in orchards after land transformation: Consequences for precision agriculture practices. Science of The Total Environment , v.635, p.343-352, 2018. https://doi.org/10.1016/j.scitotenv.2018.04.153 https://doi.org/10.1016/j.scitotenv.2018... ); (Uribeetxebarria et al., 2019Uribeetxebarria, A.; Martínez-Casasnovas, J. A.; Tisseyre, B.; Guillaume, S.; Escolà, A.; Rosell-Polo, J. R.; Arnó, J. Assessing ranked set sampling and ancillary data to improve fruit load estimates in peach orchards. Computers and Electronics in Agriculture , v.164, 104931, 2019. https://doi.org/10.1016/j.compag.2019.104931 https://doi.org/10.1016/j.compag.2019.10... ); (Coelho et al., 2018Coelho, A. L. De F.; De Queiroz, D. M.; Valente, D. S. M.; Pinto, F. De A. De C. An open-source spatial analysis system for embedded systems. Computers and Electronics in Agriculture , v.154, p.289-295, 2018. https://doi.org/10.1016/j.compag.2018.09.019 https://doi.org/10.1016/j.compag.2018.09... )
μ ± 3σ	9	(Simbahan et al., 2004Simbahan, G. C.; Dobermann, A.; Ping, J. L. Screening yield monitor data improves grain yield maps. Agronomy Journal, v.96, p.1091-1102, 2004. https://doi.org/10.2134/agronj2004.1091 https://doi.org/10.2134/agronj2004.1091... ); (Liu et al., 2009Liu, X.; Zhang, W.; Zhang, M.; Ficklin, D. L.; Wang, F. Spatio-temporal variations of soil nutrients influenced by an altered land tenure system in China. Geoderma , v.152, p.23-34, 2009. https://doi.org/10.1016/j.geoderma.2009.05.022 https://doi.org/10.1016/j.geoderma.2009.... ); (Haghverdi et al., 2015Haghverdi, A.; Leib, B. G.; Washington-Allen, R. A.; Ayers, P. D.; Buschermohle, M. J. Perspectives on delineating management zones for variable rate irrigation. Computers and Electronics in Agriculture , v.117, p.154-167, 2015. https://doi.org/10.1016/j.compag.2015.06.019 https://doi.org/10.1016/j.compag.2015.06... ); (Landrum et al., 2015Landrum, C.; Castrignanò, A.; Mueller, T.; Zourarakis, D.; Zhu, J.; De Benedetto, D. An approach for delineating homogeneous within-field zones using proximal sensing and multivariate geostatistics. Agricultural Water Management , v.147, p.144-153, 2015. https://doi.org/10.1016/j.agwat.2014.07.013 https://doi.org/10.1016/j.agwat.2014.07.... ); (Córdoba et al., 2016Córdoba, M. A.; Bruno, C. I.; Costa, J. L.; Peralta, N. R.; Balzarini, M. G. Protocol for multivariate homogeneous zone delineation in precision agriculture. Biosystems Engineering , v.143, p.95-107, 2016. https://doi.org/10.1016/j.biosystemseng.2015.12.008 https://doi.org/10.1016/j.biosystemseng.... ); (Driemeier et al., 2016Driemeier, C.; Ling, L. Y.; Sanches, G. M.; Pontes, A. O.; Magalhães, P. S. G. G.; Ferreira, J. E. A computational environment to support research in sugarcane agriculture. Computers and Electronics in Agriculture , v.130, p.13-19, 2016. https://doi.org/10.1016/j.compag.2016.10.002 https://doi.org/10.1016/j.compag.2016.10... ); (Li et al., 2016Li, Q.; Luo, Y.; Wang, C.; Li, B.; Zhang, X.; Yuan, D.; Gao, X.; Zhang, H. Spatiotemporal variations and factors affecting soil nitrogen in the purple hilly area of Southwest China during the 1980s and the 2010s. Science of The Total Environment, v.547, p.173-181, 2016. https://doi.org/10.1016/j.scitotenv.2015.12.094 https://doi.org/10.1016/j.scitotenv.2015... ); (Bodner & Robles, 2017Bodner, G. S.; Robles, M. D. Enduring a decade of drought: Patterns and drivers of vegetation change in a semi-arid grassland. Journal of Arid Environments, v.136, p.1-14, 2017. https://doi.org/10.1016/j.jaridenv.2016.09.002 https://doi.org/10.1016/j.jaridenv.2016.... ); (Sanches et al., 2018); (Betzek et al., 2019Betzek, N. M.; Souza, E. G. De; Bazzi, C. L.; Schenatto, K.; Gavioli, A.; Magalhães, P. S. G. Computational routines for the automatic selection of the best parameters used by interpolation methods to create thematic maps. Computers and Electronics in Agriculture, v.157, p.49-62, 2019. https://doi.org/10.1016/j.compag.2018.12.004 https://doi.org/10.1016/j.compag.2018.12... ); (Sanches et al., 2019Sanches, G. M.; Graziano Magalhães, P. S.; Junqueira Franco, H. C. Site-specific assessment of spatial and temporal variability of sugarcane yield related to soil attributes. Geoderma , v.334, p.90-98, 2019. https://doi.org/10.1016/j.geoderma.2018.07.051 https://doi.org/10.1016/j.geoderma.2018.... ); (Vega et al., 2019Vega, A.; Córdoba, M.; Castro-Franco, M.; Balzarini, M. Protocol for automating error removal from yield maps. Precision Agriculture , v.20, p.1030-1044, 2019. https://doi.org/10.1007/s11119-018-09632-8 https://doi.org/10.1007/s11119-018-09632... )
