Problems related to the environment are increasingly commonly known and consequently also technology is adapting to find suitable solutions. The ancestral technique of crop rotation was identified as a solution to address the problems related to pollution due to intensive food production (i.e. using fertilizers and pesticides). To ensure that this technique can actually improve food production, it is necessary to understand how modern technologies can support it; in particular the analysis of crop rotation can support farmers in decision making process and the optimization of farm management practices. The aim of this paper is to investigate how predictive process monitoring techniques can enhance crop rotation strategies by leveraging Agriculture 4.0 through real-time monitoring, resulting in more accurate and adaptive strategies. It is a position paper that proposes research questions for further study, which may help to develop the research area.

Integrating Predictive Process Monitoring Techniques in Smart Agriculture / Fioretto, Simona; Ienco, Dino; Interdonato, Roberto; Masciari, Elio. - 14670 LNAI:(2024), pp. 306-313. (Intervento presentato al convegno 27th International Symposium on Methodologies for Intelligent Systems, ISMIS 2024 tenutosi a fra nel 2024) [10.1007/978-3-031-62700-2_27].

Integrating Predictive Process Monitoring Techniques in Smart Agriculture

Fioretto, Simona;Masciari, Elio
2024

Abstract

Problems related to the environment are increasingly commonly known and consequently also technology is adapting to find suitable solutions. The ancestral technique of crop rotation was identified as a solution to address the problems related to pollution due to intensive food production (i.e. using fertilizers and pesticides). To ensure that this technique can actually improve food production, it is necessary to understand how modern technologies can support it; in particular the analysis of crop rotation can support farmers in decision making process and the optimization of farm management practices. The aim of this paper is to investigate how predictive process monitoring techniques can enhance crop rotation strategies by leveraging Agriculture 4.0 through real-time monitoring, resulting in more accurate and adaptive strategies. It is a position paper that proposes research questions for further study, which may help to develop the research area.
2024
9783031626999
9783031627002
Integrating Predictive Process Monitoring Techniques in Smart Agriculture / Fioretto, Simona; Ienco, Dino; Interdonato, Roberto; Masciari, Elio. - 14670 LNAI:(2024), pp. 306-313. (Intervento presentato al convegno 27th International Symposium on Methodologies for Intelligent Systems, ISMIS 2024 tenutosi a fra nel 2024) [10.1007/978-3-031-62700-2_27].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/991698
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