Co-designing a landscape experiment to investigate diversified cropping systems

Grahmann, Kathrin; Reckling, Moritz; Hernández-Ochoa, Ixchel; Donat, Marco; Bellingrath-Kimura, Sonoko; Ewert, Frank

doi:10.1016/j.agsy.2024.103950

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Cited by 5 publications

References 104 publications

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A participatory impact assessment of digital agriculture: A Bayesian network-based case study in Germany

MacPherson,

Rosman,

Helming

et al. 2025

Agricultural Systems

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A participatory impact assessment of digital agriculture: A Bayesian network-based case study in Germany

MacPherson,

Rosman,

Helming

et al. 2025

Agricultural Systems

View full text Add to dashboard Cite

Improving spatial transferability of deep learning models for small-field crop yield prediction

Stiller,

Grahmann,

Ghazaryan

et al. 2024

ISPRS Open Journal of Photogrammetry and Remote Sensing

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Framework for Smart Farming Using Artificial Intelligence of Things

Limbo,

Ntinda,

Nakale

2024

Advances in Business Information Systems and Analytics

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The advancement of technology has led to the introduction of smart technologies in various sectors, including farming, forming what is now known as smart farming. Smart farming involves using smart technologies to perform monitoring and automation tasks traditionally performed by humans. However, despite these advancements, farmers still need help adopting smart farming techniques in crop farming. Therefore, this chapter presents a framework designed to guide the implementation of smart technologies in crop farming activities. This framework employs the concept of artificial intelligence of things (IoT), which utilizes artificial intelligence (AI) for decision-making and data analysis based on data from internet of things (IoT) devices. The framework is structured around five modules: the sensor module, the artificial intelligence module, the automation module, the network module, and the processing module. These modules enable effective monitoring of factors essential for optimal crop growth and performing automation based on monitoring activities.

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Co-designing a landscape experiment to investigate diversified cropping systems

Cited by 5 publications

References 104 publications

A participatory impact assessment of digital agriculture: A Bayesian network-based case study in Germany

A participatory impact assessment of digital agriculture: A Bayesian network-based case study in Germany

Improving spatial transferability of deep learning models for small-field crop yield prediction

Framework for Smart Farming Using Artificial Intelligence of Things

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