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Roehrdanz, P
Sela, S
Kemeshi, J.O
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Authors
van Es, H
Sela, S
Marjerison, R
Moebiu-Clune, B
Schindelbeck, R
Moebius-Clune, D
Sela, S
van-Es, H
McLellan, E
Melkonian, J
Marjerison , R
Constas, K
KC, K
Hannah, L
Roehrdanz, P
Donatti, C
Fraser, E
Berg, A
Saenz, L
Wright, T.M
Hijmans, R.J
Mulligan, M
Sela, S
Graff, N
Mizuta, K
Miao, Y
Sela, S
Kemeshi, J.O
Gummi, S
Chang, Y
Gummi, S
Kemeshi, J.O
Chang, Y
Kemeshi, J.O
Chang, Y
Yadav, P.K
Alahe, M
Alahe, M
Kemeshi, J.O
Chang, Y
Won, K
Yang, X
Sher, M
Alahe, M
Chang, Y
Kemeshi, J.O
Gummi, S
Menendez III, H
Alahe, M
Gummi, S
Kemeshi, J.O
Chang, Y
Topics
Decision Support Systems in Precision Agriculture
Precision Nutrient Management
Geospatial Data
Site-Specific Nutrient, Lime and Seed Management
Big Data, Data Mining and Deep Learning
Drivers and Barriers to Adoption of Precision Ag Technologies or Digital Agriculture
Proximal and Remote Sensing of Soils and Crops (including Phenotyping)
Edge Computing and Cloud Solutions
Site-Specific Pasture Management
Precision Agriculture and Global Food Security
Type
Oral
Year
2016
2018
2022
2024
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Filter results11 paper(s) found.

1. Comparing Adapt-N to Static N Recommendation Approaches for US Maize Production

Large temporal and spatial variability in soil N availability leads many farmers across the US to over apply N fertilizers in maize (Zea Mays L.) production environments, often resulting in large environmental N losses.  Static N recommendation tools are typically promoted in the US, but new dynamic model-based tools allow for more precise and adaptive N recommendations that account for specific production environments and conditions. This study compares two static N recommendation tools,... H. Van es, S. Sela, R. Marjerison, B. Moebiu-clune, R. Schindelbeck, D. Moebius-clune

2. Using the Adapt-N Model to Inform Policies Promoting the Sustainability of US Maize Production

Maize (Zea mays L.) production accounts for the largest share of crop land area in the U.S. It is the largest consumer of nitrogen (N) fertilizers but has low N Recovery Efficiency (NRE, the proportion of applied N taken up by the crop). This has resulted in well-documented environmental problems and social costs associated with high reactive N losses associated with maize production. There is a potential to reduce these costs through precision management, i.e., better application timing, use... S. Sela, H. Van-es, E. Mclellan, J. Melkonian, R. Marjerison , K. Constas

3. Using Geospatial Data to Assess How Climate Change May Affect Land Suitability for Agriculture Production

Finding solutions to the challenge of sustainably feeding the world’s growing population is a pressing research need that cuts across many disciplines including using geospatial data. One possible area could be developing agricultural frontiers. Frontiers are defined as land that is currently not cultivated but that may become suitable for agriculture under climate change. Climate change may drive large-scale geographic shifts in agriculture, including expansion in cultivation at the thermal... K. Kc, L. Hannah, P. Roehrdanz, C. Donatti, E. Fraser, A. Berg, L. Saenz, T.M. Wright, R.J. Hijmans, M. Mulligan

4. Spatially Explicit Prediction of Soil Nutrients and Characteristics in Corn Fields Using Soil Electrical Conductivity Data and Terrain Attributes

Site specific nutrient management (SSNM) in corn production environments can increase nutrient use efficiency and reduce gaseous and leaching losses. To implement SSNM plans, farmers need methods to monitor and map the spatial and temporal trends of soil nutrients. High resolution electrical conductivity (EC) mapping is becoming more available and affordable. The hypothesis for this study is that EC of the soil, in conjunction with detailed terrain attributes, can be used to map soil nutrients... S. Sela, N. Graff, K. Mizuta, Y. Miao

