Browsing by Author "Gamage, N"

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Agro-Mate: A Virtual Assister to Maximize Crop Yield in Agriculture Sector
(IEEE, 2021-12-09) Dayalini, S; Sathana, M; Navodya, P. R. N; Weerakkodi, R. W. A. I. M. N; Jayakody, A; Gamage, N
Information Technology plays a vital role in the agriculture industry. The main goal of the project is to develop a mobile application to support farmers to take accurate decisions and help them with activities such as soil quality determination, best crop selection, rice disease prediction, and disaster prediction for the wet zone of Sri Lanka. To achieve the main goal the project has incorporated advanced technologies such as Deep Learning, Image Processing (IP), Internet of Things (IoT), and Machine Learning that can support farmers or investors in a way to maximize yield. ‘Agro-Mate’ application is developed in a way to facilitate the agriculture industry. ‘Agro-Mate’ consists of four components such as soil quality determination and fertilizer recommendation, best crop selection, rice disease prediction and recommendation, and natural disaster prediction and providing the recommendation. Also, the application suggests fertilizer when soil is lacking quality and provides recommendations whenever rice diseases or natural disasters are identified. The usage of android mobile devices in agriculture is one of the key components of the sector's growth, which facilitates the farmer's inaccurate decision-making to gain more quality and quantity of crops. Agro-mate’ is more likely to increase the productivity of crops and indirectly increase the GDP of Sri Lanka.
Embargo
Agro-Mate: A Virtual Assister to Maximize Crop Yield in Agriculture Sector
(IEEE, 2021-12-09) Dayalini, S; Sathana, M; Navodya, P. R. N; Weerakkodi, R. W. A. I. M. N; Jayakody, A; Gamage, N
Information Technology plays a vital role in the agriculture industry. The main goal of the project is to develop a mobile application to support farmers to take accurate decisions and help them with activities such as soil quality determination, best crop selection, rice disease prediction, and disaster prediction for the wet zone of Sri Lanka. To achieve the main goal the project has incorporated advanced technologies such as Deep Learning, Image Processing (IP), Internet of Things (IoT), and Machine Learning that can support farmers or investors in a way to maximize yield. ‘Agro-Mate’ application is developed in a way to facilitate the agriculture industry. ‘Agro-Mate’ consists of four components such as soil quality determination and fertilizer recommendation, best crop selection, rice disease prediction and recommendation, and natural disaster prediction and providing the recommendation. Also, the application suggests fertilizer when soil is lacking quality and provides recommendations whenever rice diseases or natural disasters are identified. The usage of android mobile devices in agriculture is one of the key components of the sector's growth, which facilitates the farmer's inaccurate decision-making to gain more quality and quantity of crops. Agro-mate’ is more likely to increase the productivity of crops and indirectly increase the GDP of Sri Lanka.
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An Approach of Enhancing the Quality of Public Transportation Service in Sri Lanka using IoT
(Institute of Electrical and Electronics Engineers, 2022-10-15) Weligamage, H. D; Wijesekara, S. M; Chathwara, M.D.S.; Isuru Kavinda, H.G.; Amarasena, N; Gamage, N
Traveling is one of the necessary and common behavior of any society. Thus, there are many ways of human travel. Due to the fact that Sri Lanka is still a developing country, the vast majority of the population rely on public transit as opposed to private transportation options. In this situation, public and private bus services are the most common means of transportation for people. People who use bus service for daily traveling face lot of issues due to the delays in bus arrivals, missing the bus or excessive crowd in the bus. This proposed system is intended to make bus travel more efficient and convenient for those who rely on buses as their primary means of public transit. This system provides a mobile application for passengers to utilize in order to observe the real-time position of the buses, as well as their anticipated arrival time, current passenger count, and a visualization of the available seat locations within the vehicle prior to the arrival of the bus. Besides, traditional manual ticketing procedure also cause many difficulties for the passengers like need of carrying changed money each time they travel. To avoid this serious problem, this system introduces a non-interactive automated ticketing system which has a smart card that can be tracked in a RFID zone and an automated fee calculating system using a logical conceptual algorithm considering environmental factors. Along with this a digital ticket is issued including all the required details of a journey. In addition to that, this system has a two-factor authentication process that makes use of face recognition to validate the user's identity before granting access to their smart card. The goal of this application is to provide a systematic solution for the typical challenges that public transportation users face in order to improve the service quality by using IoT-based technologies and image processing.
