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Haneklaus, S.H
Hostert, P
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Authors
Lilienthal, H
Haneklaus, S.H
Schnug, E
Thomas, L
Jakimow, B
Janz, A
Hostert, P
Lajunen, A
Topics
Site-Specific Nutrient, Lime and Seed Management
Artificial Intelligence (AI) in Agriculture
Type
Oral
Poster
Year
2018
2024
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1. Frameworks for Variable Rate Application of Manure

Worldwide, nitrogen (N) and phosphorus (P) losses from agriculture are main contributors to eutrophication of water bodies so that forceful agro-technical measures are required to reduce their diffuse discharge to the environment. With view to worldwide finite mineral rock phosphates efficient standards are required to close the agricultural P cycle. In intensive agricultural livestock production manure is often treated as a waste problem rather than an organic fertilizer and source of nutrients.... H. Lilienthal, S.H. Haneklaus, E. Schnug

2. Spectral Imaging Deep Learning Mapper for Precision Agriculture

With the growing variety of RGB cameras, spectral sensors, and platforms like field robots or unmanned aerial vehicles (UAV) in precision agriculture, there is a demand for straightforward utilization of collected field data. In recent years, deep learning has gained significant attention and delivered impressive results in the realm of computer vision tasks, such as semantic segmentation. These models have also found extensive applications in research related to precision agriculture and spectral... L. Thomas, B. Jakimow, A. Janz, P. Hostert, A. Lajunen