International Conference on Advancements in Computing [ICAC]

Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/312

The International Conference on Advancements in Computing (ICAC) is organized by the Faculty of Computing of the Sri Lanka Institute of Information Technology (SLIIT) as an open forum for academics along with industry professionals to present the latest findings and research output and practical deployments in computing.

The primary objective of ICAC is to promote innovative research that addresses real-world challenges and contributes to the social well-being of communities. The conference provides a dynamic platform for researchers from around the world to present groundbreaking findings, exchange ideas, and establish meaningful collaborations.

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Author: search.filters.author.Krishara, JSubject: search.filters.subject.Cinnamon

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    CINNOVA: Advancing Sustainable Cinnamon Farming through AI and Collaborative Solutions
    (Institute of Electrical and Electronics Engineers Inc., 2025-12-09) Bandara D.; Fernando M.K.K.L; Senadeera N.A.D.N; De Silva R.C.T; Wijendra, D; Krishara, J
    Cinnamon is one of the most economically significant export crops in Sri Lanka. However, its cultivation is challenged by plant diseases, nutrient deficiencies, and inefficient harvesting practices, which reduce yield quality and productivity. Traditional methods for identifying plant health issues are time-consuming and require expert evaluation, often inaccessible to rural farmers. To address these limitations, this study introduces an AI-driven intelligent monitoring system for sustainable cinnamon cultivation, a mobile-based solution specifically designed to enhance cinnamon farming practices. The system leverages Artificial Intelligence (AI), Deep Learning (DL), and Image Processing techniques to support real-time plant health diagnostics. It integrates multiple AI-powered components for early detection of bark diseases such as Rough Bark Disease (RBD) and Canker Disease (CD) using a contrastive learning-based model, severity prediction of leaf diseases, including Leaf Gall and Leaf Blight, using the YOLO model, identification of nutrient deficiencies, particularly Magnesium and Potassium, through transfer learning and prediction of cinnamon bark maturity and quality grades utilizing spatial attention mechanisms based on diameter and color. Each model is optimized for mobile deployment to provide real-time feedback and enable efficient decision-making. This AI-driven approach enhances disease management and improves yield quality while promoting sustainable, data-driven cinnamon cultivation in Sri Lanka.