Pregled bibliografske jedinice broj: 1169755
An IoT-Based Encapsulated Design System for Rapid Model Identification of Plant Development
An IoT-Based Encapsulated Design System for Rapid Model Identification of Plant Development // Telecom, 3 (2022), 1; 70-85 doi:10.3390/telecom3010004 (međunarodna recenzija, članak, znanstveni)
CROSBI ID: 1169755 Za ispravke kontaktirajte CROSBI podršku putem web obrasca
Naslov
An IoT-Based Encapsulated Design System for Rapid Model Identification of Plant Development
Autori
Novak, Hrvoje ; Ratković, Marko ; Cahun, Mateo ; Lešić, Vinko
Izvornik
Telecom (2673-4001) 3
(2022), 1;
70-85
Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni
Ključne riječi
encapsulated design plant growth chambers ; rapid modeling of plant development ; IoT ; artificial intelligence ; predictive agriculture
Sažetak
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.
Izvorni jezik
Engleski
Znanstvena područja
Poljoprivreda (agronomija), Informacijske i komunikacijske znanosti
POVEZANOST RADA
Projekti:
EK-EFRR-KK.05.1.1.02.0031 - Napredna i prediktivna poljoprivreda za otpornost klimatskim promjenama (AgroSPARC) (Lešić, Vinko; Zovko, Monika; Lemić, Darija; Orsag, Matko, EK - KK.05.1.1.02) ( CroRIS)
