Ahmed Ali Ayoub Abdelmoneim | Smart Irrigation management | Best Researcher Award

Dr. Ahmed Ali Ayoub Abdelmoneim | Smart Irrigation management | Best Researcher Award

Researcher Private Consultant at Info Istituto Agronomico Mediterraneo di BariThis link is disabled, Italy

Ahmed Ali Ayoub Abdelmoneim is a researcher and consultant specializing in climate change and water scarcity impacts, with a strong background in irrigation modernization. Currently working at the Mediterranean Agronomic Institute of Bari, he focuses on technical assistance, coordination, and educational roles, promoting sustainable practices in agriculture ๐ŸŒ๐Ÿ’ง.

Publication Profile

scopus

Education

Ahmed holds a Masterโ€™s in Land and Water Resources Management from the Mediterranean Agronomic Institute of Bari ๐ŸŽ“๐ŸŒฑ. He also completed doctoral research at the University of California, Davis, focusing on hydraulic analysis of furrow irrigation systems ๐ŸŒพ. His education in Laser Applications and B.Sc. in Agricultural Engineering further solidifies his expertise in agricultural technologies ๐Ÿ”ฌ.

Experience

With a diverse career, Ahmed has contributed to multiple projects with UNDP and the Egyptian Environmental Affairs Agency ๐ŸŒ๐ŸŒฑ. He is a skilled consultant and lecturer on irrigation systems, environmental impacts, and automation technologies ๐Ÿง‘โ€๐Ÿซ. His work bridges technical research and practical application in water management and climate change mitigation ๐ŸŒ๐Ÿ’ง.

Awards and Honors

Ahmed has received numerous awards, including the Best Paper Award at the World Research Forum for Engineers and Researchers ๐Ÿ…. His work has been recognized by the MHRD for excellence in research and teaching contributions ๐Ÿ†.

Research Focus

Ahmed’s research interests include irrigation modernization, water scarcity, and automation technologies for sustainable agriculture ๐Ÿ’ง๐ŸŒพ. He specializes in smart irrigation systems, soil mapping technologies, and water management solutions for climate-resilient agriculture ๐ŸŒ. His work aims to improve agricultural efficiency and minimize environmental impact ๐ŸŒฑ.

Publications ๐Ÿ“–

Forecasting Blue and Green Water Footprint of Wheat (2024, Remote Sensing)
This article explores the prediction of water footprints (both blue and green) for wheat cultivation under various agro-climatic conditions in Egyptโ€™s Nile Delta. The study uses single, hybrid, and stacking ensemble machine learning algorithms to understand water consumption patterns, aiming to optimize water resource use in agriculture.

Advancements in Remote Sensing for Evapotranspiration Estimation (2024, Remote Sensing)
This review article discusses recent advancements in evapotranspiration estimation through remote sensing technologies. The authors provide a comprehensive analysis of temperature-based models and their effectiveness in understanding water use in crops, highlighting their significance for precision agriculture.

Towards Affordable Precision Irrigation (2024, Sustainability)
The study compares two irrigation methodsโ€”weather-based and soil water potential-basedโ€”for lettuce cultivation. It examines the use of low-cost IoT-tensiometers to improve irrigation efficiency, aiming to enhance water-use efficiency and reduce costs in precision agriculture.

OptGate: A Tool for Gated Pipe Systems (2023, Journal of Irrigation and Drainage Engineering)
The paper introduces OptGate, a tool designed to evaluate the performance of conventional and self-compensating gated pipe systems. The study aids in improving irrigation systems’ efficiency by analyzing their operational behavior under different conditions.

Internet of Things (IoT) for Soil Moisture Tensiometer Automation (2023, Micromachines)
This research discusses the integration of IoT technology into soil moisture tensiometers, enabling automated soil moisture monitoring for irrigation management. This innovation enhances irrigation efficiency by providing real-time soil moisture data.

IoT for Double Ring Infiltrometer Automation (2021, Computers and Electronics in Agriculture)
This article addresses the automation of double ring infiltrometers using IoT, which simplifies the process of measuring soil infiltration rates. It contributes to improving soil water management by providing accurate, real-time data on soil permeability.

Conclusion

The researcher has demonstrated a strong and diversified body of work that aligns with the global demands of climate change and water scarcity challenges. Their expertise in irrigation modernization, technical innovation, and teaching ability makes them a strong candidate for the “Best Researcher Award.” However, further emphasis on enhancing the dissemination of their work through publications and fostering greater interdisciplinary collaboration could elevate their research’s global impact and recognition.

