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Toledo, R
Alves Filho, R
Wijewardane, N
Zhang, Y
Zhijun, M
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Authors
Guangwei, W
Zhijun, M
Liping, C
Weiqiang, F
Jianjun, D
Wijewardane, N
Ge, Y
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
Andrade, M
da Silva Fonseca, J
Toledo, R
Martin Carbajal Gamarra, F
Andrade, M
Toledo, R
Alves Filho, R
Topics
Spatial Variability in Crop, Soil and Natural Resources
Proximal Sensing in Precision Agriculture
Profitability and Success Stories in Precision Agriculture
Artificial Intelligence (AI) in Agriculture
Data Analytics for Production Ag
Edge Computing and Cloud Solutions
Remote and Proximal Sensing of Soils and Crops
Wireless Sensor Networks, Edge Computing, and Farm Connectivity
Market Room Sponsors
Type
Poster
Oral
Year
2012
2016
2018
2024
2026
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Filter results9 paper(s) found.

1. Evaluation of Application Effect of the Laser Land Leveling Technology in Typical Areas of China

The technology of laser land leveling can improve the accuracy of land leveling and it is the important measure of improving irrigation efficiency and facilitating more uniform distribution of irrigation water. The technology is more widely used in China in... W. Guangwei, M. Zhijun, C. Liping, F. Weiqiang, D. Jianjun

2. Laboratory Evaluation of Two VNIR Optical Sensor Designs for Vertical Soil Sensing

Visible and near infrared reflectance spectroscopy (VNIR) is becoming an extensively researched technology to predict soil properties such as soil organic carbon, inorganic carbon, total nitrogen, moisture  for precision agriculture. Due to its rapid, non-destructive nature and ability to infer multiple soil properties simultaneously, engineers have been trying to develop proximal sensors based on the VNIR technology to enable horizontal soil sensing and mapping. Since the vertical variation... N. Wijewardane, Y. Ge

3. 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

4. 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

5. 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

6. 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

7. Estimation of Leaf Area in Hydroponic Microgreenhouses by Digital Image Processing and Color-Based Segmentation

Monitoring plant development is a fundamental pillar of precision agriculture, as leaf area is a primary indicator of photosynthetic capacity, transpiration rates, and overall biomass accumulation. In controlled environments such as hydroponic micro-greenhouses, real-time growth monitoring enables timely adjustments to nutrient film technique (NFT) parameters. However, conventional leaf area measurement methods often rely on invasive sampling or expensive, high-maintenance optical equipment, limiting... M. Andrade, J. Da Silva Fonseca, R. Toledo, F. Martin Carbajal Gamarra

8. Autonomous Edge Computing Station for Precision Soil and Climate Monitoring in Remote and Low-Connectivity Environments

The expansion of Precision Agriculture (PA) into remote rural areas, particularly in developing countries like Brazil, is frequently hindered by severe infrastructure constraints. Large-scale adoption of digital tools faces the dual challenge of limited 4G/5G connectivity at the field level (the "talhão") and the high costs associated with conventional electrification and industrial-grade equipment. These barriers disproportionately affect small and medium-sized farmers, creating... M. Andrade, R. Toledo

9. SOLOS E PLANTAS - Updates in the Analytics Market - Brazil vs China

... R. Alves Filho