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

Abstract

The concept of precision conservation can be defined as a set of space technologies and other procedures linked to mappable environmental variables. These technologies and procedures can be used to program conservation management practices for natural resources that take into account the variability of such variables in space and time within natural or agricultural systems. In this context, the structural loss of soil due to human activities is considered as a process with spatial and temporal variations. The management of soil aggregation conditions can contribute to more regenerative and sustainable agricultural processes. It admits 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 analysis of fuzzy logic to classify aggregates in terms of shape, surface roughness, and biogenic structures. Thus, in a second stage, a model of artificial neural network was developed. This model is capable of detecting and classifying different forms of soil aggregates, thus enabling a brief discussion of the topic and its potential application in conservation management through the analysis of aggregates via automatic sorting systems. Elements of research motivation and development on adaptive technologies are presented to support decision-making that can help integrate dynamic and spatial information for understanding of the structural condition of soil and hence its more precise preservation.