Research Publications Authored by SLIIT Staff
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This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.
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Publication Open Access Unlocking cinnamon export success: Key determinants from the world's top five producers(Public Library of Science,PLOS ONE, 2025-12-11) Wisenthige, K; Jayathilaka, R; Dabare, U; Marasinghe, T; Radeesha, M; Ann, F; Kavindya, NThe purpose of this research study is to identify the factors affecting cinnamon export income (CEI) in the main five cinnamon export countries, namely China, Sri Lanka, Indonesia, Madagascar and Vietnam for the period from 1992–2022. Secondary data was sourced from the Food and Agriculture Organization and World Bank. Based on the past literature, it has been found out that production volume (PV), domestic consumption (DC), exchange rate (ER) and cultivated land area (CLA) significantly impact on CEI. Simple Linear Regression models were applied to analyse the impact of the identified factors affecting CEI in the present study. The findings revealed, PV negatively impacts the export income of cinnamon in China, Sri Lanka, and Vietnam, while having a positive impact on Indonesia and Madagascar. Moreover, while DC appears to have a positive impact in Sri Lanka, it has a negative impact in China, Vietnam, Indonesia and Madagascar for the same. Accordingly, ER is positive for countries Madagascar, Sri Lanka, and Vietnam while adverse for Indonesia and China. In addition, the study proved that CLA positively influences CEI of China, Vietnam, and Madagascar but negatively for Sri Lanka and Indonesia. Consequently, the findings from this study greatly assist policymakers, exporters, and the industry professionals in executing strategies to enhance the export income & thereof export practices of cinnamon. Finally, this research addresses several gaps in cinnamon export studies, supporting sustainable growth and competitiveness in the sector.Publication Embargo Image Processing-Based Solution to Repel Crop-Damaging Wild Animals(Springer, 2023-02-03) Fernando, W. P. S.; Madhubhashana, I. K.; Gunasekara, D. N. B. A.; Gogerly, Y. D.; Karunasena, A; Supunya, RTwo-thirds of Sri Lanka’s population is directly dependent on agriculture, which generates one-third of the nation’s GDP. However, crop efficiency in Sri Lanka has declined over the years due to several issues including sub-farm maintenance, destruction caused by wild animals, and unethical farming practices. Among them, the destruction caused by wild animals has led to conflicts between animals and humans causing loss of both animals and human lives in the past. There are a number of technical solutions proposed to solve the above problem, especially in the form of animal repellants. However, such solutions have several limitations, such as the small number of animal groups to be identified and the short distances they can be detected, and the lack of understanding of harmful animal populations. This research proposes an animal-repellent methodology considering several features of animals such as colors, coats, shape, and noise made by animals both in daytime and nighttime. The number of animals approaching crops is also detected and the behavior of animals is monitored to avoid false alarms. The research uses a wide range of techniques such as image processing and deep learning for the above purpose on audio, visual, and image data sets collected from the mentioned animal groups. The solution demonstrated a 90% accuracy for animal identification during the day, and 84% accuracy for animal 2 W. P. S. Fernando et al. identification at night, whereas the accuracy of studying animal behavior patterns is 90% and animal sounds were identified with 87% accuracyPublication Embargo Analysis of the ‘Toll Free Agricultural Advisory Service’ Data as Decision Support Tool for the Department of Agriculture(IEEE, 2022-07-18) Rajapaksha, N; Dias, NThe Department of Agriculture’s Toll-Free Agricultural Advisory Service was formed with the 1920 short code and is connected to all land and mobile telephone service providers in Sri Lanka. This short code allowed farmers and other stakeholders to contact technical officers which Agriculture Instructors immediately. All the information was gathered into the 1920 call center database. Farmers all over the island bring their agricultural problems to the 1920 Agricultural Advisory Service. Nevertheless, it can be seen that they do not do any analysis of these problems. This big data if properly examined has the potential to assist the country on a massive scale in the future. This study for carrying out to explore the possibility of introducing decision support for the 1920 reporting system to generate enhanced analytics and to make it easier to make informed decisions by the top management of the Department of Agriculture, more efficiently and effectively than the reporting method previously.Publication Embargo CEYLAGRO: INFORMATION TECHNOLOGICAL APPROACH FOR AN OPTIMIZED AND CENTRALIZED AGRICULITURE PLATFORM(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Kaushalya, T.V.H.; Wijewardana, B.Y.S.; Karunasena, A.; Kavishika, M.G.G.; Gamage, S.T.A; Weerasinghe, L.Sri Lankan Agriculture sector can be considered as a crucial component as it contributes 18% of country GDP. As native farmers still cling to inapplicable traditional theorems and practices to track customer’s vegetable consumption trends, they failed to assure a “good price” for their harvest. Also, the plants are prone to many diseases and pests’ attacks which causes loss of the harvest. Unreliable problem identification, poor knowledge on application of fertilizers and pesticides have caused the farmers to lose their profits. As a solution to mitigate these problems, this study has built a computerized system with a vegetable price prediction system and a plant disease, pest identification system. Taking Potato as an example, the parameters of the time series model were analyzed through experiment and has built the price predictor using ARIMA model. Also, with advanced Image processing and CNN techniques Plant disease, pest identifier has built. Desirable results of the entire system have been achieved with more than 94%-97% rate of accuracy. The ultimate goal of this study is to achieve the optimal growth of the sector by navigating the users for a quality and effective decision making by reliable market trends and problem identification.Publication Embargo IoT-based Monitoring System for Oyster Mushroom Farming(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Surige, Y.D.; Perera, W.S.M.; Gunarathna, P.K.N.; Ariyarathna, K.P.W.; Gamage, N.; Nawinna, D.Agriculture plays a major segment in the economy of Sri Lanka, a developing country. Mushrooms, farming is a popular option among the farmers as it consumes less space and less time for growing while offering a high nutritional value, but most farmers fail to obtain the best yield from their cultivations due to the defects and inefficiencies in the manual methods that are being presently used. This paper presents an ICT solution to avoid inefficiencies in the mushroom farming process. The system is developed focusing one of the popular mushroom type ‘Oyster Mushrooms’. The system offers four functionalities to perform mushroom farming precisely The system offers four functionalities to perform mushroom farming precisely. The Environmental Monitoring function is built with the support of a Long Short Term Memory (LSTM), Harvest time detection function is developed with the support of Convolutional Neural Networks (CNN) with Mobile Net V2 model, The Disease detection and control recommendation function is based on the support of CNN with mobile Net V2 model and the Yield prediction function is developed using the support of Long Short Term Memory (LSTM), The farmer is connected to the system through a mobile application. The system can monitor the environmental factors with an accuracy of 89% and the harvest time can be detected with an accuracy of 92%. Also, the system detects the mushroom diseases with an accuracy of 99% and predicts the monthly yield of a mushroom cultivation with an accuracy of 97%. The intense use of precise farming will eventually lead to high mushroom yields.