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GONZALES, B
Huang, J
Simek, P
Thomson, S.J
Zhang, Y
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Authors
Ulman, M
Stoces, M
Jarolimek, J
Simek, P
Thomson, S.J
DeFauw, S.L
English, P.J
Hanks, J.E
Fisher, D.K
Foster, P.N
Zimba, P.V
Khakbazan, M
Moulin, A
Huang, J
Michiels, P
Xie, R
Krogmeier, J
Buckmaster, D
Ault, A
Wang, Y
Zhang, Y
Layton, A
Noel, S
Balmos, A
Buckmaster, D
Krogmeier, J
Evans, J
Zhang, Y
Glavin, M
Byrne, D
Harkin, S.J
Basir, M.S
Krogmeier, J
Zhang, Y
Buckmaster, D
Zhang, Y
Bailey, J
Balmos, A
Castiblanco Rubio, F.A
Krogmeier, J
Buckmaster, D
Love, D
Zhang, J
Allen, M
Topics
Food Security and Precision Agriculture
Remote Sensing Application / Sensor Technology
Profitability and Success Stories in Precision Agriculture
Artificial Intelligence (AI) in Agriculture
Data Analytics for Production Ag
Edge Computing and Cloud Solutions
Type
Poster
Oral
Year
2016
2008
2018
2024
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Authors

Filter results7 paper(s) found.

1. Open Data for Food Quality and Food Security Control: a Case Study of the Czech Republic

Food quality and food security is of a high public interest in the European Union. In the Czech Republic, food quality and food security is under control of three different public authorities: the Czech Trade Inspection Authority (CTIA) that is affiliated with the Ministry of Industry and Trade of the Czech Republic, the Czech Agriculture and Food Inspection Authority (CAFIA) that is affiliated with the Ministry of Agriculture of the Czech Republic and the regional network of hygienic stations... M. Ulman, M. Stoces, J. Jarolimek, P. Simek

2. Thermal Characterization and Spatial Analysis of Water Stress in Cotton (Gossypium Hirsutum L.) and Phytochemical Composition Related to Water Stress in Soybean (Glycine Max)

Studies were designed to explore spatial relationships of water and/or heat stress in cotton and soybeans and to assess factors that may influence yield potential. Investigations focused on detecting the onset of water/heat stress in row crops using thermal and multispectral imagery with ancillary physicochemical data such as soil moisture status and photosynthetic pigment concentrations. One cotton field with gradations in soil texture showed distinct patterns in thermal imagery, matching patterns... S.J. Thomson, S.L. Defauw, P.J. English, J.E. Hanks, D.K. Fisher, P.N. Foster, P.V. Zimba

3. Evaluation of the Potential for Precision Agriculture and Soil Conservation at Farm and Watershed Scale: A Case Study

Precision agriculture and soil conservation have the potential to increase crop yield and economic return while reducing environmental impacts. Landform, spatial variability of soil processes, and temporal trends may affect crop N response and should be considered for precision agriculture. The objective of this research was to evaluate the viability of precision agriculture in improving N use efficiency and profitability at the farm and watershed level in western Canada. Two studies are described... M. Khakbazan, A. Moulin, J. Huang, P. Michiels, R. Xie

4. Use Cases for Real Time Data in Agriculture

Agricultural data of many types (yield, weather, soil moisture, field operations, topography, etc.) comes in varied geospatial aggregation levels and time increments. For much of this data, consumption and utilization is not time sensitive. For other data elements, time is of the essence. We hypothesize that better quality data (for those later analyses) will also follow from real-time presentation and application of data for it is during the time that data is being collected that errors can be... J. Krogmeier, D. Buckmaster, A. Ault, Y. Wang, Y. Zhang, A. Layton, S. Noel, A. Balmos

5. 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

6. Private Simple Databases for Digital Records of Contextual Events and Activities

Farmers’ commitment and ability to keep good records varies tremendously. Records and notes are often cryptic, misplaced, or damaged and for many, remain unused. If such information were recorded digitally and stored in the cloud, we immediately solve some access and consistency issues and make this data FAIR (findable, accessible, interoperable, reusable). More importantly, interoperable digital formats can also enable mining for insights and analysis... M.S. Basir, J. Krogmeier, Y. Zhang, D. Buckmaster

7. Enabling Field-level Connectivity in Rural Digital Agriculture with Cloud-based LoRaWAN

The widespread adoption of next-generation digital agriculture technologies in rural areas faces a critical challenge in the form of inadequate field-level connectivity. Traditional approaches to connecting people fall short in providing cost-effective solutions for many remote agricultural locations, exacerbating the digital divide. Current cellular networks, including 5G with millimeter wave technology, are urban-centric and struggle to meet the evolving digital agricultural needs, presenting... Y. Zhang, J. Bailey, A. Balmos, F.A. Castiblanco rubio, J. Krogmeier, D. Buckmaster, D. Love, J. Zhang, M. Allen