Xin Lv| Crop Information Technology | Best Researcher Award

Dr. Changmin Shi | Crop Information Technology | Best Researcher Award

Deputy Director at Shihezi University, China

Xin Lv, Deputy Director at Shihezi University, is a distinguished agricultural scientist with expertise in crop cultivation, smart agriculture, and applied meteorology. With over three decades of experience, he has led numerous national and provincial projects, authored influential publications, and significantly contributed to advancing cotton production technology in China. His work integrates modern precision agriculture techniques, fostering technological progress and sustainable agricultural practices.

Publication Profile

scopus

๐ŸŽ“ Education

๐ŸŽ“ BSc in Agronomy โ€“ Shihezi University (1987) ๐ŸŽ“ MSc in Applied Meteorology โ€“ Nanjing Institute of Meteorology (1994 ย PhD in Crop Cultivation and Tillage Science โ€“ Shandong Agricultural University (2002)

๐Ÿ’ผ Experience

๐Ÿ”ง Deputy Director at Shihezi University ๐Ÿ“Š Hosted 19 national and 17 provincial-level projects ๐Ÿ“ Published 289 papers (58 SCI/EI) ๐Ÿ“š Authored 10 books and 15 technical regulations ๐Ÿค Collaborates with National Cotton Engineering Technology Research Center

๐Ÿ† Awards and Honors

๐Ÿฅ‡ First Prize, Xinjiang Scientific and Technological Progress (2015, 2020, 2023) ๐Ÿ… Invention Patent Award (2018) ๐Ÿ† Second Prize, National Science and Technology Progress Award (Collective) ๐ŸŒฑ Director of China Crop Society, VP of Smart Agriculture Committee

๐ŸŒฑ Research Focus

Xin Lv’s research targets precision agriculture, focusing on cotton production. His work addresses challenges like high costs and inefficiencies through advanced nutrient monitoring, water management, and pest control systems. He pioneered a cloud platform for water and fertilizer management, enhancing crop productivity and sustainability.

Publications

๐ŸŒต Warming effect of desert on cotton Verticillium wilt

Industrial Crops and Products โ€“ Examines how desert warming influences cotton disease distribution.

๐Ÿ›ฐ๏ธ Growth monitoring index for cotton using Sentinel-2A

Field Crops Research โ€“ Utilizes satellite data to monitor cotton growth on a large scale.

๐ŸŒฑ Improving soil quality predictions using spectral data fusion

Geoderma โ€“ Combines Vis-NIR and pXRF data for enhanced soil quality assessments.

๐ŸŒก๏ธ Mulching-induced warming impacts on cotton zones

Atmosphere โ€“ Assesses how mulching alters temperatures in machine-picked cotton fields.

๐Ÿ“Š Image-spectral fusion for cotton nitrogen monitoring

Computers and Electronics in Agriculture โ€“ Deep learning-based nitrogen content monitoring.

๐ŸŒ Soil nitrogen estimation using feature selection and stratification

Computers and Electronics in Agriculture โ€“ Increases accuracy in soil nitrogen estimation.

๐Ÿš UAV imagery reducing interference for cotton nitrogen monitoring

Frontiers in Plant Science โ€“ UAV-based imaging techniques for precise monitoring.

๐Ÿงช UAV RGB images for cotton nitrogen diagnostics

Notulae Botanicae Horti Agrobotanici โ€“ Uses UAVs to diagnose nitrogen content in cotton.

๐ŸŒฟ Vegetation indices fusion for cotton leaf area prediction

Frontiers in Plant Science โ€“ Enhances accuracy in leaf area prediction.

๐ŸŒฑ Estimating cotton leaf nitrogen with the PROSPECT model

Notulae Botanicae Horti Agrobotanici โ€“ Constructs models for estimating leaf nitrogen content.

 

Conclusion

XinLv’s extensive research output, leadership in national projects, and innovation in crop information technology position him as an outstanding candidate for the Best Researcher Award. His contributions have significantly advanced precision agriculture and smart farming practices, driving sustainable solutions to key agricultural challenges. With continued efforts in international collaborations and outreach, XinLvโ€™s impact on global agricultural innovation is expected to grow even further.