Boxplot	26	(Eugster et al., 2010Eugster, W.; Moffat, A. M.; Ceschia, E.; Aubinet, M.; Ammann, C.; Osborne, B.; Davis, P. A.; Smith, P.; Jacobs, C.M.J.; Moors, E.J.; Dantec, V.; Béziat, P.; Saunders, M.; Jans, W. W. P.; Grünwald, T.; Rebmann, C.; Kutsch, W. L.; Czerný, R.; Janouš, Dalibor; Moureaux, C.; Buchmann, N. Management effects on European cropland respiration. Agriculture, Ecosystems & Environment v.139, p.346-362, 2010. https://doi.org/10.1016/j.agee.2010.09.001 https://doi.org/10.1016/j.agee.2010.09.0... ); (Tesfahunegn et al., 2011Tesfahunegn, G. B.; Tamene, L.; Vlek, P. L. G. Catchment-scale spatial variability of soil properties and implications on site-specific soil management in northern Ethiopia. Soil and Tillage Research , v.117, p.124-139, 2011. https://doi.org/10.1016/j.still.2011.09.005 https://doi.org/10.1016/j.still.2011.09.... ); (Henriques et al., 2012Henriques, R.; Bacao, F.; Lobo, V. Exploratory geospatial data analysis using the GeoSOM suite. Computers, Environment and Urban Systems, v.36, p.218-232, 2012. https://doi.org/10.1016/j.compenvurbsys.2011.11.003 https://doi.org/10.1016/j.compenvurbsys.... ); (Li et al., 2012Li, X.; Lee, W. S.; Li, M.; Ehsani, R.; Mishra, A. R.; Yang, C.; Mangan, R. L. Spectral difference analysis and airborne imaging classification for citrus greening infected trees. Computers and Electronics in Agriculture , v.83, p.32-46, 2012. https://doi.org/10.1016/j.compag.2012.01.010 https://doi.org/10.1016/j.compag.2012.01... ); (Eitel et al., 2014Eitel, J. U. H.; Magney, T. S.; Vierling, L. A.; Dittmar, G. Assessment of crop foliar nitrogen using a novel dual-wavelength laser system and implications for conducting laser-based plant physiology. ISPRS Journal of Photogrammetry and Remote Sensing, v.97, p.229-240, 2014. https://doi.org/10.1016/j.isprsjprs.2014.09.009 https://doi.org/10.1016/j.isprsjprs.2014... ); (Kanellopoulos et al., 2014Kanellopoulos, A.; Reidsma, P.; Wolf, J.; Van Ittersum, M. K. Assessing climate change and associated socio-economic scenarios for arable farming in the Netherlands: An application of benchmarking and bio-economic farm modelling. European Journal of Agronomy , v.52, p.69-80, 2014. https://doi.org/10.1016/j.eja.2013.10.003 https://doi.org/10.1016/j.eja.2013.10.00... ); (Oliver & Webster, 2014Oliver, M. A.; Webster, R. A tutorial guide to geostatistics: Computing and modelling variograms and kriging. Catena , v.113, p.56-69, 2014. https://doi.org/10.1016/j.catena.2013.09.006 https://doi.org/10.1016/j.catena.2013.09... ); (Wang et al., 2015Wang, N.; Jassogne, L.; Van Asten, P. J. A.; Mukasa, D.; Wanyama, I.; Kagezi, G.; Giller, K. E. Evaluating coffee yield gaps and important biotic, abiotic, and management factors limiting coffee production in Uganda. European Journal of Agronomy , v.63, p.1-11, 2015. https://doi.org/10.1016/j.eja.2014.11.003 https://doi.org/10.1016/j.eja.2014.11.00... ); (Castaldi et al., 2016Castaldi, F.; Palombo, A.; Santini, F.; Pascucci, S.; Pignatti, S.; Casa, R. Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon. Remote Sensing of Environment, v.179, p.54-65, 2016. https://doi.org/10.1016/j.rse.2016.03.025 https://doi.org/10.1016/j.rse.2016.03.02... ); (Jordanova et al., 2016Jordanova, N.; Jordanova, D.; Petrov, P. Soil magnetic properties in Bulgaria at a national scale-Challenges and benefits. Global and Planetary Change, v.137, p.107-122, 2016. https://doi.org/10.1016/j.gloplacha.2015.12.015 https://doi.org/10.1016/j.gloplacha.2015... ); (Pelosi et al., 2016Pelosi, A.; Medina, H.; Villani, P.; D’urso, G.; Chirico, G. B. Probabilistic forecasting of reference evapotranspiration with a limited area ensemble prediction system. Agricultural Water Management , v.178, p.106-118, 2016. https://doi.org/10.1016/j.agwat.2016.09.015 https://doi.org/10.1016/j.agwat.2016.09.... ); (Rodriguez-Moreno et al., 2016Rodriguez-Moreno, F.; Zemek, F.; Kren, J.; Pikl, M.; Lukas, V.; Novak, J. Spectral monitoring of wheat canopy under uncontrolled conditions for decision-making purposes. Computers and Electronics in Agriculture , v.125, p.81-88, 2016. https://doi.org/10.1016/j.compag.2016.05.002 https://doi.org/10.1016/j.compag.2016.05... ); (Turner et al., 2016Turner, P. A.; Griffis, T. J.; Mulla, D. J.; Baker, J. M.; Venterea, R. T. A geostatistical approach to identify and mitigate agricultural nitrous oxide emission hotspots. Science of The Total Environment , v.572, p.442-449, 2016. https://doi.org/10.1016/j.scitotenv.2016.08.094 https://doi.org/10.1016/j.scitotenv.2016... ); (Gili et al., 