Trustworthy Digital Agriculture: Challenges, Countermeasures, and Opportunities
Digital agriculture [agriculture technology (AgTech)] applies emerging technologies, such as the Internet of Things, sensors, and artificial intelligence, to collect, transmit, and process information related to agricultural enterprises. Potential benefits of AgTech are improved productivity, supply chain efficiency, food safety, and the sustainable use of natural resources, contributing to the resilience of farming practices. While the benefits of AgTech use are clear, cyber risks are introduced with this technology that require effective management. Farmers need confidence that the data gained through the use of AgTech are trustworthy (accurate, reliable, and authentic). Data trustworthiness is critical for quality data-informed decision making. Poor quality data may impact agricultural production and negate any potential benefits gained. This article outlines risks related to data trustworthiness for a diverse audience, including those with an agricultural background and/or a technical background in communication and network security, and for the end users (i.e., farmers). This article also discusses the threats associated with AgTech and provides six scenarios that identify potential cyberthreat actors, possible motivations, attack vectors, compromised security goals, and the impact on farm businesses.
Authors
- Warren Armstrong (ORCID: https://orcid.org/0000-0002-0798-1409)
- Luke Kane (ORCID: https://orcid.org/0000-0002-3851-1987)
- Praveen Gauravaram (ORCID: https://orcid.org/0000-0001-9135-2930)
- Leonie Simpson (ORCID: https://orcid.org/0000-0001-8434-9741)
- Mir Ali Rezazadeh Baee (ORCID: https://orcid.org/0000-0003-1575-7287)
- Yinhao Jiang (ORCID: https://orcid.org/0000-0002-1733-9479)
Institutions
- Charles Sturt University (AU)
- Queensland University of Technology (AU)
- Services Australia (AU)
- QuintessenceLabs (Australia) (AU)
Publication Details
- Journal
- IEEE Transactions on AgriFood Electronics
- Published
- 2025-06-18
- DOI
- https://doi.org/10.1109/tafe.2025.3576623
- Citations
- 1
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 1.75