Soil Classification Resorting to Machine Learning Techniques

• Autor(a) principal: Dias, Didier Narciso
• Data de Publicação: 2019
• Tipo de documento: Dissertação
• Idioma: eng
• Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
• Texto Completo: http://hdl.handle.net/10362/125335
• topic: Soil Classification; Soil Properties; Ensemble Learning; Neural Networks; Gradient Tree Boosting; Random Forests; Domínio/Área Científica::Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática

Soil classification is the act of resuming the most relevant information about a soil profile into a single class, from which we can infer a large amount of properties without extensive knowledge of the subject. These classes then make the communication of soils, and how they can best be used in areas such as agriculture and forestry, simpler and easier to understand. Unfortunately soil classification is expensive and requires that specialists perform varied experiments, to be able to precisely attribute a class to a soil profile. This master’s thesis focuses on machine learning algorithms for soil classification mainly based on its intrinsic attributes, in the Mexico region. The data set used contains 6 760 soil profiles, the 19 464 horizons that constitute them, as well as physical and chemical properties, such as pH or organic content, belonging to those horizons. Four data modelling methods were tested (i.e., standard depths, n first layers, thickness, and area weighted thickness), as well as different values for a k-Nearest Neighbours imputation. A comparison between state of the art machine learning algorithms was also made, namely Random Forests, Gradient Tree Boosting, Deep Neural Networks and Recurrent Neural Networks. All of our modelling methods provided very similar results, when properly parametrised, reaching Kappa values of 0.504 and an accuracy of 0.554, with the standard depths method providing the most consistent results. The k parameter for the imputation showed very little impact on the variation on the results. Gradient Tree Boosting was the algorithm with the best overall results, closely followed by the Random Forests model. The neuron based methods never achieved a Kappa score over 0.4, therefore providing substantially worse results.