2017Gili, A.; Álvarez, C.; Bagnato, R.; Noellemeyer, E. Comparison of three methods for delineating management zones for site-specific crop management. Computers and Electronics in Agriculture , v.139, p.213-223, 2017. https://doi.org/10.1016/j.compag.2017.05.022 https://doi.org/10.1016/j.compag.2017.05... ); (Medina et al., 2017Medina, H.; De Jong Van Lier, Q.; García, J.; Ruiz, M. E. Regional-scale variability of soil properties in Western Cuba. Soil and Tillage Research , v.166, p.84-99, 2017. https://doi.org/10.1016/j.still.2016.10.009 https://doi.org/10.1016/j.still.2016.10.... ); (Nouri et al., 2017Nouri, M.; Gomez, C.; Gorretta, N.; Roger, J. M. Clay content mapping from airborne hyperspectral Vis-NIR data by transferring a laboratory regression model. Geoderma , v.298, p.54-66, 2017. https://doi.org/10.1016/j.geoderma.2017.03.011 https://doi.org/10.1016/j.geoderma.2017.... ); (Paraforos et al., 2017Paraforos, D. S.; Reutemann, M.; Sharipov, G.; Werner, R.; Griepentrog, H. W. Total station data assessment using an industrial robotic arm for dynamic 3D in-field positioning with sub-centimetre accuracy. Computers and Electronics in Agriculture , v.136, p.166-175, 2017. https://doi.org/10.1016/j.compag.2017.03.009 https://doi.org/10.1016/j.compag.2017.03... ); (Fujinuma et al., 2018Fujinuma, R.; Kirchhof, G.; Ramakrishna, A.; Sirabis, W.; Yapo, J.; Woruba, D.; Gurr, G.; Menzies, N. Intensified sweet potato production in Papua New Guinea drives plant nutrient decline over the last decade. Agriculture, Ecosystems & Environment , v.254, p.10-19, 2018. https://doi.org/10.1016/j.agee.2017.11.012 https://doi.org/10.1016/j.agee.2017.11.0... ); (Liang et al., 2018Liang, W.; Kirk, K. R.; Greene, J. K. Estimation of soybean leaf area, edge, and defoliation using color image analysis. Computers and Electronics in Agriculture , v.150, p.41-51, 2018. https://doi.org/10.1016/j.compag.2018.03.021 https://doi.org/10.1016/j.compag.2018.03... ); (Neave et al., 2018Neave, H. W.; Lomb, J.; Weary, D. M.; Leblanc, S. J.; Huzzey, J. M.; Von Keyserlingk, M. A. G. Behavioral changes before metritis diagnosis in dairy cows. Journal of Dairy Science, v.101, p.4388-4399, 2018. https://doi.org/10.3168/jds.2017-13078 https://doi.org/10.3168/jds.2017-13078... ); (Paraforos et al., 2018Paraforos, D. S.; Hübner, R.; Griepentrog, H. W. Automatic determination of headland turning from auto-steering position data for minimising the infield non-working time. Computers and Electronics in Agriculture , v.152, p.393-400, 2018. https://doi.org/10.1016/j.compag.2018.07.035 https://doi.org/10.1016/j.compag.2018.07... ); (Schönhart et al., 2018Schönhart, M.; Trautvetter, H.; Parajka, J.; Blaschke, A. P.; Hepp, G.; Kirchner, M.; Mitter, H.; Schmid, E.; Strenn, B.; Zessner, M. Modelled impacts of policies and climate change on land use and water quality in Austria. Land Use Policy, v.76, p.500-514, 2018. https://doi.org/10.1016/j.landusepol.2018.02.031 https://doi.org/10.1016/j.landusepol.201... ); (Sirsat et al., 2018Sirsat, M. S.; Cernadas, E.; Fernández-Delgado, M.; Barro, S. Automatic prediction of village-wise soil fertility for several nutrients in India using a wide range of regression methods. Computers and Electronics in Agriculture , v.154, p.120-133, 2018. https://doi.org/10.1016/j.compag.2018.08.003 https://doi.org/10.1016/j.compag.2018.08... ); (Barca et al., 2019Barca, E.; De Benedetto, D.; Stellacci, A. M. Contribution of EMI and GPR proximal sensing data in soil water content assessment by using linear mixed effects models and geostatistical approaches. Geoderma , v.343, p.280-293, 2019. https://doi.org/10.1016/j.geoderma.2019.01.030 https://doi.org/10.1016/j.geoderma.2019.... ); (Liu et al., 2019Liu, X.; Guanter, L.; Liu, L.; Damm, A.; Malenovský, Z.; Rascher, U.; Peng, D.; Du, S.; Gastellu-Etchegorry, J.-P. Downscaling of solar-induced chlorophyll fluorescence from canopy level to photosystem level using a random forest model. Remote Sensing of Environment , v.231, 110772, 2019. https://doi.org/10.1016/ j.rse.2018.05.035 https://doi.org/10.1016/ j.rse.2018.05.0... ); (Prasad et al., 2019Prasad, R.; Deo, R. C.; Li, Y.; Maraseni, T. Weekly soil moisture forecasting with multivariate sequential, ensemble empirical mode decomposition and Boruta-random forest hybridizer algorithm approach. Catena , v.177, p.149-166, 2019. https://doi.org/10.1016/j.catena.2019.02.012 https://doi.org/10.1016/j.catena.2019.02... )
