Integrating fuzzy cognitive maps and multi-agent systems for sustainable agriculture

Abstract

In conventional agriculture, a wide variety of decisions are made with high uncertainty. Recommendations regarding pesticides, fertilizers, and irrigation water are made in a very generic manner and do not consider the intrafield variability of parameters that can affect crop yields. This has serious economic and environmental implications. The goal of precision agriculture is to improve agricultural sustainability and to optimize crop growth decisions by accounting for field variability and site-specific parameter values. The aim of the present work was to build an online smart platform based on innovative technologies such as multi-agent processing and fuzzy cognitive maps that improves decision-making by farming field-management nodes, which should ultimately save resources at the farm level while increasing the income from farming through the optimal use of both water and fertilizers and respecting the environment and the consumer. The platform helps farmers to access knowledge that can assist them in making the right decisions when implementing precision agriculture management actions (e.g., matching the application of nitrogen to crop demands, predicting key growth stages, and estimating the optimal duration of irrigation). © 2020, Springer Nature Switzerland AG.