Precision conservation: from visual analysis of soil aggregates to the use of neural networks
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Data
2020-01-01
Autores
Ribeiro, Admilson Írio [UNESP]
Filho, Afonso Peche
Rosas, Claudia Liliana Gutierrez [UNESP]
Albiero, Daniel
Fengler, Felipe Hashimoto [UNESP]
de Medeiros, Gerson Araujo [UNESP]
Diniz, Ivando Severino [UNESP]
Carvalho, Marcela Merides [UNESP]
Longo, Regina Márcia
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Resumo
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.
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Artificial neural networks, Fuzzy logic, Morphometry, Soil aggregate
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Revista Ciencia Agronomica, v. 51, n. 5, p. 01-13, 2020.