Scopus Index Publications

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This collection consists of all Scopus-indexed publications produced by SLIIT researchers. Scopus is recognized worldwide as a leading and reputable academic indexing database.

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    PublicationOpen Access
    Image processing techniques to identify tomato quality under market conditions
    (Elsevier B.V., 2024-03) Abekoon, T; Sajindra, H; Jayakody, J．A．D．C．A．; Samarakoon, E．R．J; Rathnayake, U
    Tomatoes are essential in both agriculture and culinary spheres, demanding rigorous quality assessment. It is highly advantageous to discern the maturity level and the time range post-harvesting of tomatoes in the market through visual analysis of their images. This research endeavors to forecast tomato quality by accurately determining the maturity level and the duration post-harvest, specifically tailored to Sri Lankan market conditions, with a particular focus on Padma tomatoes. It identifies maturity stages (Green, Breakers, Turning, Pink, Light Red, Red) and post-harvest dates using image processing techniques. Greenhouse-grown Padma tomatoes mimic market conditions for image capture, and Convolutional Neural Networks facilitate this analysis. Model 1, using ReLU and sigmoid activation functions, accurately classifies tomatoes with 99 % training and validation accuracy. Model 2, with seven classes, achieves 99 % training and 98 % validation accuracy using ReLU and softmax activation functions. Integration of the IPGRI/IITA 1998 classification method enhances tomato categorization. Efficient tomato image screening optimizes resource use. This study successfully determines Padma tomato post-harvest dates based on maturity stages, a significant contribution to tomato quality assessment under market conditions.
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    PublicationEmbargo
    DenGue CarB: Mosquito Identification and Classification using Machine Learning
    (2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Mohommed, M.; Rajakaruna, P.; Kehelpannala, N.; Perera, A.; Abeysiri, L.
    This research paper discusses a web-based application that assists Public Health Officers in the dengue identification process. The mosquito classification is done using image processing and machine learning techniques. The training models are developed using Convolutional Neural Networks Algorithm, Support Vector Machine Algorithm, and K-Nearest Neighbors Algorithm to validate the results to determine the most accurate and suitable algorithm. this paper discusses the previous related research work on its significance and drawbacks while highlighting design, methods, and implementation in the solution. We conclude that the CNN algorithm provides the highest accuracy among the machine learning techniques used.