PCA	15	(Hotelling, 1933Hotelling, H. Analysis of a complex of statistical variables into principal components. Journal of Educational Psychology, v.24, p.417-441, 1933. http://dx.doi.org/10.1037/h0071325 http://dx.doi.org/10.1037/h0071325... ); (Gnanadesikan, 1977Gnanadesikan, R. Methods for statistical analysis of multivariate observations. New York: Wiley, 1977.); (Jolliffe, 2005Jolliffe, I. Principal Component Analysis. Encyclopedia of Statistics in Behavioral Science, v.2, p.15-23, 2005. http://dx.doi.org/10.1037/h0071325 http://dx.doi.org/10.1037/h0071325... ); (Mat et al., 2014Mat, N. N.; Rowshon, K. M.; Guangnan, C.; Troy, J. Prediction of Sugarcane Quality Parameters Using Visible-shortwave Near Infrared Spectroradiometer. Agriculture and Agricultural Science Procedia, v.2, p.136-143, 2014. https://doi.org/10.1016/j.aaspro.2014.11.020 https://doi.org/10.1016/j.aaspro.2014.11... ); (Waruru et al., 2015Waruru, B. K.; Shepherd, K. D.; Ndegwa, G. M.; Sila, A.; Kamoni, P. T. Application of mid-infrared spectroscopy for rapid characterization of key soil properties for engineering land use. Soils and Foundations, v.55, p.1181-1195, 2015. https://doi.org/10.1016/j.sandf.2015.09.018 https://doi.org/10.1016/j.sandf.2015.09.... ); (Kaniu & Angeyo, 2015Kaniu, M. I.; Angeyo, K. H. Challenges in rapid soil quality assessment and opportunities presented by multivariate chemometric energy dispersive X-ray fluorescence and scattering spectroscopy. Geoderma , v.241-242, p.32-40, 2015. https://doi.org/10.1016/j.geoderma.2014.10.014 https://doi.org/10.1016/j.geoderma.2014.... ); (Knadel et al., 2015Knadel, M.; Thomsen, A.; Schelde, K.; Greve, M. H. Soil organic carbon and particle sizes mapping using vis-NIR, EC and temperature mobile sensor platform. Computers and Electronics in Agriculture , v.114, p.134-144, 2015. https://doi.org/10.1016/j.compag.2015.03.013 https://doi.org/10.1016/j.compag.2015.03... ); (Stockmann et al., 2015Stockmann, U.; Malone, B. P.; McBratney, A. B.; Minasny, B. Landscape-scale exploratory radiometric mapping using proximal soil sensing. Geoderma , v.239-240, p.115-129, 2015. https://doi.org/10.1016/j.geoderma.2014.10.005 https://doi.org/10.1016/j.geoderma.2014.... ); (Morellos et al., 2016Morellos, A.; Pantazi, X. E.; Moshou, D.; Alexandridis, T.; Whetton, R.; Tziotzios, G.; Wiebensohn, J.; Bill, R.; Mouazen, A. M. Machine learning based prediction of soil total nitrogen, organic carbon and moisture content by using VIS-NIR spectroscopy. Biosystems Engineering , v.152, p.104-116, 2016. https://doi.org/10.1016/j.biosystemseng.2016.04.018 https://doi.org/10.1016/j.biosystemseng.... ); (Páscoa et al., 2016Páscoa, R. N. M. J.; Lopo, M.; T. Santos, C. A.; Graça, A. R.; Lopes, J. A. Exploratory study on vineyards soil mapping by visible/near-infrared spectroscopy of grapevine leaves. Computers and Electronics in Agriculture , v.127, p.15-25, 2016. https://doi.org/10.1016/j.compag.2016.05.014 https://doi.org/10.1016/j.compag.2016.05... ); (Adeline et al., 2017Adeline, K. R. M.; Gomez, C.; Gorretta, N.; Roger, J.-M. Predictive ability of soil properties to spectral degradation from laboratory Vis-NIR spectroscopy data. Geoderma, v.288, p.143-153, 2017. https://doi.org/10.1016/j.geoderma.2016.11.010 https://doi.org/10.1016/j.geoderma.2016.... ); (Demattê et al., 2017Demattê, J. A. M.; Ramirez-Lopez, L.; Marques, K. P. P.; Rodella, A. A. Chemometric soil analysis on the determination of specific bands for the detection of magnesium and potassium by spectroscopy. Geoderma , v.288, p.8-22, 2017. https://doi.org/10.1016/j.geoderma.2016.11.013 https://doi.org/10.1016/j.geoderma.2016.... ); (Mirzaeitalarposhti et al., 2017Mirzaeitalarposhti, R.; Demyan, M. S.; Rasche, F.; Cadisch, G.; Müller, T. Mid-infrared spectroscopy to support regional-scale digital soil mapping on selected croplands of South-West Germany. Catena , v.149, p.283-293, 2017. https://doi.org/10.1016/j.catena.2016.10.001 https://doi.org/10.1016/j.catena.2016.10... ); (Gholizadeh et al., 2018Gholizadeh, A.; Žižala, D.; Saberioon, M.; Borůvka, L. Soil organic carbon and texture retrieving and mapping using proximal, airborne and Sentinel-2 spectral imaging. Remote Sensing of Environment , v.218, p.89-103, 2018. https://doi.org/10.1016/j.rse.2018.09.015 https://doi.org/10.1016/j.rse.2018.09.01... ); (Moura-Bueno et al., 2019Moura-Bueno, J. M.; Dalmolin, R. S. D.; Ten Caten, A.; Dotto, A. C.; Demattê, J. A. M. M. Stratification of a local VIS-NIR-SWIR spectral library by homogeneity criteria yields more accurate soil organic carbon predictions. Geoderma , v.337, p.565-581, 2019. https://doi.org/10.1016/j.geoderma.2018.10.015 https://doi.org/10.1016/j.geoderma.2018.... )

