A Uav-based multisensor framework for legal industrial Cannabis monitoring and open-access dataset development.

Genta Rexha, Ina Papadhopulli, Aleksandër Biberaj, Elson Agastra, Enida Sheme, Elinda Meçe

Abstract read

In one paragraph

Article in Data in brief, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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5 · Who and what money

Authors and funding

6 authors.

Genta RexhaDepartment of Electronics and Telecommunications, Faculty of Information Technology, Polytechnic University of Tirana, Albania.

Ina PapadhopulliDepartment of Computer Engineering, Faculty of Information Technology, Polytechnic University of Tirana, Albania.

Aleksandër BiberajDepartment of Electronics and Telecommunications, Faculty of Information Technology, Polytechnic University of Tirana, Albania.

Elson AgastraDepartment of Electronics and Telecommunications, Faculty of Information Technology, Polytechnic University of Tirana, Albania.

Enida ShemeDepartment of Computer Engineering, Faculty of Information Technology, Polytechnic University of Tirana, Albania.

Elinda MeçeDepartment of Computer Engineering, Faculty of Information Technology, Polytechnic University of Tirana, Albania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Industrial hemp cultivation is expanding and requires reliable monitoring for legal compliance and agricultural management. This paper presents a standardized UAV-based multisensor framework designed for Cannabis sativa L. It integrates RGB, multispectral, and thermal imaging as core modules, with hyperspectral and LiDAR as optional extensions. The framework sets protocols for sensor integration, flight planning, field measurements, and annotation, ensuring datasets that meet EU altitude limits (≤120 m AGL). Multi-altitude and multi-time-of-day acquisitions are proposed to capture spatial and diurnal variability. These data improve model robustness for phenotyping, stress detection, and THC compliance verification. Potential applications include precision agriculture, breeding, regulatory monitoring, environmental assessment, and illicit crop detection. Open-access datasets generated through this framework will support reproducibility, machine learning development, and collaboration among researchers, farmers, and regulators.

Indexed as

Cannabis sativa L.Dataset standardizationIndustrial hempMachine learning classificationMultisensor imagingRegulatory complianceUAV remote sensing

Identifiers

PMID41624300

DOI10.1016/j.dib.2026.112463

PMCPMC12855589

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LicenceCC BY-NC

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.