A
Ahmed A. Hashem Data-verified
Affiliation confirmed via AI analysis of OpenAlex, ORCID, and web sources.
Researcher
faculty
6 h-index
17 pubs
213 cited
Research Areas
Links
Biography and Research Information
OverviewAI-generated summary
Ahmed A. Hashem's research focuses on the application of remote sensing technologies and advanced analytical methods to address agricultural challenges. His work investigates the use of unmanned aerial vehicles (UAVs) and deep learning for detecting intra-field variations in crop yield, specifically in rice. Hashem also studies the impact of soil amendments, such as gypsum and compost, and the application of nanoparticles to improve soil properties and crop productivity, particularly in salt-affected environments and for faba beans. His research extends to understanding the influence of groundwater and soil salinity on evapotranspiration using remote sensing, and optimizing water recharge into aquifers. Hashem has published on topics including nitrogen deficiency determination in maize, optimizing water recharge, and utilizing multispectral imagery for phenotype prediction. He has collaborated with several researchers at Arkansas State University, including Emily S. Bellis and John W. Nowlin, on multiple publications.
Metrics
- h-index: 6
- Publications: 17
- Citations: 213
Selected Publications
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A maximal overlap discrete wavelet packet transform coupled with an LSTM deep learning model for improving multilevel groundwater level forecasts (2024)
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Optimizing the quantity of recharge water into a sedimentary aquifer through infiltration galleries using a surrogate assisted coupled simulation–optimization approach (2024)
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Small Unmanned Aircraft Systems and Agro-Terrestrial Surveys Comparison for Generating Digital Elevation Surfaces for Irrigation and Precision Grading (2023)
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A Maximal Overlap Discrete Wavelet Packet Transform Coupled with an LSTM Deep Learning Model for Improving Multilevel Groundwater Level Forecasts (2023)
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The combined impact of shallow groundwater and soil salinity on evapotranspiration using remote sensing in an agricultural alluvial setting (2023)
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Effect of Gypsum, Compost, and Foliar Application of Some Nanoparticles in Improving Some Chemical and Physical Properties of Soil and the Yield and Water Productivity of Faba Beans in Salt-Affected Soils (2023)
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NAPPN Annual Conference Abstract: Characterizing Rice Nitrogen Use Phenotypes from Multitemporal UAV Imagery with Manifold Learning (2022)
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Early cascade rice irrigation shutoff (ECIS) conserves water: implications for cascade flood automation (2022)
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Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning (2022)
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Determining nitrogen deficiencies for maize using various remote sensing indices (2022)
Collaboration Network
Top Collaborators
Michele L. Reba
United States Department of Agriculture (US)
6 shared publications
- Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning
- Optimizing the quantity of recharge water into a sedimentary aquifer through infiltration galleries using a surrogate assisted coupled simulation–optimization approach
- Small Unmanned Aircraft Systems and Agro-Terrestrial Surveys Comparison for Generating Digital Elevation Surfaces for Irrigation and Precision Grading
- Early cascade rice irrigation shutoff (ECIS) conserves water: implications for cascade flood automation
- A maximal overlap discrete wavelet packet transform coupled with an LSTM deep learning model for improving multilevel groundwater level forecasts
Showing 5 of 6 shared publications
Emily S. Bellis
University of Arkansas System
4 shared publications
In database
- Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning
- Optimizing the quantity of recharge water into a sedimentary aquifer through infiltration galleries using a surrogate assisted coupled simulation–optimization approach
- Manifold and spatiotemporal learning on multispectral unoccupied aerial system imagery for phenotype prediction
