Using Artificial Neural Networks to Produce High- Resolution Soil Property Maps

High-resolution maps of soil property are considered as the most important inputs for decision support and policy-making in agriculture, forestry, flood control, and environmental protection. Commonly, soil properties are mainly obtained from field surveys. Field soil surveys are generally time-consuming and expensive, with a limitation of application throughout a large area. As such, high-resolution soil property maps are only available for small areas, very often, being obtained for research purposes. In the chapter, artificial neural network (ANN) models were introduced to produce high-resolution maps of soil property. It was found that ANNs can be used to predict high-resolution soil texture, soil drainage classes, and soil organic content across landscape with reasonable accuracy and low cost. Expanding applications of the ANNs were also presented.

• Tipo de recurso: Book
• Fecha de publicación: 2021
• Institución: Universidad de Bogotá Jorge Tadeo Lozano
• Repositorio: Expeditio: repositorio UTadeo
• Idioma: eng
• OAI Identifier: oai:expeditiorepositorio.utadeo.edu.co:20.500.12010/16833
• Acceso en línea: https://www.intechopen.com/books/advanced-applications-for-artificial-neural-networks/using-artificial-neural-networks-to-produce-high-resolution-soil-property-maps; http://hdl.handle.net/20.500.12010/16833
• Palabra clave: Biología; Carbono orgánico del suelo; Textura de la tierra; Drenaje del suelo
• License: Abierto (Texto Completo)