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Turner, I
Tóth, G
Boiko, I
Bakshi, A
Basir, M.S
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Authors
Boiko, I
Lang, V
Tóth, G
Csenki, S
Dafnaki, D
Turner, I
Kerry, R
Jensen, R
Woolley, E
Hansen, N
Hopkins, B
Bari, M.A
Bakshi, A
Witt, T
Caragea, D
Jagadish, K
Felderhoff, T
Pramanik, S
Choton, J
Basir, M.S
Krogmeier, J
Zhang, Y
Buckmaster, D
Topics
Global Proliferation of Precision Agriculture and its Applications
Geospatial Data
Drainage Optimization and Variable Rate Irrigation
Big Data, Data Mining and Deep Learning
Data Analytics for Production Ag
Type
Poster
Oral
Year
2012
2022
2024
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Filter results5 paper(s) found.

1. System Approach to Implementing Precision Agriculture in Ukraine

... I. Boiko

2. Comparison and Validation of Different Soil Survey Techniques to Support a Precision Agricultural System

The data need of precision agriculture has resulted in an intensive increase in the number of modern soil survey equipment and methods available for farmers and consultants. In many cases these survey methods cannot provide accurate information under the used environmental conditions. On a 36 hectare experimental field, several methods have been compared to identify the ones which can support the PA system the best. The methods included contact and non contact soil scanning, yield mapping, high... V. Lang, G. Tóth, S. Csenki, D. Dafnaki

3. Investigation of Automated Analysis of Snowmelt from Time-series Sentinel 2 Imagery to Inform Spatial Patterns of Spring Soil Moisture in the American Mountain West

Variable rate irrigation of crops is a promising approach for saving water whilst maintaining crop yields in the semi-arid American Mountain West – much of which is currently experiencing a mega drought. The first step in determining irrigation zones involves characterizing the patterns of spatial variation in soil moisture and determining if these are relatively stable temporally in relation to topographic features and soil texture. Characterizing variable rate irrigation zones is usually... I. Turner, R. Kerry, R. Jensen, E. Woolley, N. Hansen, B. Hopkins

4. Deep Learning to Estimate Sorghum Yield with Uncrewed Aerial System Imagery

In the face of growing demand for food, feed, and fuel, plant breeders are challenged to accelerate yield potential through quick and efficient cultivar development. Plant breeders often conduct large-scale trials in multiple locations and years to address these goals. Sorghum breeding, integral to these efforts, requires early, accurate, and scalable harvestable yield predictions, traditionally possible only after harvest, which is time-consuming and laborious. This research harnesses high-throughput... M.A. Bari, A. Bakshi, T. Witt, D. Caragea, K. Jagadish, T. Felderhoff

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