An IoT-Based Encapsulated Design System for Rapid Model Identification of Plant Development

Abstract

Actual and upcoming climate changes will evidently have the largest impact on agriculture crop cultivation in terms of reduced harvest, increased costs, and necessary deviations from traditional farming. The aggravating factor for the successful applications of precision and predictive agriculture is the lack of granulated historical data due to slow, year-round cycles of crops, as a prerequisite for further analysis and modeling. A methodology of plant growth observation with the rapid performance of experiments is presented in this paper. The proposed system enables the collection of data with respect to various climate conditions, which are artificially created and permuted in the encapsulated design, suitable for further correlation 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 in a remote, autonomous, and real-time manner. Over 40 sensors and up to 24 yearly harvests per device enable the yearly collection of approximately 750,000 correlated database entries, which it is possible to independently stack with higher numbers of devices. Such accumulated data is exploited to develop mathematical models of wheat in different growth stages by applying the concepts of artificial intelligence and utilizing them for the prediction of crop development and harvest.

Case Studies with the Contiki-NG Simulator to Design Strategies for Sensors’ Communication Optimization in an IoT-Fog Ecosystem

“…This increase in the number of built IoT devices has boosted research about applications for areas such as traffic surveillance [ 5 , 6 ], environmental monitoring [ 7 , 8 ], smart cities [ 9 , 10 , 11 ], intelligent transport systems [ 12 ], and agriculture [ 13 , 14 ]. These applications require a reconfigurable architecture and environments that require different computing resources that can be used more efficiently at the edge of the network.…”