A decision support system based on multisensor data fusion for sustainable greenhouse management

Risultato della ricerca: Articlepeer review

43 Citazioni (Scopus)

Abstract

The sustainable exploitation of natural resources is nowadays an important challenge for governments and institutions, considering the expected increase of the world population. In order to respond to this emergent criticality, the principles of green economy have been introduced in the European policy discussion to achieve a good compromise between the sustainability and the profitability of productions by increasing the efficiency of farming operations. Such approach poses some technical and financial challenges for small-sized enterprises because they generally do not possess adequate internal knowledge, nor they can acquire external expertise due to their budget restrictions. Decision Support Systems (DSS) can be an effective solution to overcome such difficulties, since they can involve expertsâ knowledge and complex mathematical elaborations on contextual data, thus helping managers in taking more effective decisions. Based on these motivations, the paper proposes a methodological approach to the development of a DSS in the specific context of Integrated Pest Management (IPM) applied to intensive (greenhouse) production, and an experimental validation based on real data. The DSS involves a rule-based decision approach based on referenced mathematical models applied to the information gathered by a sensor network. The multi sensor decision fusion methodology proposed represents an innovative contribution to the literature and an easy-to use and low cost solution to reduce the use of pesticides and fertilizers in protected crops.
Lingua originaleEnglish
pagine (da-a)4057-4065
Numero di pagine9
RivistaJOURNAL OF CLEANER PRODUCTION
Volume172
Stato di pubblicazionePublished - 2018

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Environmental Science(all)
  • Strategy and Management
  • Industrial and Manufacturing Engineering

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