Addressing Addressing Illicit Tobacco Growth in Pakistan: Leveraging AI and Satellite Technology for Precise Monitoring and Effective Solutions

Authors

  • Waleed Khan University of Engineering and Technology Peshawar
  • Nasru Minallah National Center for Big Data and Cloud Computing, University of Engineering and Technology Peshawar, KP , Paksitan
  • Atif Sardar Department of Renewable Energy Engineering, University of Engineering and Technology Peshawar, Pakistan

Keywords:

Tobacco, Artificial Intelligence, Deep Learning, Recurrent Neural Networks, Remote Sensing, Sentinel-2, Planet-Scope

Abstract

The market share of illicit tobacco products in Pakistan has seen a significant surge in recent years. In 2022, it reached a staggering 42.5%. Since January 2023, there has been a sharp 32.5% increase in volumes of Duty Not Paid (DNP) products and a remarkable 67% surge in the quantities of smuggled cigarettes. This rise can be attributed to the unregistered and unlicensed tobacco cultivation in Pakistan. This sector has largely relied on conventional methods for data collection in the field, primarily managed by the country's crop statistical departments. The utilization of cutting-edge artificial intelligence techniques and satellite imagery for generating crop statistics has the potential to address this issue effectively. We established a synergy by combining images from two remote sensing satellites and collected field data to detect tobacco crops using Recurrent Neural Networks (RNN). The results affirm the effectiveness of these techniques in detecting and estimating the acreage of tobacco crops in the observed areas, particularly in a union council of the Swabi region. We conducted surveys to collect training and validation data through our proprietary smartphone application, GeoSurvey. The collected data was subsequently refined, preprocessed, and organized to prepare it for use with our deep learning algorithm. The model we developed for the detection and acreage estimation of tobacco crops is called Convolutional Long Short-Term Memory (ConvLSTM). We created two datasets from the acquired satellite images for comparison. Our experimentation results demonstrated that the use of ConvLSTM for the synergy of Sentinel-2 and Planet-Scope imagery yields higher training and validation accuracy, reaching 98.09% and 96.22%, respectively. In comparison, the use of time series Sentinel-2 images alone achieved training and testing accuracy of 97.78% and 95.56%.

Author Biographies

Waleed Khan, University of Engineering and Technology Peshawar

WALEED KHAN received the B.Sc. degree in electrical computer engineering from Comsats University Islamabad, Abbottabad Campus, Pakistan, in 2015, and the M.Sc. degree in computer systems engineering from the University of Engineering and Technology, Peshawar (UET Peshawar), Pakistan. He is currently pursuing the Ph.D. degree from the Department of Computer Systems Engineering, UET Peshawar. He is also working as a Team Lead with the National Center for Big Data and Cloud Computing (NCBC), UET Peshawar, Pakistan. His main area of research involves deep learning algorithms in the field of remote sensing.

Nasru Minallah, National Center for Big Data and Cloud Computing, University of Engineering and Technology Peshawar, KP , Paksitan

Dr. Nasru Minallah received the B.Sc. degree in computer systems engineering from the University of Engineering and Technology (UET), Peshawar, Pakistan, in 2006, the M.Sc. degree from LUMS, in 2006, and the Ph.D. degree from Southampton University, U.K., in 2010, with a focus in multimedia and its applications. Furthermore, he has been a Postdoctoral Fellow in France. He has published his worked in several IEEE conferences and journals. He is PI and Co-PI on several research grants worth more than $100K.

Atif Sardar, Department of Renewable Energy Engineering, University of Engineering and Technology Peshawar, Pakistan

Aiif Sardar Khan received his bachelor’s and master’s degree in electrical engineering from the University of Engineering and Technology Peshawar. He is currently pursuing the Ph.D. degree in energy harvesting. He was a Lecturer at the Department of Electrical Engineering, University of Engineering and Technology Peshawar, Pakistan. He has been a Lecturer with the Department of Renewable Energy Engineering, University of Engineering and Technology Peshawar, Pakistan, since January 2020. His current research interests include energy harvesting, power management, sensors, and wearable electronics.

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Published

2023-10-22

How to Cite

Khan, W., Minallah, N., & Khan, A. S. (2023). Addressing Addressing Illicit Tobacco Growth in Pakistan: Leveraging AI and Satellite Technology for Precise Monitoring and Effective Solutions. International Journal of Innovations in Science & Technology, 5(4), 424–439. Retrieved from https://journal.50sea.com/index.php/IJIST/article/view/549

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