Review article
Rapid Plant Development Modelling System for Predictive Agriculture
Vinko Lešić
; University of Zagreb, Faculty of Electrical Engineering and Computing, Department of Control and Computer Engineering, Laboratory for Renewable Energy Systems
Hrvoje Novak
; University of Zagreb, Faculty of Electrical Engineering and Computing, Department of Control and Computer Engineering, Laboratory for Renewable Energy Systems
Marko Ratković
; Innovation Centre Nikola Tesla, Zagreb, Croatia
Abstract
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.
Keywords
plant growth encapsulated design, rapid plant development modelling; IoT, big data, artificial intelligence, predictive agriculture
Hrčak ID:
270519
URI
Publication date:
12.12.2021.
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