Technique	N	Studies
Local Moran's Index (LISA)	4	(Lamsal et al., 2009Lamsal, S.; Bliss, C. M.; Graetz, D. A. Geospatial mapping of soil nitrate-nitrogen distribution under a mixed-land use system. Pedosphere, v.19, p.434-445, 2009. https://doi.org/10.1016/S1002-0160(09)60136-3 https://doi.org/10.1016/S1002-0160(09)60... ); (Calafat et al., 2015Calafat, C.; Gallego, A.; Quintanilla, I. Integrated geo-referenced data and statistical analysis for dividing livestock farms into geographical zones in the Valencian Community (Spain). Computers and Electronics in Agriculture , v.114, p.58-67, 2015. https://doi.org/10.1016/j.compag.2015.03.005 https://doi.org/10.1016/j.compag.2015.03... ); (Córdoba et al., 2016Córdoba, M. A.; Bruno, C. I.; Costa, J. L.; Peralta, N. R.; Balzarini, M. G. Protocol for multivariate homogeneous zone delineation in precision agriculture. Biosystems Engineering , v.143, p.95-107, 2016. https://doi.org/10.1016/j.biosystemseng.2015.12.008 https://doi.org/10.1016/j.biosystemseng.... ); (Vega et al., 2019Vega, A.; Córdoba, M.; Castro-Franco, M.; Balzarini, M. Protocol for automating error removal from yield maps. Precision Agriculture , v.20, p.1030-1044, 2019. https://doi.org/10.1007/s11119-018-09632-8 https://doi.org/10.1007/s11119-018-09632... )

