International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

CinneX: Integrating Digital Technologies for Sustainable Cinnamon Farming in Sri Lanka

Geethanjali WimalaratneFaculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri LankaDr. Lakmini AbeywardhanaFaculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri LankaDeshapriya V.P.GFaculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri LankaWijekoon W.M.T.NFaculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri LankaBandara D.H.M.A.K.PFaculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri LankaNayanajith K.A.D.BFaculty of Computing, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

Vol 9 No 6 (2025): Volume 9, Issue 6, June 2025 | Pages: 302-308

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 07-07-2025

doi Logo doi.org/10.47001/IRJIET/2025.906041

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Abstract

Sri Lanka’s cinnamon industry plays a vital role in the nation’s economy, but it faces significant challenges, including volatility in prices, disease management, inconsistencies in classification and misidentification of species. This research, titled Integrating Digital Technologies for Sustainable Cinnamon Farming in Sri Lanka, aims to address these issues through four key technological innovations. First, a predictive analytics system that uses machine learning algorithms and time series analysis will forecast cinnamon prices, providing farmers with real-time market insights through a mobile application and web dashboard. Second, an advanced image processing and machine learning-based system will detect and assess cinnamon leaf spot disease, Black Sooty Mold Disease, Leaf Gall Forming Louse and Leaf Gall Forming Mites Disease ensuring timely and accurate treatment recommendations to minimize crop damage. Third, an automated cinnamon-grade identification system that uses image processing and machine learning will enhance the accuracy and standardization of cinnamon grading, improving market value and quality assurance. Lastly, a species identification system will be developed to authenticate different types of cinnamon leaves using computer vision techniques, preventing fraud, and ensuring product integrity. By integrating these digital technologies, this research contributes to sustainable cinnamon cultivation, empowers farmers with data-driven decision-making tools, improves economic stability, and improves global competitiveness of Sri Lankan cinnamon products.

Keywords

Cinnamon farming, machine learning, image processing, price prediction, disease detection, quality assessment, species identification, sustainable agriculture


Citation of this Article

Geethanjali Wimalaratne, Dr. Lakmini Abeywardhana, Deshapriya V.P.G, Wijekoon W.M.T.N, Bandara D.H.M.A.K.P, & Nayanajith K.A.D.B. (2025). CinneX: Integrating Digital Technologies for Sustainable Cinnamon Farming in Sri Lanka. International Research Journal of Innovations in Engineering and Technology - IRJIET, 9(6), 302-308. Article DOI https://doi.org/10.47001/IRJIET/2025.906041

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