Machine learning as a useful tool for diagnosis of soil compaction under continuous no-tillage in Brazil
Devison Souza Peixoto
A , Sérgio Henrique Godinho Silva A , Silvino Guimarães Moreira B , Alessandro Alvarenga Pereira da Silva B , Thayná Pereira Azevedo Chiarini A , Lucas de Castro Moreira da Silva C , Nilton Curi A and Bruno Montoani Silva A *
+ Author Affiliations
- Author Affiliations
A Department of Soil Science, Federal University of Lavras, Avenida Doutor Sylvio Menicucci 1001, CEP 37200-000 Lavras, Minas Gerais, Brazil.
B Department of Agriculture, Federal University of Lavras, Avenida Doutor Sylvio Menicucci 1001, CEP 37200-000 Lavras, Minas Gerais, Brazil.
C Department of Agricultural Engineering, Federal University of Viçosa, Campus Universitário, CEP 36570-900 Viçosa, Minas Gerais, Brazil.
Soil Research 61(2) 145-158 https://doi.org/10.1071/SR22048
Submitted: 28 February 2022 Accepted: 8 August 2022 Published: 5 September 2022
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing
Abstract
Context: 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.
Keywords: Artificial Neural Network, crop yield, decision tree, occasional tillage, Random Forest, soil physical properties, soil porosity, Support Vector Machine.
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