Program	N	Studies	License
FastMapping	1	(Paccioretti et al., 2020Paccioretti, P.; Córdoba, M.; Balzarini, M. FastMapping: Software to create field maps and identify management zones in precision agriculture. Computers and Electronics in Agriculture , v.175, 105556, 2020. https://doi.org/10.1016/j.compag.2020.105556 https://doi.org/10.1016/j.compag.2020.10... )	Free
JMP Pro	1	(Uribeetxebarria et al., 2018Uribeetxebarria, A.; Daniele, E.; Escolà, A.; Arnó, J.; Martínez-Casasnovas, J. A. Spatial variability in orchards after land transformation: Consequences for precision agriculture practices. Science of The Total Environment , v.635, p.343-352, 2018. https://doi.org/10.1016/j.scitotenv.2018.04.153 https://doi.org/10.1016/j.scitotenv.2018... )	Proprietary software
MapFilter	-		Free
Matlab	4	(Liang et al., 2018Liang, W.; Kirk, K. R.; Greene, J. K. Estimation of soybean leaf area, edge, and defoliation using color image analysis. Computers and Electronics in Agriculture , v.150, p.41-51, 2018. https://doi.org/10.1016/j.compag.2018.03.021 https://doi.org/10.1016/j.compag.2018.03... ; Paraforos et al., 2018Paraforos, D. S.; Hübner, R.; Griepentrog, H. W. Automatic determination of headland turning from auto-steering position data for minimising the infield non-working time. Computers and Electronics in Agriculture , v.152, p.393-400, 2018. https://doi.org/10.1016/j.compag.2018.07.035 https://doi.org/10.1016/j.compag.2018.07... , 2017Paraforos, D. S.; Reutemann, M.; Sharipov, G.; Werner, R.; Griepentrog, H. W. Total station data assessment using an industrial robotic arm for dynamic 3D in-field positioning with sub-centimetre accuracy. Computers and Electronics in Agriculture , v.136, p.166-175, 2017. https://doi.org/10.1016/j.compag.2017.03.009 https://doi.org/10.1016/j.compag.2017.03... ; Sanches et al., 2019Sanches, G. M.; Graziano Magalhães, P. S.; Junqueira Franco, H. C. Site-specific assessment of spatial and temporal variability of sugarcane yield related to soil attributes. Geoderma , v.334, p.90-98, 2019. https://doi.org/10.1016/j.geoderma.2018.07.051 https://doi.org/10.1016/j.geoderma.2018.... )	Proprietary software
Python	1	(Coelho et al., 2018Coelho, A. L. De F.; De Queiroz, D. M.; Valente, D. S. M.; Pinto, F. De A. De C. An open-source spatial analysis system for embedded systems. Computers and Electronics in Agriculture , v.154, p.289-295, 2018. https://doi.org/10.1016/j.compag.2018.09.019 https://doi.org/10.1016/j.compag.2018.09... )	Free
SAS	3	(Chung et al., 2013Chung, S. O.; Sudduth, K. A.; Motavalli, P. P.; Kitchen, N. R. Relating mobile sensor soil strength to penetrometer cone index. Soil and Tillage Research , v.129, p.9-18, 2013. https://doi.org/10.1016/j.still.2012.12.004 https://doi.org/10.1016/j.still.2012.12.... ; Neave et al., 2018Neave, H. W.; Lomb, J.; Weary, D. M.; Leblanc, S. J.; Huzzey, J. M.; Von Keyserlingk, M. A. G. Behavioral changes before metritis diagnosis in dairy cows. Journal of Dairy Science, v.101, p.4388-4399, 2018. https://doi.org/10.3168/jds.2017-13078 https://doi.org/10.3168/jds.2017-13078... ; Barca et al., 2019Barca, E.; De Benedetto, D.; Stellacci, A. M. Contribution of EMI and GPR proximal sensing data in soil water content assessment by using linear mixed effects models and geostatistical approaches. Geoderma , v.343, p.280-293, 2019. https://doi.org/10.1016/j.geoderma.2019.01.030 https://doi.org/10.1016/j.geoderma.2019.... )	Proprietary software
