Rule-based expert system for detection of coffee rust warnings in colombian crops

RustOnt: An Ontology to Explain Weather Favorable Conditions of the Coffee Rust

Crop disease management in smart agriculture involves applying and using new technologies to reduce the impact of diseases on the quality of products. Coffee rust is a disease that factors such as poor agronomic management activities and climate conditions may favor. Therefore, it is crucial to identify the relationships between these factors and this disease to learn how to face its consequences and build intelligent systems to provide appropriate management or help farmers and experts make decisions accordingly. Nevertheless, there are no studies in the literature that propose ontologies to model these factors and coffee rust. This paper presents a new ontology called RustOnt to help experts more accurately model data, expressions, and samples related to coffee rust and apply it whilst taking into account the geographical location where the ontology is adopted. Consequently, this ontology is crucial for coffee rust monitoring and management by means of smart agriculture systems. RustOnt was successfully evaluated considering quality criteria such as clarity, consistency, modularity, and competence against a set of initial requirements for which it was built.

A Rule-Based System for Human Performance Evaluation: A Case Study

The issues of many organizations are related to the proper evaluation of human performance and efficient decision-making. The expert system application within the decision-making process is not a novelty, but the widespread of its implementation regarding performance evaluation has not been recognized yet. To overcome this problem, a case study of rule-based expert system application in the decision-making process regarding human performance in periodical technical inspection stations in Croatia is presented. The rule-based expert system improves the quality of traditional decision-making as designed rules provide a visual, transparent, and accurate comparison of observed values with the expected values. Moreover, it provides easy problem identification. Therefore, rules regarding periodical technical inspection inspectors’ performance are designed and embedded in the expert system architecture. However, more effort should be made into data analysis to define parameters and their relations for the purpose of designing rules. Thus, the binary logistic regression and an ANOVA statistical test were conducted to identify which parameters can be assumed as relevant indicators regarding the performance of periodical technical inspection inspectors. In this study, the expert system application has resulted in faster response, greater efficiency, and increased objectivity. That is of utmost importance for providing an efficient and transparent periodical technical inspection system.