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Weist, D
Hostert, P
Kim, J
Wang, D.R
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Authors
Schulthess, R
Schelling, K
Weist, D
Sung, N
Chung, S
Kim, Y
han, K
Choi, J
Kim, J
Cho, Y
Jang, S
Pathak, H
Warren, C.J
Buckmaster, D
Wang, D.R
Thomas, L
Jakimow, B
Janz, A
Hostert, P
Lajunen, A
Topics
Precision A-Z for Practitioners
Engineering Technologies and Advances
In-Season Nitrogen Management
Artificial Intelligence (AI) in Agriculture
Type
Poster
Oral
Year
2010
2016
2024
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Filter results4 paper(s) found.

1. From Rapideye's Spad In The Sky To N Application Maps

... R. Schulthess, K. Schelling, D. Weist

2. Evaluation of a Sensor and Control Interface Module for Monitoring of Greenhouse Environment

Protected horticulture in greenhouses and plant factories has been increased in many countries due to the advantages of year-round production in controlled environment for improved productivity and quality. For protected horticulture, environmental conditions are monitored and controlled through wired and wireless devices. Various devices are used for monitoring and control of spatial and temporal variability in crop growth environmental conditions. Recently, various sensors and control devices,... N. Sung, S. Chung, Y. Kim, K. Han, J. Choi, J. Kim, Y. Cho, S. Jang

3. Advancing Adaptive Agricultural Strategies: Unraveling Impacts of Climate Change and Soils on Corn Productivity Using APSIM

With unprecedented challenges to achieve sustainable crop productivity under climate change and dynamic soil conditions, adaptive management strategies are required for optimizing cropping systems. Using sensors, cropping systems can be continuously monitored and the data collected by them can be analyzed for making informed adaptive management decisions to enhance productivity and environmental sustainability. But sensors can only tell the past and decisions bring implications into the future.... H. Pathak, C.J. Warren, D. Buckmaster, D.R. Wang

4. 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