Isatis	2	(Castrignanò et al., 2012Castrignanò, A.; Wong, M. T. F. T. F.; Stelluti, M.; De Benedetto, D.; Sollitto, D. Use of EMI, gamma-ray emission and GPS height as multi-sensor data for soil characterisation. Geoderma , v.175, p.78-89, 2012. https://doi.org/10.1016/j.geoderma.2012.01.013 https://doi.org/10.1016/j.geoderma.2012.... ; Cavallo et al., 2016Cavallo, G.; De Benedetto, D.; Castrignanò, A.; Quarto, R.; Vonella, A. V.; Buttafuoco, G. Use of geophysical data for assessing 3D soil variation in a durum wheat field and their association with crop yield. Biosystems Engineering, v.152, p.28-40, 2016. https://doi.org/10.1016/j.biosystemseng.2016.07.002 https://doi.org/10.1016/j.biosystemseng.... )	Proprietary software
R Core	10	(Sudduth & Drummond, 2007Sudduth, K. A.; Drummond, S. T. Yield Editor: Software for Removing Errors from Crop Yield Maps. Agronomy Journal, v.99, p.1471-1482, 2007. https://doi.org/10.2134/agronj2006.0326 https://doi.org/10.2134/agronj2006.0326... a; Stockmann et al., 2015Stockmann, U.; Malone, B. P.; McBratney, A. B.; Minasny, B. Landscape-scale exploratory radiometric mapping using proximal soil sensing. Geoderma , v.239-240, p.115-129, 2015. https://doi.org/10.1016/j.geoderma.2014.10.005 https://doi.org/10.1016/j.geoderma.2014.... ; Castaldi et al., 2016Castaldi, F.; Palombo, A.; Santini, F.; Pascucci, S.; Pignatti, S.; Casa, R. Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon. Remote Sensing of Environment, v.179, p.54-65, 2016. https://doi.org/10.1016/j.rse.2016.03.025 https://doi.org/10.1016/j.rse.2016.03.02... ; Córdoba et al., 2016Córdoba, M. A.; Bruno, C. I.; Costa, J. L.; Peralta, N. R.; Balzarini, M. G. Protocol for multivariate homogeneous zone delineation in precision agriculture. Biosystems Engineering , v.143, p.95-107, 2016. https://doi.org/10.1016/j.biosystemseng.2015.12.008 https://doi.org/10.1016/j.biosystemseng.... ; Gili et al., 2017Gili, A.; Álvarez, C.; Bagnato, R.; Noellemeyer, E. Comparison of three methods for delineating management zones for site-specific crop management. Computers and Electronics in Agriculture , v.139, p.213-223, 2017. https://doi.org/10.1016/j.compag.2017.05.022 https://doi.org/10.1016/j.compag.2017.05... ; Schönhart et al., 2018Schönhart, M.; Trautvetter, H.; Parajka, J.; Blaschke, A. P.; Hepp, G.; Kirchner, M.; Mitter, H.; Schmid, E.; Strenn, B.; Zessner, M. Modelled impacts of policies and climate change on land use and water quality in Austria. Land Use Policy, v.76, p.500-514, 2018. https://doi.org/10.1016/j.landusepol.2018.02.031 https://doi.org/10.1016/j.landusepol.201... ; Sirsat et al., 2018Sirsat, M. S.; Cernadas, E.; Fernández-Delgado, M.; Barro, S. Automatic prediction of village-wise soil fertility for several nutrients in India using a wide range of regression methods. Computers and Electronics in Agriculture , v.154, p.120-133, 2018. https://doi.org/10.1016/j.compag.2018.08.003 https://doi.org/10.1016/j.compag.2018.08... ; Barca et al., 2019Barca, E.; De Benedetto, D.; Stellacci, A. M. Contribution of EMI and GPR proximal sensing data in soil water content assessment by using linear mixed effects models and geostatistical approaches. Geoderma , v.343, p.280-293, 2019. https://doi.org/10.1016/j.geoderma.2019.01.030 https://doi.org/10.1016/j.geoderma.2019.... ; Moura-Bueno et al., 2019Moura-Bueno, J. M.; Dalmolin, R. S. D.; Ten Caten, A.; Dotto, A. C.; Demattê, J. A. M. M. Stratification of a local VIS-NIR-SWIR spectral library by homogeneity criteria yields more accurate soil organic carbon predictions. Geoderma , v.337, p.565-581, 2019. https://doi.org/10.1016/j.geoderma.2018.10.015 https://doi.org/10.1016/j.geoderma.2018.... ; Vega et al., 2019Vega, A.; Córdoba, M.; Castro-Franco, M.; Balzarini, M. Protocol for automating error removal from yield maps. Precision Agriculture , v.20, p.1030-1044, 2019. https://doi.org/10.1007/s11119-018-09632-8 https://doi.org/10.1007/s11119-018-09632... )	Free