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AwareME: public awareness through game-based learning
(IEEE, 2020-11-16) Dassanayake, D. K. M. P. M. M; Wijesinghe, S. N; Jayasiri, T. L. C; Keenawinna, K. A. R. T; Rankothge, W. H; Gamage, N
It is widely recognized that a nation with minimum problems relating to areas such as health, environment, infrastructure, and technology is a developed country [1]. However, the developing/ lower-middle income countries need many improvements in the above-mentioned areas, as they are still lacking in those areas [1]. Apart from the risk associated with these problems, the main challenge faced by developing countries is, making the public aware of these problems. In this paper, we are proposing a mobile game-based learning platform: "AwareME" which focuses on following problems: (1) health awareness (dengue fever), (2) environmental awareness (garbage disposal), (3) cyber security awareness (social media) and (4) safety awareness (road safety). The "AwareME" platform includes quizzes, 2D/3D puzzle games, and 3D action games with activities to improve the cognitive skills and awareness of the public. We have provided the results of an initial performance evaluation of "AwareME" platform.
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"Cropmaster" - Real-Time Coordination of Multirobot Systems for Autonomous Crop Harvesting: Design and Implementation
(Institute of Electrical and Electronics Engineers Inc., 2025) Pramod, I; Arachchi, A.M; Rashen, C; Chinthaka, G; Pandithage, D; Gamage, N
The CropMaster is an autonomous rover system designed to enhance Scotch Bonnet production by improving disease management, crop sorting, autonomous navigation, and real-time environmental monitoring. Equipped with sensors to measure sunlight, humidity, pH, NPK content, and soil moisture, the rover securely transmits analyzed data to a web-based dashboard. LIDAR technology enables efficient autonomous navigation, allowing the rover to move around fields and avoid obstacles. The MQTT protocol facilitates communication between multiple rovers, preventing duplicate measurements and ensuring data is sent to the dashboard for comprehensive data collection across large areas. TensorFlow's machine learning models allow the rover to accurately assess crop health and detect early-stage diseases, followed by automated pesticide and fertilizer application through a spraying system. To maintain reliability, the rover's operations, including data transfer and task execution, are continuously monitored for Quality of Service (QoS). All collected data is stored in the cloud for long-term access. Built with a lightweight aluminum and plastic chassis and robotic arms, the rover is designed for adaptability and operational efficiency, aiming to improve crop management and increase yields across extensive agricultural fields.
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Data-driven Online Decision Support for Hotel Site Selection
(IEEE, 2021-12-22) Kumarage, I; Weerakeshara, N; Chamika, T; Rathnapala, H; Nawinna, D. P; Gamage, N
Location is one of the fundamental factors that determine hotel success. The location, once selected, cannot be changed without a significant investment. This research aims to identify the location-specific factors that affect Sri Lankan coastal hotels. The factors that affect the location rating have been assessed under location attraction, accessibility, and popularity. An ensemble learning model has been trained to predict the location score of a hypothetical location, assess the manner accessibility affects hotel performance, and predict location popularity based on the surrounding competition. The results show that this method can assess hotel location and performance, with significant accuracy and the identified location-related factors that contribute to a hotel's success can be used by hoteliers and investors to improve decision making.
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Dynamic Bandwidth Allocation in Enterprise Network Architecture: A Real-Time Optimization Approach
(Institute of Electrical and Electronics Engineers Inc., 2025) Wickramasinghe T.M.L.D; Costa M.M.R.S; Dissanayake S.C.W.; Abayakoon A.M.W.Y.; Lokuliyana, S; Gamage, N
Enterprise networks increasingly rely on cloud platforms, remote collaboration tools, and real-time communication, placing high demands on bandwidth availability and responsiveness. Static bandwidth allocation approaches often fail to adapt to dynamic traffic conditions, leading to congestion, inefficiency, and degraded Quality of Service (QoS) for critical services such as VoIP and video conferencing. This research introduces a novel real-time bandwidth allocation system that integrates Deep Packet Inspection (DPI), supervised machine learning, and Linux traffic control (tc). Unlike prior solutions that focus only on classification or simulation, our system actively enforces bandwidth policies based on live predictions. Traffic is captured and analyzed in the WAN, while adaptive policies are deployed in the LAN. A web dashboard offers real-time traffic and bandwidth visibility. The proposed system addresses realworld enterprise challenges by enabling intelligent, responsive bandwidth management without requiring costly infrastructure changes, achieving measurable improvements in latency, throughput, and application-level prioritization.
