The key role of the agrifood sector and the relevance of digital technologies are emphasized as a crucial part of the United Nations 2030 Agenda implementation. Producing sufficient and safe food for a growing population without overexploiting natural resources is one of the major problems that our society must face, finding solutions that are sustainable in the long term. This is a global challenge, placed in a difficult context of unstable climate, increasing competition for land, water, and energy, in an increasingly urbanized and globalized world. To adequately address this issue, it is mandatory to develop an integrated, large-scale, multidisciplinary research program. In this context, Italy has developed as a structural organization, the National Research Center for Technologies in Agriculture (Agritech), whose research program, with nine spokes, has been developed to integrate a broad diversity of participating research institutions and companies to face this ambitious challenge. In the context of the Spoke 6 “Management models to promote sustainability and resilience of agricultural production systems,” where engineers and agronomists have been jointly involved, this special section “Cutting-Edge Agritech: Modeling, Control, and Sensor Networks for Sustainable Agricultural Practices,” was designed. Eight papers are included, and they contribute to sustainability suggesting the combination of digital twin technology with advanced control systems applied for greenhouse climate management, automated decision support systems to optimize irrigation, prediction models applicable at the edge, control algorithms for smart greenhouses, microwave techniques to assess trees health, greenhouse monitoring by drones, and reflectometry enhanced by machine learning techniques. Finally, a systematic review of continual learning in the agrifood sector is included.

Guest Editorial: Special Section on Cutting-Edge Agritech: Modeling, Control, and Sensor Networks for Sustainable Agricultural Practices / Battilani, Paola; Trinchero, Daniele. - In: IEEE TRANSACTIONS ON AGRIFOOD ELECTRONICS.. - ISSN 2771-9529. - ELETTRONICO. - 4:1(2026), pp. 37-38. [10.1109/TAFE.2026.3679822]

Guest Editorial: Special Section on Cutting-Edge Agritech: Modeling, Control, and Sensor Networks for Sustainable Agricultural Practices

Daniele Trinchero
2026

Abstract

The key role of the agrifood sector and the relevance of digital technologies are emphasized as a crucial part of the United Nations 2030 Agenda implementation. Producing sufficient and safe food for a growing population without overexploiting natural resources is one of the major problems that our society must face, finding solutions that are sustainable in the long term. This is a global challenge, placed in a difficult context of unstable climate, increasing competition for land, water, and energy, in an increasingly urbanized and globalized world. To adequately address this issue, it is mandatory to develop an integrated, large-scale, multidisciplinary research program. In this context, Italy has developed as a structural organization, the National Research Center for Technologies in Agriculture (Agritech), whose research program, with nine spokes, has been developed to integrate a broad diversity of participating research institutions and companies to face this ambitious challenge. In the context of the Spoke 6 “Management models to promote sustainability and resilience of agricultural production systems,” where engineers and agronomists have been jointly involved, this special section “Cutting-Edge Agritech: Modeling, Control, and Sensor Networks for Sustainable Agricultural Practices,” was designed. Eight papers are included, and they contribute to sustainability suggesting the combination of digital twin technology with advanced control systems applied for greenhouse climate management, automated decision support systems to optimize irrigation, prediction models applicable at the edge, control algorithms for smart greenhouses, microwave techniques to assess trees health, greenhouse monitoring by drones, and reflectometry enhanced by machine learning techniques. Finally, a systematic review of continual learning in the agrifood sector is included.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/3010792