SPSS	5	(Tesfahunegn et al., 2011Tesfahunegn, G. B.; Tamene, L.; Vlek, P. L. G. Catchment-scale spatial variability of soil properties and implications on site-specific soil management in northern Ethiopia. Soil and Tillage Research , v.117, p.124-139, 2011. https://doi.org/10.1016/j.still.2011.09.005 https://doi.org/10.1016/j.still.2011.09.... ; Wang et al., 2015Wang, N.; Jassogne, L.; Van Asten, P. J. A.; Mukasa, D.; Wanyama, I.; Kagezi, G.; Giller, K. E. Evaluating coffee yield gaps and important biotic, abiotic, and management factors limiting coffee production in Uganda. European Journal of Agronomy , v.63, p.1-11, 2015. https://doi.org/10.1016/j.eja.2014.11.003 https://doi.org/10.1016/j.eja.2014.11.00... ; Li et al., 2016Li, Q.; Luo, Y.; Wang, C.; Li, B.; Zhang, X.; Yuan, D.; Gao, X.; Zhang, H. Spatiotemporal variations and factors affecting soil nitrogen in the purple hilly area of Southwest China during the 1980s and the 2010s. Science of The Total Environment, v.547, p.173-181, 2016. https://doi.org/10.1016/j.scitotenv.2015.12.094 https://doi.org/10.1016/j.scitotenv.2015... ; Medina et al., 2017Medina, H.; De Jong Van Lier, Q.; García, J.; Ruiz, M. E. Regional-scale variability of soil properties in Western Cuba. Soil and Tillage Research , v.166, p.84-99, 2017. https://doi.org/10.1016/j.still.2016.10.009 https://doi.org/10.1016/j.still.2016.10.... ; Rosemary et al., 2017Rosemary, F.; Vitharana, U. W. A.; Indraratne, S. P.; Weerasooriya, R.; Mishra, U. Exploring the spatial variability of soil properties in an Alfisol soil. Catena , v.150, p.53-61, 2017. https://doi.org/10.1016/j.catena.2016.10.017 https://doi.org/10.1016/j.catena.2016.10... )	Proprietary software
Statgraphics	1	(Gianquinto et al., 2011Gianquinto, G.; Orsini, F.; Fecondini, M.; Mezzetti, M.; Sambo, P.; Bona, S. A methodological approach for defining spectral indices for assessing tomato nitrogen status and yield. European Journal of Agronomy, v.35, p.135-143, 2011. https://doi.org/10.1016/j.eja.2011.05.005 https://doi.org/10.1016/j.eja.2011.05.00... )	Proprietary software
Statistica	1	(Jordanova & Petrov, 2016Jordanova, N.; Jordanova, D.; Petrov, P. Soil magnetic properties in Bulgaria at a national scale-Challenges and benefits. Global and Planetary Change, v.137, p.107-122, 2016. https://doi.org/10.1016/j.gloplacha.2015.12.015 https://doi.org/10.1016/j.gloplacha.2015... )	Proprietary software
YieldEditor	2	(Sudduth & Drummond, 2007Sudduth, K. A.; Drummond, S. T. Yield Editor: Software for Removing Errors from Crop Yield Maps. Agronomy Journal, v.99, p.1471-1482, 2007. https://doi.org/10.2134/agronj2006.0326 https://doi.org/10.2134/agronj2006.0326... b; Sudduth et al., 2012Sudduth, K. A.; Drummond, S. T.; Myers, D. B. Yield Editor 2.0: Software for Automated Removal of Yield Map Errors. 2012 ASABE Annual International Meeting, v.7004, 14, 2012. https://doi.org/10.13031/2013.41893 https://doi.org/10.13031/2013.41893... )	Free

[1] * Corresponding author - E-mail: eduardo.souza@unioeste.br

Brasil

Brasil

Data processing to remove outliers and inliers: A systematic literature study¹ 1 Research developed at Universidade Estadual do Oeste do Paraná, Cascavel, PR, Brazil

Processamento de dados para remoção de pontos outliers e inliers: Estudo sistemático da literatura

ABSTRACT

HIGHLIGHTS:

Brasil

Brasil

Data processing to remove outliers and inliers: A systematic literature study1 1 Research developed at Universidade Estadual do Oeste do Paraná, Cascavel, PR, Brazil

Processamento de dados para remoção de pontos outliers e inliers: Estudo sistemático da literatura

ABSTRACT

HIGHLIGHTS:

Data processing to remove outliers and inliers: A systematic literature study¹ 1 Research developed at Universidade Estadual do Oeste do Paraná, Cascavel, PR, Brazil