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E-Medic – Autonomous Drone for Healthcare System
(IEEE, 2021-04-12) Abeygunawaradana, p.; Gamage, N; De Alwis, L; Ashan, S; Nilanka, C; Godamune, P
This paper presents the implementation of a platform for the delivery of medicine using an autonomous drone. The platform consists of a Healthcare platform that connects doctors and patients and an autonomous drone that handles the delivery of medicine to the patients. This platform mainly contains several functionalities for the E-prescribing and delivery management of a drone. For patient management, the E-medic system uses a mobile application with facial recognition-based Authentication. Also, this platform is developed with separate web-applications to handle prescriptions, orders, and delivery management. Since this system uses an autonomous drone for deliveries, this platform also has functionalities to operate the delivery drone using a web application. the system uses REST APIs to operate the drone regardless of the distance to the ground station. The delivery drone can discover the shortest path to the destination and fly to that destination autonomously with the help of the computer vision-based obstacle avoidance system.
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E-Medic–Autonomous Drone for Healthcare System
(IEEE, 2021-02-19) Abeygunawaradana, P; Gamage, N; De Alwis, L; Ashan, S; Nilanka, S; Godamune, P
This paper presents the implementation of a platform for the delivery of medicine using an autonomous drone. The platform consists of a Healthcare platform that connects doctors and patients and an autonomous drone that handles the delivery of medicine to the patients. This platform mainly contains several functionalities for the E-prescribing and delivery management of a drone. For patient management, the E-medic system uses a mobile application with facial recognition-based Authentication. Also, this platform is developed with separate web-applications to handle prescriptions, orders, and delivery management. Since this system uses an autonomous drone for deliveries, this platform also has functionalities to operate the delivery drone using a web application. the system uses REST APIs to operate the drone regardless of the distance to the ground station. The delivery drone can discover the shortest path to the destination and fly to that destination autonomously with the help of the computer vision-based obstacle avoidance system.
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Easy Learning: Augmented Reality Based Environmental Studies for Primary Students
(IEEE, 2019-12-05) Wickramapala, T; Jayawardhana, L; Tharaki, S; Senevirathna, S; Gamage, N; Wickramarathna, J
Primary education is every child's fundamental right. According to the United Nations Educational, Scientific and Cultural Organization (UNESCO), primary schooling offers learning and educational activities typically intended to provide learners with basic abilities in reading and writing. The students find it difficult to identify trees and animals around them due to the lack of exposure to the natural environment. This research study introduces mobile based application (Easy Learning) which embraced augmented reality technology (AR) to motivate and aid learners in studying Environmental Studies in terms of identification of animals and trees. In order to provide sufficient knowledge about trees and animals, this research focuses on safe internet browsing and summarization for trees and animals. Easy learning suggest safe videos for kids and generates knowledge based questions to evaluate themselves as well. The study also evaluates whether the students like the features of the Easy Learning and the rate of knowledge change, through pre and post questionnaires given at the beginning and at the end of the implementation of Easy Learning. The findings proves that the Easy Learning as an interactive AR based learning instrument, for Environmental Studies which improves the learning curve.
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Evaluating the Success of Digital Learning in Sri Lankan Tertiary Education
(IEEE, 2022-12-09) Weerapperuma, J; Nawinna, D; Gamage, N
This paper takes a social capital perspective to explain the underlying mechanisms that drive the success of digital learning in tertiary education in an emerging economy. It is crucial to explore ways in which the success of tertiary education can be maximized since these students will immediately contribute to the economy. Although digital-learning initiatives have advanced in developed countries, it is still in its early phases in many developing countries, including Sri Lanka. This study focuses on structural, relational, and cognitive dimensions of social capital and provides a new theoretical framework to examine its relationship to digital educational success. The study uses a quantitative approach where the data is collected from University students in Sri Lanka using a survey deployed online. The model is validated using the structural equation modeling technique. The findings of this study indicated that the three dimensions of social capital positively influence the success of digital education at the tertiary level. Further, this paper contributes to the existing literature on Social Capital Theory and provides valuable insights and recommendations for policymakers in the educational sector on improving digital learning achievements.
Open Access
Forecasting accuracy of Holt-Winters Exponential Smoothing: evidence from New Zealand.
