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Human-centered ai in smart farming : toward agriculture 5.0
Andreas Holzinger, Iztok Fister, Iztok Fister, Peter Kaul, Senthold Asseng, 2024, original scientific article

Abstract: This paper delineates the contemporary landscape, challenges, and prospective developments in human-centred artificial intelligence (AI) within the ambit of smart farming, a pivotal element of the emergent Agriculture 5.0, supplanting Agriculture 4.0. Analogous to Industry 4.0, agriculture has witnessed a trend towards comprehensive automation, often marginalizing human involvement. However, this approach has encountered limitations in agricultural contexts for various reasons. While AI’s capacity to assume human tasks is acknowledged, the inclusion of human expertise and experiential knowledge (human-in-the-loop) often proves indispensable, corroborated by the Moravec’s Paradox: tasks simple for humans are complex for AI. Furthermore, social, ethical, and legal imperatives necessitate human oversight of AI, a stance strongly reflected in the European Union’s regulatory framework. Consequently, this paper explores the advancements in human-centred AI focusing on their application in agricultural processes. These technological strides aim to enhance crop yields, minimize labor and resource wastage, and optimize the farm-to-consumer supply chain. The potential of AI to augment human decision-making, thereby fostering a sustainable, efficient, and resilient agri-food sector, is a focal point of this discussion - motivated by the current worldwide extreme weather events. Finally, a framework for Agriculture 5.0 is presented, which balances technological prowess with the needs, capabilities, and contexts of human stakeholders. Such an approach, emphasizing accessible, intuitive AI systems that meaningfully complement human activities, is crucial for the successful realization of future Agriculture 5.0.
Keywords: human-centered AI, smart framing, agriculture 5.0, digital transformation, artificial intelligence
Published in DKUM: 23.04.2025; Views: 0; Downloads: 4
Full text (1,10 MB)

Rockerbot: rover kinematics for maize farming
Matteo Zinzani, Mirko Usuelli, Paolo Cudrano, Simone Mentasti, Carlo Arnone, Andrea Cerutti, Alba Lo Grasso, Abdelrahman Tarek Farag, Matteo Matteucci, 2024, original scientific article

Abstract: Crop inspection plays a significant role in modern agricultural practices as it enables farmers to evaluate the condition of their fields and make informed decisions regarding crop management. However, existing methods of crop inspection are often labor-intensive, leading to slow and costly processes. Therefore, there is a pressing need for more efficient and cost-effective approaches to crop inspection to improve agricultural productivity, sustainability, and to deal with labor shortage. In this study, we present Rockerbot, a novel agricultural robot designed as a compact rover capable of navigating and surveying maize fields in their early growth stages. This technology is essential for timely landscape adjustments to ensure optimal crop production. The document offers a comprehensive review of the decisions made during the hardware and software development stages. The hardware section is centered around design choices influenced by the rover’s kinematics, while the software section outlines the tasks that Rockerbot can perform using mobile perception, such as mapping, sensing, and detection.
Keywords: agricultural robotics, smart agriculture, autonomous navigation, watering, mapping
Published in DKUM: 23.04.2025; Views: 0; Downloads: 0
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Time series numerical association rule mining variants in smart agriculture
Iztok Fister, Dušan Fister, Iztok Fister, Vili Podgorelec, Sancho Salcedo-Sanz, 2023, original scientific article

Keywords: association rule mining, smart agriculture, optimization, evolutionary algotihms, internet of things
Published in DKUM: 12.06.2024; Views: 121; Downloads: 12
Full text (1,49 MB)
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