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

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.