5. From Fragmented Data to Unified Insights: Leveraging Data Standardization Tools for Better Collaboration and Agronomic Big Data Analysis

The quantity and scope of agronomic data available for researchers in both industry and academia is increasing rapidly. Data sources include a myriad of different streams, such as field experiments, sensors, climatic data, socioeconomic data or remote sensing. The lack of standards and workflows frequently leads agronomic data to be fragmented and siloed, hampering collaboration efforts within research labs, university departments, or research institutes. Researchers and businesses therefore allocate... S. Sela

6. R2B2 Project: Design and Construction of a Low-cost and Efficient Autonomous UGV For Row Crop Monitoring

Driving the adoption of agricultural technological advancements like Unmanned Ground Vehicles (UGVs) by small-scale farmers (SSFs) is a major concern for researchers and agricultural organizations. They aim for the adoption of precision farming (PF) by SSFs to increase crop yield to meet the increasing demand for food due to population growth. In the United States, the cost of purchasing and maintaining rugged UGVs capable of precision agricultural operations stands as a barrier to the adoption... J.O. Kemeshi, S. Gummi, Y. Chang

7. Botanix Explorer (BX1): Precision Plant Phenotyping Robot Detecting Stomatal Openings for Precision Irrigation and Drought Tolerance Experiments

Under drought conditions, the kidney-shaped organs on the epidermal surface of plants, called stomata, are crucial to plant health. During transpiration, the stomata, which resemble pores, open and close. When the rate of photosynthesis is balanced, plants can withstand droughts by decreasing their stomatal transpiration. Drought-stressed plants are characterized by a higher number of open stomata. Measuring the pore aperture ratio is essential for precisely quantifying the degree of stomatal... S. Gummi, J.O. Kemeshi, Y. Chang

8. Comparing Global Shutter and Rolling Shutter Cameras for Image Data Collection in Motion on a UGV

In a bid to drive the adoption of precision farming (PF) technology by reducing the cost of developing an Unmanned Ground Vehicle (UGV), during the Reduction-To-Below-Two grand (R2B2) project we compared Arducam’s AR0234, a global shutter camera (GSC) to their IMX462, a rolling shutter camera (RSC). Since the cost of the AR0234 is approximately three times the price of the IMX462, the comparison was done to determine the possibility of using the latter for image data collection in place... J.O. Kemeshi, Y. Chang, P.K. Yadav, M. Alahe

9. Securing Agricultural Data with Encryption Algorithms on Embedded GPU Based Edge Computing Devices

Smart Agriculture (SA) has captured the interest of both the agricultural business and the scientific community in recent years. Overall, SA aims to help the agricultural and food industry to avoid crop failures, loss of revenues as well as help farmers use inputs (such as fertilizers and pesticides) more efficiently by utilizing Internet of Things (IoT) devices and computing systems. However, rapid digitization and reliance on data-driven technologies create new security threats that can defeat... M. Alahe, J.O. Kemeshi, Y. Chang, K. Won, X. Yang, M. Sher

10. Design of an Automatic Travelling Electric Fence System for Sustainable Grazing Management

Fences are used in Precision Livestock Farming (PLF) to prevent herbivores from overgrazing and under grazing forages. While effective in controlling animal entry and exit, traditional fences are not flexible enough to meet the needs of both foraging animals and plants in terms of both nutrient availability and physiological demands. An electric fencing system is a form of traditional fencing that employs an electric charge to create a barrier and dissuade animals or people from crossing it. Even... M. Alahe, Y. Chang, J.O. Kemeshi, S. Gummi, H. Menendez iii

11. Securing Agricultural Imaging Data in Smart Agriculture: a Blockchain-based Approach to Mitigate Cybersecurity Threats and Future Innovations

Smart agriculture (SA) is a new technology that combines the Internet of Things (IoT) with a variety of smart devices, such as drones, unmanned ground vehicles (UGVs), and computer systems. The integration of technology improvements in SA has led to an increase in cybersecurity concerns, specifically pertaining to the protection of sensitive agricultural image data. It’s necessary to better understand SA network systems; establish stronger network structures; identify different types and... M. Alahe, S. Gummi, J.O. Kemeshi, Y. Chang