Research Publications
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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 VAPECA - Smart Agricultural and Analysis Monitoring System(Institute of Electrical and Electronics Engineers, 2022-10-15) Jithmal Pitigala, P. K. D. U; Laksahan, T. M. K; Hewapathirana, S. S; Sadeepika Herath, H. M. H; Chandrasiri, S; Nadeesa Pemadasa, M. GAgriculture dramatically contributes to the economy by creating a monetary future for developing nations. However, in Sri Lanka, the farmers have confined resources and encounter numerous challenges to enrich their crop productivity and prevail in the competitive business world. In the directive, the farmers' knowledge about export crops and weak decision- making needs to be exposed [1]. This study has built a mobile application with budget planning, determining plant conditions, weather forecasting, analyzing harvest quality, and a price prediction system to mitigate these hardships. This application would be utilized to manage three critical plants in Sri Lanka t for extraction and export. Those are Vanilla, Pepper, and Cardamom. The key technologies used for the system are deep learning and machine learning. The overall system obtained desirable outcomes with an accuracy rate higherthan 94%-97%. The ultimate intent of this study is to achieve the optimal growth of the agriculture sector by navigating the farmers to get maximum crop yield, quality, and effective decision-making through reliable market trends and to enhance the farmers' profitPublication Open Access CRITERIA FOR SELECTION OF SUITABLE SITES FOR RAINWATER HARVESTING IN THE MIDDLE EAST FOR AGRICULTURE USE: A REVIEW STUDY(SLIIT, 2022-02-11) Zubaidi, S; Harris, C; Carnacina, I; Abdellatif, M; AL-Hasani, BIn arid and semi-arid regions, rainwater harvesting represents an important alternate source of water supply around the globe. The middle east is an integrally dry zone and due to climate change, it has experienced a severe drought for the last decade that made the situation worse. Many countries are suffering from awful water crises due to increasing water demand for agriculture and rapid urbanization. Therefore, unconventional water supply resources practices such as using rainwater harvesting for agricultural purposes can be possibly used to tackle water scarcity. Numerous researchers have established different criteria and methods to identify suitable sites selection techniques for rainwater harvesting (RWH). The main objective of this study was to determine the most commonly effective methods and techniques that have been practiced in the middle-east region to summarize the best methods for rainwater harvesting site selection. These effective common methods of multi-criteria analysis (MCA) were discussed accordingly in this review paper and based on the previous studies the best applicable method was the integration between (MCA) method and the GIS technique. The study employed a method of gathering and recording the main criteria and techniques which were established in the last thirty years. It compared and categorized the main methodologies from previous studies and practices from international organizations and scientific research and identified the most common characteristics and sets of criteria suitable for use in arid and semi-arid regions. The techniques were diverse based on biophysical criteria to methods including socio-economic criteria, precisely in the last two decades. The most effective and significant criteria for suitable site selection of RWH in the arid and semi-arid regions found are: land use/land cover, slope, type of soil, rainfall intensity, streams distant and the cost. The success of RWH selection sites tended to increase when these criteria are measured and based on these criteria.Publication Embargo Analysis of the ‘Toll Free Agricultural Advisory Service’ Data as Decision Support Tool for the Department of Agriculture(IEEE, 2022-07-18) Dias, N; Rajapaksha, 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 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.