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Aguiar Jordão, F
Byrne, D
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Authors
Buckmaster, D
Krogmeier, J
Evans, J
Zhang, Y
Glavin, M
Byrne, D
Harkin, S.J
Aguiar Jordão, F
Santos, D.J
Dalevedo, G.D
Gaion, L.A
Pascoaloto, I.M
Fernandes, E
, J
Lemos, T.F
Topics
Artificial Intelligence (AI) in Agriculture
Site-Specific Nutrient, Lime and Seed Management
Type
Oral
Poster
Year
2024
2026
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1. In-Field and Loading Crop: A Machine Learning Approach to Classify Machine Harvesting Operating Mode

This paper addresses the complex issue of classifying mode of operation (active, idle, stationary unloading, on-the-go unloading, turning) and coordinating agricultural machinery. Agricultural machinery operators must operate within a limited time window to optimize operational efficiency and reduce costs. Existing algorithms for classifying machinery operating modes often rely on heuristic methods. Examples include rules conditioned on machine speed, bearing angle and operational time... D. Buckmaster, J. Krogmeier, J. Evans, Y. Zhang, M. Glavin, D. Byrne, S.J. Harkin

2. Silage Corn Production Under Different Management Strategies: Conventional and 4.0

Agriculture 4.0 has emerged as a strategic tool to maximize operational efficiency and environmental sustainability in agricultural production. The integration of telemetry, automation, and spatial data analysis facilitates more precise management, reducing input waste and enhancing production predictability relative to traditional methods. In this context, the objective of this study was to evaluate the impact of adopting Agriculture 4.0 technologies on the agronomic performance and productive... F. Aguiar Jordão, D.J. Santos, G.D. Dalevedo, L.A. Gaion, I.M. Pascoaloto, E. Fernandes, J. , T.F. Lemos