Abstract
Smart Agriculture (SA) is an evolution of Precision Farming (PF). It has technological basis very close to the paradigms of Industry 4.0 (Ind-4.0), so that it is also often referred to as Agriculture 4.0. After the proposal of a brief historical examination that provides a conceptual frame to the above terms, the common aspects of SA and Ind-4.0 are analyzed. These are primarily to be found in the cognitive approaches of Knowledge Management 4.0 (KM4.0, the actual theoretical basis of Ind-4.0), which underlines the need to use Integrated Information Systems (IIS) to manage all the activity areas of any production system. Based upon an infological approach, “raw data” becomes “information” only when useful to (or actually used in) a decision-making process. Thus, an IIS must be always designed according to such a view, and KM4.0 conditions the way of collecting and processing data on farms, together with the “information precision” by which the production system is managed. Such precision needs, on their turn, depend on the hierarchical level and the “Macrodomain of Prevailing Interest” (MPI) related to each decision, where the latter identifies a predominant viewpoint through which a system can be analyzed according to a prevailing purpose. Four main MPIs are here proposed: (1) physical and chemical, (2) biological and ecological, (3) productive and hierarchical, and (4) economic and social. In each MPI, the quality of the knowledge depends on the cognitive level and the maturity of the methodological approaches there achieved. The reliability of information tends to decrease from the first to the fourth MPI; lower the reliability, larger the tolerance margins that a measurement systems must ensure. Some practical examples are then discussed, taking into account some IIS-monitoring solutions of increasing complexity in relation to information integration needs and related data fusion approaches. The analysis concludes with the proposal of new operational indications for the verification and certification of the reliability of the information on the entire decision-making chain.
Highlights
The first practical experiences related to the application of so-called Precision Farming (PF)technologies date back to the end of the last century and concern the possibility of improving the performance of machines used in spreading or harvesting operations through new automated data management practices
“precision/accuracy of a measure” with the concept of “precision of the decisional context” (=ability to satisfy the needs of a decision maker, according to adequate levels of reliability). (ii) we analyze some explanatory case studies, typical of the agricultural and zootechnical sectors, able to highlight some practical aspects of the theoretical concepts of the first part; they will range from cases of simple measurement devices to more complex situations with integrated acquisition systems in several application sectors. (iii) we propose possible application strategies to achieve forms of validation and certification of complex monitoring systems, typical of Smart Agriculture (SA) contexts
We operate in the full domain of Macrodomain of Prevailing Interest” (MPI)-1; there are needs for information that-regardless of the decision-making level that requires them-admit very narrow tolerance ranges; in the examples considered here, 3. They concern both the automatic guidance rather than the fully automated field transplantation operations (MPI-1), and the feedback from laboratory tests aimed at a product control (MPI-1 or MPI-2); measurements for information related to Management Support Systems (MSS) applications generally allow higher tolerances, especially when the final information results from the aggregation of several measurements or the integration of several measurement systems used in continuous monitoring activities; in the SA
Summary
The first practical experiences related to the application of so-called Precision Farming (PF). DECISION; in the infological approach, the DECISIONs “require” INFORMATION, which “set” RAW DATA; it is, a simple conceptual approach which, virtual, is able to heavily influence the choice of IIS components and its general architecture, with potentially significant repercussions on the quality of the subsequent management of the enterprise; in the infological approach, the concept of precision depends on the type of decision to be taken and the levels of risk the decision-maker is willing to assume a priori with respect to the efficacy derived from the effects of the decision itself; the quality of decision-making depends on the degree of satisfaction of the objective that the decision is called to resolve (efficacy); from this standpoint, an evaluation process can be represented by a specific class (EVALUATION) that expresses the relationship > between the DECISION and PURPOSE entities; raw data acquisition devices may require the use of different types of basic components (POSITIONING SYSTEMS, SENSORS, IDENTIFICATION SYSTEMS) that are often to be included in integrated combinations; each component has its own application methods and helps to condition the information reliability through the quality of the measures it can perform (conditioned by its precision and accuracy attributes, which must satisfy the tolerance requirements of the raw data); data acquisition has its own temporal dimension, of fundamental importance to reconstruct, the dynamic aspects of production processes; generally, the timestamp is in charge of the (5). All considered, when operating within production systems; going beyond the typical applications of the research or certification world, where the main focus of the reliability of the measures mainly concerns instrumental devices, the common concept of “information precision” can never be intended in absolute terms, rather, it must always be placed in relation to the nature of the decision to be taken and the risks that may derive from the poor quality of the latter
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