(New Zealand Journal of Applied Business Research, 2020) Dassanayake, W; Ardekani, I; Jayawardena, C; Sharifzadeh, H; Gamage, N
Financial time series is volatile, dynamic, nonlinear, nonparametric, and chaotic. Accurate forecasting of stock market prices and indices is always challenging and complex endeavour in time series analysis. Accurate predictions of stock market price movements could bring benefits to different types of investors and other stakeholders to make the right trading strategies. Adopting a technical analysis perspective, this study examines the predictive power of Holt-Winters Exponential Smoothing (HWES) methodology by testing the models on the New Zealand stock market (S&P/NZX50) Index. Daily time-series data ranging from January 2009 to December 2017 are used in this study. The forecasting performance of the investigated models is evaluated using the root mean square error (RMSE], mean absolute error (MAE) and mean absolute percentage error (MAPE). Employing HWES on the undifferenced S&P/NZX50 Index (model 1) and HWES on the differenced S&P/NZX50 Index (model 2) we find that model 1 is the superior predictive algorithm for the experimental dataset. When the tested models are evaluated overtime of the sample period we find the supportive evidence to our original findings. The evaluated HWES models could be employed effectively to predict the time series of other stock markets or the same index for diverse periods (windows) if substantiate algorithm training is carried out.
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Interactive Solution to Improve Flood Awareness Among Public–Flood Run
(IEEE, 2019-12-10) Karunanayake, T; Dayarathne, P; Doratiyawa, C; Wickramanayake, A; Rankothge, W. H; Gamage, N
Flood is the most common natural disaster in Sri Lanka that causes a huge destruction annually [1]. Lack of awareness on flood among the public is one of the main reasons behind the huge destruction of lives and property.We have proposed a platform for flood awareness, named "Flood Run" which is an interactive 3D mobile game, with following modules: puzzle games, action games and memorizing games, adventure games and quizzes. These four modules consist of activities to develop the essential skills to improve flood awareness. This research used game-based learning and interactive game designing techniques. This research paper presents the performance evaluation of the four types of modules. The results show that, with the help of the provided solution, the expected skills of the people are improved, and through that flood awareness among public is improved.
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IoT-based Monitoring System for Oyster Mushroom Farming
(IEEE, 2021-12-09) Surige, Y. D; Perera, W. S; Gunarathna, P. K; Ariyarathna, K. P; Gamage, N; Nawinna, D. P
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.
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IoT-Based Smart Hydroponics for Automated Nutrient, Climate, Irrigation, and Health Monitoring
(Institute of Electrical and Electronics Engineers Inc., 2025) Ashik M.A.M; Bogahawatta C.A; Perera M.R.D; Dassanayake D.R.I.P; Jayakody, A; Gamage, N
This study presents HydroNutraLeaf as a selfgoverning hydroponic tower system built with Internet of Things technology to automate the critical aspects of hydroculture farming by uniting water supply management with environmental control and watering systems and plant health monitoring capabilities. The system unites multiple essential components to operate as one unit. The system incorporates an automatic plant disease detection system through real-time image acquisition which uses Convolutional Neural Network (CNN) algorithms and cloud-based warning protocols for classification purposes. An automated system comprising Raspberry Pi actuators, NPK sensors, and machine learning functions delivers nutrients at proper stages during plant growth. A reinforcement learning system directs the management of climate factors including temperature and humidity together with Light Emitting Diode (LED) spectrums to achieve superior yield production and product quality. The system includes a self-operated irrigation system with Electrical Conductivity (EC), potential of hydrogen (pH) regulation features which utilizes SVM-based prediction methods in combination with real-time monitoring to achieve optimum root environment conditions. Users can access a dashboard in Grafana to monitor and control the system by using cloud platforms which include Firebase and AWS. The experimental findings reveal water consumption decreased by 30% along with improved nutritional efficiency reaching 25% and enhanced crop yield reaching 15% with better health performance. The sustainable farming operations and commercial greenhouse implementation benefit from HydroNutraLeaf solution which operates through a scalable model based on data analysis and requires minimal human intervention.
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LAWSUP - A Smart Platform to Assist Stakeholders of Business Law
(IEEE, 2022-12-09) Sulakshi, U L H; Opatha, S D; De Silva, K S D; Sandeepa, M M A D N; Nawinna, D; Harasgama, H; Gamage, N
Corporate law, sometimes known as business law, is the body of law that governs the rights, relationships, and behavior of persons, corporations, organizations, and businesses. Business Organizations, employees/laborers, and the public are involved in this area of the law accompanying lawyers, and legal advisors. Business organizations need legal advice. Employees face many difficulties and injustices at their workplaces. People who wish to start a new business, search for legal guidance. When one of these parties needs support, they must seek a lawyer, go to the lawyer, and get legal support. When delivering legal support to clients, lawyers are still going through a manual process. There are very few systems that have been implemented for the law domain so far, and those only search engine types of systems that are unable to support every stakeholder of this domain. There is no common platform for all these stakeholders to find solutions, connect with a good lawyer and get support. We have identified the main issues faced by business organizations, employees that need legal support, the general public, and lawyers, and developed a web solution by implementing Machine Learning, Classification Algorithms, Text mining, Natural Language Processing, and Web Crawlers.
