Machine learning as a useful tool for diagnosis of soil compaction under continuous no-tillage in Brazil

Registro completo de metadados
MetadadosDescriçãoIdioma
Autor(es): dc.creatorPeixoto, Devison Souza-
Autor(es): dc.creatorSilva, Sérgio Henrique Godinho-
Autor(es): dc.creatorMoreira, Silvino Guimarães-
Autor(es): dc.creatorSilva, Alessandro Alvarenga Pereira da-
Autor(es): dc.creatorChiarini, Thayná Pereira Azevedo-
Autor(es): dc.creatorSilva, Lucas de Castro Moreira da-
Autor(es): dc.creatorCuri, Nilton-
Autor(es): dc.creatorSilva, Bruno Montoani-
Data de aceite: dc.date.accessioned2026-02-09T11:40:12Z-
Data de disponibilização: dc.date.available2026-02-09T11:40:12Z-
Data de envio: dc.date.issued2023-02-07-
Data de envio: dc.date.issued2023-02-07-
Data de envio: dc.date.issued2022-09-05-
Fonte completa do material: dc.identifierhttps://repositorio.ufla.br/handle/1/55971-
Fonte completa do material: dc.identifierhttps://www.publish.csiro.au/sr/SR22048-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/capes/1144808-
Descrição: dc.descriptionContext: 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.-
Idioma: dc.languageen-
Publicador: dc.publisherCSIRO Publishing-
Direitos: dc.rightsrestrictAccess-
???dc.source???: dc.sourceSoil Research-
Palavras-chave: dc.subjectArtificial neural network-
Palavras-chave: dc.subjectCrop yield-
Palavras-chave: dc.subjectDecision tree-
Palavras-chave: dc.subjectOccasional tillage-
Palavras-chave: dc.subjectRandom forest-
Palavras-chave: dc.subjectSoil physical properties-
Palavras-chave: dc.subjectSoil porosity-
Palavras-chave: dc.subjectSupport vector machine-
Título: dc.titleMachine learning as a useful tool for diagnosis of soil compaction under continuous no-tillage in Brazil-
Tipo de arquivo: dc.typeArtigo-
Aparece nas coleções:Repositório Institucional da Universidade Federal de Lavras (RIUFLA)

Não existem arquivos associados a este item.