In the heart of Morocco, researchers are tackling a global challenge: sustainable water management in agriculture. Nisrine Lachgar, a lead author from Euromed University of Fes (UEMF), has spearheaded the development of an expert system for precision irrigation that promises to revolutionize how farmers and agronomists make critical water management decisions. Published in the IEEE Access journal, which translates to “Access to Electrical and Electronic Engineering,” this research could significantly impact the energy sector by optimizing water use in agriculture, a field notorious for its high energy consumption.
Precision irrigation is not a new concept, but current solutions often rely on complex Internet of Things (IoT) frameworks or artificial intelligence (AI) models that are often seen as “black boxes.” These systems can be challenging to understand and implement, leading to issues with interoperability, transparency, and reproducibility. Lachgar’s team aimed to overcome these constraints by developing an ontology-driven expert system that formalizes knowledge of crops, soil, and climate into a modular framework. This approach ensures that the system’s recommendations are both efficient and comprehensible to end-users.
The system uses the Web Ontology Language (OWL) and Semantic Web Rule Language (SWRL) rules, combining the structured precision of the V-cycle methodology with the data-driven focus of the Cross-Industry Standard Process for Data Mining (CRISP-DM) and the semantic precision of the NeOn methodology. “We wanted to create a system that is not only technically robust but also user-friendly,” Lachgar explains. “By using ontology, we can make the decision-making process transparent and understandable for farmers and agronomists.”
The research team validated the system using scenario-based assessments, demonstrating that the ontology accurately deduced crop coefficients, integrated AI-predicted evapotranspiration with sensor-derived assertions, and produced consistent irrigation recommendations across diverse situations. All reasoning processes performed in under one second, showcasing the system’s computational efficiency.
The implications for the energy sector are substantial. Agriculture accounts for a significant portion of global water use, and inefficient irrigation practices can lead to excessive energy consumption. By providing farmers with a tool that offers precise, understandable recommendations, this expert system can help optimize water use, reduce energy consumption, and promote sustainable agricultural practices.
Lachgar’s research provides a replicable and adaptable foundation for ontology-based irrigation decision support. Future implementations could integrate real-time sensor data and field validation, further enhancing the system’s accuracy and utility. As the world grapples with the challenges of climate change and water scarcity, such innovations are crucial for ensuring food security and sustainable resource management.
This work not only advances the field of precision agriculture but also underscores the importance of interdisciplinary collaboration. By bridging the gap between technology and practical application, Lachgar and her team have developed a tool that has the potential to transform agricultural practices and contribute to a more sustainable future. As the research community continues to explore the possibilities of ontology-driven systems, the insights gained from this study will undoubtedly shape future developments in the field.