Precision conservation: from visual analysis of soil aggregates to the use of neural networks

• Autor(a) principal: Ribeiro,Admilson Írio
• Data de Publicação: 2020
• Outros Autores: Peche Filho,Afonso, Rosas,Claudia Liliana Gutierrez, Albiero,Daniel, Fengler,Felipe Hashimoto, Medeiros,Gerson Araujo de, Diniz,Ivando Severino, Carvalho,Marcela Merides, Longo,Regina Márcia
• Tipo de documento: Artigo
• Idioma: eng
• Título da fonte: Revista ciência agronômica (Online)
• Texto Completo: http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1806-66902020000500409
• topic: Soil aggregate; Fuzzy logic; Artificial neural networks; Morphometry

ABSTRACT The concept of precision conservation can be defined as a set of space technologies and other procedures linked to mappable environmental variables, which can be used to program conservation management practices for natural resources that consider the variability of these variables in space and time within of natural or agricultural systems. In this context, structural loss of soil through human activities is considered, as with a process with a spatial and temporal variation. The management of soil aggregation conditions can contribute to more regenerative and sustainable agricultural processes. It allows spatial analysis technologies through georeferenced visual indicators or even the use of systems with automatic learning, known as deep learning. In this sense, a fair visual method was developed with an analysis of fuzzy logic to classify aggregates in terms of shape, surface roughness, and biogenic structures. Thus, in a second stage, a model of the artificial neural network was developed, capable of detecting and classifying different forms of soil aggregates, thus allowing a brief discussion of the theme and its potential for application in conservation management through the analysis of aggregates via systems automatic sorting. In this way, elements are presented for the motivation of research and development in adaptive technologies in supporting decision-making that can help integrate dynamic and spatial information in the understanding of the soil’s structural condition to preserve the soil more precisely.