A new AIoTwin blog post is now available, presenting recent research on how predictive edge AI can make smart agriculture more efficient, scalable, and resilient. The article explores how lightweight AI models, that can run on edge devices, can drastically reduce data transmission while maintaining high accuracy. This approach enables smarter energy use, improved resilience in low-connectivity areas, and scalable deployment even on farms without historical sensor data.
Key topics covered in the blog post include:
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Predictive edge AI that transmits data only when necessary
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In-situ, cross-site, and satellite-to-ground deployment scenarios
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Over 92% reduction in data transmission at a 1.0 °C threshold
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Results from agrometeorological stations across Croatia
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How this supports climate-smart, data-efficient agriculture
Authored by Dora Kreković, following her short-term exchange at Technische Universität Berlin, the blog post explains how the approach performs across three deployment scenarios: in-situ training and inference, cross-site model transfer, and satellite-trained models for farms without historical data. The results demonstrate how predictive edge AI can support climate-smart and data-efficient agriculture, even in regions with limited sensing infrastructure.
We are also pleased to announce that this work has been accepted for presentation at the AIOT Conference 2025, taking place in Osaka, Japan, from 3 to 5 December.
Read the full blog post here.
More updates and technical articles from AIoTwin researchers will follow soon.