Blessing Nnenna Azubuike  | Remote Sensing and Dairy Science | Research Excellence Award

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Mrs. Blessing Nnenna Azubuike  | Remote Sensing and Dairy Science | Research Excellence Award

University of Sydney | Australia

Blessing Nnenna Azubuike is a data scientist and data engineer specialising in environmental and institutional analytics, with a focus on applying sensor- and data-analytics to sustainable agricultural systems. She completed her doctoral studies in sensor and data analytics at the School of Life and Environmental Sciences, University of Sydney, working on remote sensing, machine learning and optimisation for grazing and feed-management. Her training in agriculture (with honours in Agricultural Extension and Rural Development) provides a strong foundation for bridging field-based knowledge and computational analytics. She has designed and implemented production-grade ETL pipelines, reproducible machine-learning workflows, and stakeholder-facing analytic dashboards — utilising Python, R, SQL, cloud platforms and Microsoft/Azure data tools. In her role as data scientist she developed and optimised scalable data pipelines for multi-source environmental and farm data, built predictive and deep-learning models for pasture and dairy system performance, and translated complex data into actionable insights for farm managers and industry stakeholders. Her work has contributed to improved pasture-cover estimation using satellite data, and to data-driven feed optimization for dairy herds. She also mentors junior analysts, embeds data governance into analytic systems, and fosters cross-disciplinary collaboration. Her h-index, total document count and citation count are currently being compiled.

Profile: Orcid 

Featured Publications 

Azubuike, B. N., Chlingaryan, A., Correa-Luna, M., Clark, C. E. F., & Garcia, S. C. (2025). A data-driven approach for optimising supplement allocation to individual lactating dairy cows in pasture-based systems. Smart Agricultural Technology, (Nov 2025), Article 101669.

Azubuike, B. N., Chlingaryan, A., Correa-Luna, M., Clark, C. E. F., & Garcia, S. C. (2025). Data augmentation and interpolation improves machine learning-based pasture biomass estimation from Sentinel-2 imagery. Remote Sensing, 17(23), 3787.

Mr. Ahmad Faraz Ishaq – Crop Yield Modeling – Best Researcher Award 

Posted on by Innovator Awards

Mr. Ahmad Faraz Ishaq - Crop Yield Modeling - Best Researcher Award 

Beihang University - Pakistan

Author Profile 

ORCID 

🎓 Early academic pursuits

Ahmad Faraz Ishaq laid the foundation for his academic journey with a strong passion for agronomy and its technological applications. he pursued an m.sc. (hons) in agronomy from the university of agriculture faisalabad, where he delved into crop growth optimization and sustainable agricultural practices. his early research focused on improving mungbean yield through optimized sowing dates and planting patterns, reflecting his keen interest in enhancing crop productivity. his academic excellence paved the way for his future endeavors in integrating geospatial technologies with agricultural research.

🌍 Professional endeavors

Ahmad Ishaq has built a distinguished career that seamlessly bridges agriculture and space technology. currently serving as a manager at suparco, he specializes in geo-spatial technologies for crop yield modeling, area estimation, and damage assessment caused by natural hazards. his expertise extends to monitoring crop health using ndvi and ndwi indices, ensuring precise and data-driven agricultural decisions. he also plays a crucial role in supporting agro-industries, particularly in sugarcane harvest monitoring. his professional journey reflects a deep commitment to leveraging space-based technologies for the advancement of precision agriculture.

🌿 Contributions and research focus

Ahmad has made groundbreaking contributions to agricultural research by integrating crop growth models, radiative transfer models, and machine learning to enhance crop trait estimation Crop Yield Modeling and yield prediction. his notable publications include "a synergistic framework for coupling crop growth, radiative transfer, and machine learning to estimate wheat crop traits in pakistan" and "a systematic review of radiative transfer models for crop yield prediction and crop traits." his research has introduced innovative methodologies that combine satellite data with machine learning to develop robust models for wheat yield prediction, significantly improving agricultural forecasting and resource management.

🏆 Accolades and recognition

Ahmad's contributions to agricultural research and precision farming have been widely acknowledged. his work has been published in prestigious journals like remote sensing, where he has co-authored influential studies on geospatial technologies for crop modeling. he has also contributed to practical agricultural knowledge, co-authoring articles in the international journal of Crop Yield Modeling agriculture and biology and publishing insights on wind break management in daily dawn. his ability to translate complex research into real-world applications has earned him recognition in both academic and professional circles.

👨‍💻 Impact and influence

Ahmad Ishaq's research has significantly influenced the field of precision agriculture, particularly in pakistan. his expertise in integrating space-based technologies with agronomy has provided farmers and policymakers with actionable insights for crop management. by employing machine learning and remote sensing data, he has enabled more accurate predictions of crop health Crop Yield Modeling and yield, reducing risks associated with climate change and natural disasters. his contributions have enhanced decision-making processes in the agricultural sector, making farming more efficient and sustainable.

🛠️ Legacy and future contributions

As he pursues a ph.d. in space technology and its application at beihang university, beijing, ahmad continues to push the boundaries of precision agriculture. his research aims to further integrate space-based innovations with agricultural practices, ensuring more sustainable and data-driven farming solutions. his legacy lies in his dedication to merging agronomy with cutting-edge technology, setting a precedent for future researchers and professionals in the field. through his continued efforts, he is shaping a future where precision agriculture plays a central role in global food security and environmental sustainability.

Notable Publications 

  • Title: Geospatial Robust Wheat Yield Prediction Using Machine Learning and Integrated Crop Growth Model and Time-Series Satellite Data
    Author(s): Rana Ahmad Faraz Ishaq, Guanhua Zhou, Guifei Jing, Syed Roshaan Ali Shah, Aamir Ali, Muhammad Imran, Hongzhi Jiang, Obaid-ur-Rehman
    Journal: Remote Sensing

  • Title: Assessing Above-Ground Biomass Dynamics and Carbon Sequestration Potential Using Machine Learning and Spaceborne LiDAR in Hilly Conifer Forests of Mansehra District, Pakistan
    Author(s): Muhammad Imran, Guanhua Zhou, Guifei Jing, Chongbin Xu, Yumin Tan, Rana Ahmad Faraz Ishaq, Muhammad Kamran Lodhi, Maimoona Yasinzai, Ubaid Akbar, Anwar Ali
    Journal: Forests

  • Title: A Systematic Review of Radiative Transfer Models for Crop Yield Prediction and Crop Traits Retrieval
    Author(s): Rana Ahmad Faraz Ishaq, Guanhua Zhou, Tian Chen, Yumin Tan, Guifei Jing, Hongzhi Jiang, Obaid-ur-Rehman
    Journal: Remote Sensing

  • Title: Contextual Band Addition and Multi-Look Inferencing to Improve Semantic Segmentation Model Performance on Satellite Images
    Author(s): Syed Roshaan Ali Shah, Obaid-Ur-Rehman, Yasir Shabbir, Rana Ahmad Faraz Ishaq
    Journal: Journal of Spatial Science

  • Title: Trans-Boundary Spatio-Temporal Analysis of Sentinel 5P Tropospheric Nitrogen Dioxide and Total Carbon Monoxide Columns Over Punjab and Haryana Regions with COVID-19 Lockdown Impact
    Author(s): Yasir Shabbir, Guanhua Zhou, Obaid-ur-Rehman, Syed Roshaan Ali Shah, Rana Ahmad Faraz Ishaq
    Journal: Environmental Monitoring and Assessment