Proceedings
Authors
| Filter results3 paper(s) found. |
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1. Democratizing Prescriptive Agronomy: Quality-Preserving Edge AI for Sugar BeetsThe global sugar beet sector faces a critical production paradox where agronomic interventions designed to maximize root yield often compromise sucrose concentration and processing quality. While precision agriculture aims to navigate this delicate balance, current methodologies have reached a methodological impasse. Existing solutions are bifurcated between descriptive data-intensive machine learning (ML), which struggles to generalize across heterogeneous fields, and physiological Process-Based... A. Tabbassi, S. Henkler, A. Zakhary, K. Rother |
2. Digital Agriculture in Dairy Farming: Connectivity Diagnosis and Barriers to Technology Adoption in an Agrotechnological DistrictThe integration of digital tools and communication systems has underpinned a profound transformation in global production chains, aiming to optimize farm operations through technological innovation. Within this context, the present study forms part of the Semear Digital project, coordinated by Embrapa, and is grounded in the premise that digital inclusion constitutes an indispensable strategy for the sustainability and competitiveness of contemporary dairy farming. The primary objective... M.D. Melo, C.M. Paiva, A.L. Oliveira, F.N. Maciel, G.C. Siqueira , M.R. Borges , G.S. Furtado, P.M. Leme |
3. Fusing Deep Learning and Control Theory for Optimized Sugar Beet Yield PredictionAccurate yield prediction is a vital field of research in precision agriculture, enabling optimal resource allocation and enhanced food security under growing climatic uncertainty. Traditional models struggle to capture complex, non-linear interactions between environmental drivers and crop growth. To address this, we present our approach, a multi-stage method for sugar beet yield prediction and management that integrates deep learning with control-theoretic techniques and mathematical language... A. Tabbassi |