- NAPPN Annual Conference Abstract: Characterizing Rice Nitrogen Use Phenotypes from Multitemporal UAV Imagery with Manifold Learning
John W. Nowlin
University of Arkansas System
4 shared publications
In database
- Optimizing the quantity of recharge water into a sedimentary aquifer through infiltration galleries using a surrogate assisted coupled simulation–optimization approach
- Small Unmanned Aircraft Systems and Agro-Terrestrial Surveys Comparison for Generating Digital Elevation Surfaces for Irrigation and Precision Grading
- A maximal overlap discrete wavelet packet transform coupled with an LSTM deep learning model for improving multilevel groundwater level forecasts
- A Maximal Overlap Discrete Wavelet Packet Transform Coupled with an LSTM Deep Learning Model for Improving Multilevel Groundwater Level Forecasts
J.H. Massey
United States Department of Agriculture (US)
3 shared publications
- Determining nitrogen deficiencies for maize using various remote sensing indices
- Small Unmanned Aircraft Systems and Agro-Terrestrial Surveys Comparison for Generating Digital Elevation Surfaces for Irrigation and Precision Grading
- Early cascade rice irrigation shutoff (ECIS) conserves water: implications for cascade flood automation
Fared Farag
Arkansas State University
3 shared publications
In database
- “Version 1.3.0 pyfao56: FAO-56 evapotranspiration in Python”
- Manifold and spatiotemporal learning on multispectral unoccupied aerial system imagery for phenotype prediction
- NAPPN Annual Conference Abstract: Characterizing Rice Nitrogen Use Phenotypes from Multitemporal UAV Imagery with Manifold Learning
Dilip Kumar Roy
Bangladesh Agricultural Research Institute (BD)
3 shared publications
- Optimizing the quantity of recharge water into a sedimentary aquifer through infiltration galleries using a surrogate assisted coupled simulation–optimization approach
- A maximal overlap discrete wavelet packet transform coupled with an LSTM deep learning model for improving multilevel groundwater level forecasts
- A Maximal Overlap Discrete Wavelet Packet Transform Coupled with an LSTM Deep Learning Model for Improving Multilevel Groundwater Level Forecasts
Deborah L. Leslie
University of Memphis (US)
3 shared publications
- Optimizing the quantity of recharge water into a sedimentary aquifer through infiltration galleries using a surrogate assisted coupled simulation–optimization approach
- A maximal overlap discrete wavelet packet transform coupled with an LSTM deep learning model for improving multilevel groundwater level forecasts
- A Maximal Overlap Discrete Wavelet Packet Transform Coupled with an LSTM Deep Learning Model for Improving Multilevel Groundwater Level Forecasts
Brayden W. Burns
Arkansas State University
2 shared publications
In database
- Determining nitrogen deficiencies for maize using various remote sensing indices
- Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning
V. Steven Green
University of Arkansas System
2 shared publications
In database
- Determining nitrogen deficiencies for maize using various remote sensing indices
- Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning
Jason Causey
Arkansas State University
2 shared publications
In database
- Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning
- Manifold and spatiotemporal learning on multispectral unoccupied aerial system imagery for phenotype prediction
Hesham M. Aboelsoud
Agricultural Research Center (EG)
2 shared publications
- Effect of Gypsum, Compost, and Foliar Application of Some Nanoparticles in Improving Some Chemical and Physical Properties of Soil and the Yield and Water Productivity of Faba Beans in Salt-Affected Soils
- The combined impact of shallow groundwater and soil salinity on evapotranspiration using remote sensing in an agricultural alluvial setting
Aaron M. Shew
Arkansas State University
1 shared publication
In database
- Determining nitrogen deficiencies for maize using various remote sensing indices
Maria Arlene Adviento‐Borbe
United States Department of Agriculture (US)
1 shared publication
- Determining nitrogen deficiencies for maize using various remote sensing indices
Mohamed Milad
Arkansas State University
1 shared publication
In database
- Determining nitrogen deficiencies for maize using various remote sensing indices
Benjamin R. K. Runkle
United States Department of Agriculture
1 shared publication
In database
- Detecting Intra-Field Variation in Rice Yield With Unmanned Aerial Vehicle Imagery and Deep Learning
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