Proceedings

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Weist, D
Hostert, P
Kim, J
Wang, D.R
Maxton, C
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Authors
Lund, E
Maxton, C
Kweon, G
Schulthess, R
Schelling, K
Weist, D
Sung, N
Chung, S
Kim, Y
han, K
Choi, J
Kim, J
Cho, Y
Jang, S
Lund, E
Maxton, C
Lund, T
Lund, E
Lund, T
Maxton, C
Pathak, H
Warren, C.J
Buckmaster, D
Wang, D.R
Thomas, L
Jakimow, B
Janz, A
Hostert, P
Lajunen, A
Topics
Proximal Sensing in Precision Agriculture
Precision A-Z for Practitioners
Engineering Technologies and Advances
Proximal Sensing in Precision Agriculture
Proximal and Remote Sensing of Soil and Crop (including Phenotyping)
In-Season Nitrogen Management
Artificial Intelligence (AI) in Agriculture
Type
Poster
Oral
Year
2012
2010
2016
2022
2024
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Authors

Filter results7 paper(s) found.

1. The Ultimate Soil Survey in One Pass: Soil Texture, Organic Matter, pH, Elevation, Slope, and Curvature

The goal of accurately mapping soil variability preceded GPS-aided agriculture, and has been a challenging aspect of precision agriculture since its inception.  Many studies have found the range of spatial dependence is shorter than the distances used in most grid sampling.  Other studies have examined variability within government soil surveys and concluded that they have limited utility in many precision applications.  Proximal soil sensing has long been envisioned as a method... E. Lund, C. Maxton, G. Kweon

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

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

3. 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

4. A Data Fusion Method for Yield and Soil Sensor Maps

Utilizing yield maps to their full potential has been one of the challenges in precision agriculture.  A key objective for understanding patterns of yield variation is to derive management zones, with the expectation that several years of quality yield data will delineate consistent productivity zones.  The anticipated outcome is a map that shows where soil productive potentials differ.  In spite of the widespread usage of yield monitors, commercial agriculture has found it difficult... E. Lund, C. Maxton, T. Lund

5. Measuring Soil Carbon with Intensive Soil Sampling and Proximal Profile Sensing

Soils have a large carbon storage capacity and sequestering additional carbon in agricultural fields can reduce CO2 levels in the atmosphere, helping to mitigate climate change. Efforts are underway to incentivize agricultural producers to increase soil organic carbon (SOC) stocks in their fields using various conservation practices.  These practices and the increased SOC provide important additional benefits including improved soil health, water quality and – in some cases –... E. Lund, T. Lund, C. Maxton

6. 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

7. 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