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

Waleed Khan ()
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Waleed Khan: Department of Computer Science & Information Technology, University of Engineering and Technology, Peshawar, Pakistan

International Journal of Innovations in Science & Technology, 2023, vol. 5, issue 4, 424-439

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%.

Keywords: Tobacco; Artificial Intelligence; Deep Learning; Recurrent Neural Networks; Remote Sensing; Sentinel-2; Planet-Scope (search for similar items in EconPapers)
Date: 2023
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