engleski

Rapid Plant Development Modelling System for Predictive Agriculture

Actual and upcoming climate changes will evidently have the largest impact on agriculture crops cultivation in terms of reduced harvest, increased costs, and necessary deviation from traditional farming. The aggravating factor for the successful applications of precision and predictive agriculture is the lack of big data due to slow, year-round cycles of crops, as a prerequisite for further analysis and modelling. The goal of our proposed system is to enable rapid collection of data with respect to various climate conditions, which are artificially created and permuted in the encapsulated design and correlated with plant development identifiers. The design is equipped with a large number of sensors and connected to the central database in a computer cloud, which enables the interconnection and coordination of multiple geographically distributed devices and related experiments. This accumulated data is exploited to develop mathematical models of wheat at different growth stages by applying the concepts of artificial intelligence and to utilize them to predict crop development and harvest. The paper focuses on a system concept to gather data for future models to be used publicly and interactively through a portal for predicting plant development under real and hypothetical climate conditions.

plant growth encapsulated design ; rapid plant development modelling ; IoT ; big data ; artificial intelligence ; predictive agriculture

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