Precision Farming is nowadays an important challenge aiming to improve environmental quality in rural areas, giving more sustainability to agricultural operations by improving the quality of fieldworks and increasing the food safety and security for social life. To these purposes, the setting up of specific methodologies, for precision Farming Management System (FMS), is required the use of sensors and Geomatics techniques and optimization of field operation with the aim to build a precision Farming Management System (FMS) enhancing the agricultural performance and ability to predict and mitigate environmental risks. The research activities here presented belong to a multidisciplinary project named “PFRLab: Setting of a Precision Farming Robotic Laboratory for cropping system sustainability and food safety and security” founded by the Università Politecnica delle Marche during the period 2017–2020. The main goal of the research is to design a database for data management based on agriculture ontology, using as case study the experimental farm of Università Politecnica delle Marche. To solved this task, many data have been connected by advanced technologies like Unmanned (UAV), Internet of Things (IoT), Remote Sensing and field sensors. In the future, the big amount of field data allow building a Decision Support System to increase the performances of precision farming experimental trials and making farm’s decision potentially more productive and efficient.

An Ontology-Based Study for the Design of a Database for Data Management in Precision Farming / Chiappini, Stefano; Galli, Andrea; Malinverni, Eva Savina; Zingaretti, Primo; Orsini, Roberto; Fiorentini, Marco; Zenobi, S. - ELETTRONICO. - 67:(2020), pp. 811-818. (Intervento presentato al convegno International Mid-Term Conference of the Italian Association of Agricultural Engineering - MID-TERM AIIA 2019 tenutosi a Matera (Italy) nel 12-13 September 2019) [10.1007/978-3-030-39299-4].

An Ontology-Based Study for the Design of a Database for Data Management in Precision Farming

Chiappini Stefano
Methodology
;
Galli Andrea
Methodology
;
Malinverni Eva Savina
Methodology
;
Zingaretti Primo
Methodology
;
Orsini Roberto
Membro del Collaboration Group
;
Fiorentini Marco
Membro del Collaboration Group
;
Zenobi S
Membro del Collaboration Group
2020-01-01

Abstract

Precision Farming is nowadays an important challenge aiming to improve environmental quality in rural areas, giving more sustainability to agricultural operations by improving the quality of fieldworks and increasing the food safety and security for social life. To these purposes, the setting up of specific methodologies, for precision Farming Management System (FMS), is required the use of sensors and Geomatics techniques and optimization of field operation with the aim to build a precision Farming Management System (FMS) enhancing the agricultural performance and ability to predict and mitigate environmental risks. The research activities here presented belong to a multidisciplinary project named “PFRLab: Setting of a Precision Farming Robotic Laboratory for cropping system sustainability and food safety and security” founded by the Università Politecnica delle Marche during the period 2017–2020. The main goal of the research is to design a database for data management based on agriculture ontology, using as case study the experimental farm of Università Politecnica delle Marche. To solved this task, many data have been connected by advanced technologies like Unmanned (UAV), Internet of Things (IoT), Remote Sensing and field sensors. In the future, the big amount of field data allow building a Decision Support System to increase the performances of precision farming experimental trials and making farm’s decision potentially more productive and efficient.
2020
Lecture Notes in Civil Engineering, book series (LNCE, volume 67)
978-3-030-39298-7
978-3-030-39299-4
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/275510
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