Smart Detection Of Illicit Cannabis Plantations Using Remote Sensing Technology And Machine Learning
https://doi.org/10.24057/2071-9388-2025-3538
Abstract
Remote sensing technology and machine learning classifiers can be utilized to develop smart detection systems for illicit crops such as Cannabis sativa L. Machine learning algorithms for classifying medium-resolution optical satellite data can be compared to identify the best model for enhancing law enforcement’s detection of illicit crops efficiently and accurately. Remote sensing-based smart detection systems have been developed in South America and Central Asia; however, these methods cannot be used effectively for Indonesia due to high cloud coverage, geographical differences, and the smaller area of Cannabis sativa L. plantations. This research developed an agile methodology that employs backpropagation neural networks to analyze the statistical growth phenology of cannabis derived from multitemporal medium-resolution remote sensing data. Using datasets derived from Indonesian law enforcement eradication records, the method achieved 94% accuracy and a kappa coefficient of 0.9. Further, plant growth phenology based on vegetation index values from multitemporal data was used to assess the condition of identified cannabis plantations.
Supporting agencies: This research was supported by Bina Nusantara University, Research Organization for Aeronautics and Space, National Research, and Innovation Agency. The authors thank the Indonesian National Intelligence Agency, the Indonesian National Narcotics Agency, and the Research Organization for Electronics and Informatics, as well as the National Research and Innovation Agency, for their support.
About the Authors
Dedi Irawadi
Information Systems Management Department, Bina Nusantara University; Research Center for Satellite Technology, Research Organization for Aeronautics and Space, National Research and Innovation Agency (BRIN)
Indonesia
Indonesia
Jakarta, 11480; Bogor, 16310
Tuga Mauritsius
Information Systems Management Department, Bina Nusantara University
Indonesia
Indonesia
Jakarta, 11480
Dony Kushardono
Research Center for Geoinformatics, Research Organization for Electronics and Informatics, National Research and Innovation Agency (BRIN)
Indonesia
Indonesia
Bandung, 40135
Syarif Budhiman
Research Center for Geoinformatics, Research Organization for Electronics and Informatics, National Research and Innovation Agency (BRIN)
Indonesia
Indonesia
Bandung, 40135
Karunika Diwyacitta
Research Center for Geoinformatics, Research Organization for Electronics and Informatics, National Research and Innovation Agency (BRIN)
Indonesia
Indonesia
Bandung, 40135
Bayu S. Adhitama
Research Center for Geoinformatics, Research Organization for Electronics and Informatics, National Research and Innovation Agency (BRIN)
Indonesia
Indonesia
Bandung, 40135
Fauzan A. Ayubi
Research Center for Geoinformatics, Research Organization for Electronics and Informatics, National Research and Innovation Agency (BRIN)
Indonesia
Indonesia
Bandung, 40135
Olivia Maftukhaturrizqoh
Research Center for Geoinformatics, Research Organization for Electronics and Informatics, National Research and Innovation Agency (BRIN)
Indonesia
Indonesia
Bandung, 40135
Ika S. Supriyani
Research Center for Geoinformatics, Research Organization for Electronics and Informatics, National Research and Innovation Agency (BRIN)
Indonesia
Indonesia
Bandung, 40135
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Review
For citations:
Irawadi D., Mauritsius T., Kushardono D., Budhiman S., Diwyacitta K., Adhitama B.S., Ayubi F.A., Maftukhaturrizqoh O., Supriyani I.S. Smart Detection Of Illicit Cannabis Plantations Using Remote Sensing Technology And Machine Learning. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY. 2025;18(1):130-138. https://doi.org/10.24057/2071-9388-2025-3538
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