The Idea and the Challenge
Pakistan’s agriculture is volatile; the lack of national planning and limited technology results in uneven crop distribution and vulnerability to extreme weather conditions. Spatial planning would potentially lead to improvement in food security, income, market stability and resource use, benefiting policymakers, extension services and farmers.
The research would present a decision-support framework for national crop planning that merged field-level historical, environmental, and socioeconomic data with satellite mosaics and geospatial analysis for crop allocation.
The initial challenge was to find field-level historical records and continuous satellite imagery, correcting cloud, sensor issues, integrating aerial photos from drones, fixing inconsistent formats, missing data, and scarce ground truth. We would need to develop robust preprocessing pipelines for each sub-problem, leading to harmonization, and gap-filling, creating reliable inputs.
The Turning Point
To move further with my research, I enlisted the help of Dr. Shoab Amhed Khan, an excellent mentor who is not only well versed in multiple domains but had the leadership skills necessary for the development of this project. Further, the collaborations with The Centers extensive network helped in gaining useful insights with less struggle.
These collaborations and meetings majorly supported my project by reducing the data collection time and development of mini-modules for our project. The Center put together a small team of engineers for the development of our mosaicking module that took unstable and multiple feeds from a legacy drone system and converted it into an enhanced and stable video through a pro-processing pipeline. The separated feeds were fused together to create a super-mosaic to identify the issues within the agricultural fields.
Collaboration and Environment
The research-driven environment and the team of experts at The Center made this monumental task far less daunting. My supervisor, Dr. Shoab, demonstrated an exceptional attitude toward both research and people, instilling those same values in his team and in me. This culture created a workplace that was enjoyable and safe, particularly for women. As a mother of two young children, I deeply appreciate the support I received at The Center, which enabled me to continue my work with both passion and peace of mind.
The Breakthrough - The Outcome and Publication
Our work had three major modules. The first one is a preprocessing engine for aerial videos with multiple feeds embedded in a single file from legacy aerial platforms with no stabilization mechanism.
The system took these videos, separated, stabilized and enhanced each feed, generating a mosaic with different view angles. These mosaics were combined to create a super mosaic at the end.
The second module involved developing a Machine Learning prediction model that took historical data including environmental, non-environmental factors, and satellite-imagery derived indices to predict crop yield at the specified location.
The third module was a decision-support framework that integrated historical data from multiple channels, including predicted yields from individual farmers as well as data from the Ministry of Agriculture and its affiliated organizations.
This work resulted in two impact factor publications with details as under:
The Lasting Impact - Looking Forward
This project sharpened my technical and interpersonal abilities. I mastered new tools of GIS and mastered the agricultural landscape that was beyond my field otherwise. This helped me to become an out-of-box problem solver, enabling me to work on various practical domains of life.
I improved stakeholder engagement, data acquisition, project management, team leadership, scientific writing, and time-management while balancing family responsibilities. These skills prepared me for research, policy advisory, or industry roles focused on data-driven solutions.
My time at the Center for Advanced Research in Engineering was foundational, it deepened my passion for research and in applying remote sensing and machine learning to agriculture. equipping me with the tools and confidence to scale resilient, evidence-based systems nationwide.
Editor
Talha Khan
Tags
Agriculture, Crop Planning, Remote Sensing, Machine Learning, GIS, Decision-Support Systems
Sources
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Nida RASHEED, Waqar S. QURESHI, Shoab A. KHAN, Manshoor A. NAQVI, Eisa ALANAZI, AirMatch: An Automated Mosaicing System with Video Preprocessing Engine for Multiple Aerial Feeds, IEICE Transactions on Information and Systems, 2021, Volume E104.D, Issue 4, Pages 490-499, Released on J-STAGE April 01, 2021, Online ISSN 1745-1361, Print ISSN 0916-8532, https://doi.org/10.1587/transinf.2020EDK0003
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N. Rasheed, S. A. Khan, A. Hassan and S. Safdar, "A Decision Support Framework for National Crop Production Planning," in IEEE Access, vol. 9, pp. 133402-133415, 2021, doi: 10.1109/ACCESS.2021.3115801.