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dc.creatorPeixoto, Devison Souza-
dc.creatorSilva, Sérgio Henrique Godinho-
dc.creatorMoreira, Silvino Guimarães-
dc.creatorSilva, Alessandro Alvarenga Pereira da-
dc.creatorChiarini, Thayná Pereira Azevedo-
dc.creatorSilva, Lucas de Castro Moreira da-
dc.creatorCuri, Nilton-
dc.creatorSilva, Bruno Montoani-
dc.date.accessioned2023-02-08T13:04:37Z-
dc.date.available2023-02-08T13:04:37Z-
dc.date.issued2022-09-05-
dc.identifier.citationPEIXOTO, D. S. et al. Machine learning as a useful tool for diagnosis of soil compaction under continuous no-tillage in Brazil. Soil Research, [S.l.], Sept. 2022. DOI: 10.1071/SR22048.pt_BR
dc.identifier.urihttps://www.publish.csiro.au/sr/SR22048pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/55971-
dc.description.abstractContext: correct diagnosis of the state of soil compaction is a challenge in continuous no-tillage (NT). Aims and methods: the aim of this study was to evaluate the performance of four machine learning algorithms to diagnose the state of soil compaction (NT and occasional tillage – OT). For these purposes, data from a field experiment conducted in a clayey Typic Hapludox with mechanical (chiselling and subsoiling) and chemical (gypsum and limestone) methods for mitigation of soil compaction were used. To diagnose the state of soil compaction, soil physical properties [soil bulk density, penetration resistance, macroporosity (MAC), microporosity (MIC), air capacity (AC), available water content, relative field capacity and total porosity (TP)] in addition to crop yield (Rel_Yield) were used as predictor variables for Classification and Regression Trees (CART), Random Forest (RF), Artificial Neural Network (ANN) and Support Vector Machine (SVM) algorithms. Key results: the most important variables for predicting the state of soil compaction were Rel_Yield and soil porosity (MAC, TP, MIC and AC). The machine learning algorithms had satisfactory performance in diagnosing which sites were compacted and which were not. The decision tree algorithms (CART and RF) performed better than ANN and SVM, reaching accuracy = 0.90, Kappa index = 0.76 and sensitivity = 0.83. Conclusions and implications: the machine learning algorithm approach proved to be an efficient tool in diagnosing soil compaction in continuous NT, improving decision-making concerning the use of OT.pt_BR
dc.languageen_USpt_BR
dc.publisherCSIRO Publishingpt_BR
dc.rightsrestrictAccesspt_BR
dc.sourceSoil Researchpt_BR
dc.subjectArtificial neural networkpt_BR
dc.subjectCrop yieldpt_BR
dc.subjectDecision treept_BR
dc.subjectOccasional tillagept_BR
dc.subjectRandom forestpt_BR
dc.subjectSoil physical propertiespt_BR
dc.subjectSoil porositypt_BR
dc.subjectSupport vector machinept_BR
dc.titleMachine learning as a useful tool for diagnosis of soil compaction under continuous no-tillage in Brazilpt_BR
dc.typeArtigopt_BR
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