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Mobile Base Solution for Individuals with Limited Knowledge About Cars
(IEEE, 2022-12-09) Nammunige, H; Chamuditha, T; Udara, S; Athapaththu, D; Gamage, A; Gamage, N
Different modes of transportation were discovered by our ancestors from ancient times. Currently, the majority of people choose to purchase a personal automobile for transport needs. However, the vast majority of people are not automobile industry experts. As a result, the majority of people have trouble when recognizing cars. Due to numerous variations of a single vehicle model, even an expert has trouble correctly identifying a certain car model. People must take into account a number of factors before purchasing a specific automobile. Some of crucial factors are service costs and future market prices. Ordinary people require the assistance of a professional when estimating the market price of a car and calculating the cost of servicing a car. Accidents can also occur at any time when driving a car often. In similar circumstances, consumers require the assistance of an insurance agent or a technician to estimate the cost of damage repair. In this study, we provide a way for non-automotive experts to use their smartphones to identify car models, forecast future market prices, determine and forecast servicing costs, and estimate minor damage repair costs. This paper demonstrates how we accomplished aforementioned tasks using YOLO V4, Multiple Linear Regression, Random Forest Classifier and Faster RCNN.
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A Smart Aquaponic System for Enhancing The Revenue of Farmers in Sri Lanka
(IEEE, 2022-10-19) Ekanayake, D; de Alwis, P; Harshana, P; Munasinghe, D; Jayakody, A; Gamage, N
Sri Lanka's agricultural sector confronts serious challenges from fertilizer shortages and agriculture-related chemical scarcity. Innovations comparable to aquaponic systems may be offered to Sri Lankan farmers to overcome these difficulties using IoT and ML technology. This research scope is to implement a smart and secure aquaponic environment monitoring system to forecast plant and fish growth factors, provide Sri Lankan farmers with insights into the environment's behaviors, and take measures according to the predictions utilizing control mechanisms. In this research, more exact predictions have been generated by the Random Forest algorithm model rather than the LSTM model, and most of the investigated parameters given good accuracy according to the absolute mean error (Media TDS-1.95, Media pH-0.06, Media Temperature-0.49, Env. Temperature- 0.94, Env. Humidity-2.70) except the environment light intensity (64.11). The ML solution studied in this research paper would increase the quality of traditional agriculture in Sri Lanka for greater productivity and economic benefit.
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Smart Platform for Cloud Service Providers
(IEEE, 2019-12-05) Dharmapriya, W. A. S. P; Supipi, K. G; Ravindu Nimesh, G. G; Muhandiram, M. A. B. K; Rankothge, W. H; Gamage, N
Cloud computing offers many types of computer related services without the direct active management of their users. Cloud Service Providers (CSPs) are responsible to manage these services such as placement of services in the cloud, resource allocation, network monitoring etc. The cloud service provider is required to monitor the network traffic, predict the dynamic traffic changes, and scale out the resources accordingly. We have proposed a platform for cloud service providers that automates the cloud management related services with following modules: (1) traffic monitoring, (2) traffic prediction, (3) virtual service instances placement and (4) traffic load balancing. We have used continuous and periodic approaches for traffic monitoring, Auto-Regressive Integrated Moving Average (ARIMA) model for traffic prediction, Randomized Weighted Majority Algorithm (RWMA) for virtual service instances placement and a threshold-based approach for load balancing. In this paper, we are presenting the performances of our cloud management platform, specially an evaluation of the algorithms used in above mentioned modules. Our results show that, using our proposed modules, the cloud management related services can be automated efficiently and reliably.
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SmartCop: an automated platform to mitigate the impact of road accidents
(IEEE, 2020-12-01) Sewwandi, A. K. T; Dissanayake, D. M. K. P; Navanjani, D. H. K. H; Shangavie, R; Rankothge, W. H; Gamage, N
Road accidents have become one of the major issues both locally and globally as they cause many deaths, injuries, fatalities, and economic deprivation. Major reasons for the rapid increase in road accidents are not only the negligence of public unawareness, but also the improper scheduling and enforcement of traffic police officers to control the traffic. In this paper, we have proposed the SmartCop platform to mitigate the impact of road accidents with four modules: (1) predict road accidents, (2) recommend and schedule police officers, (3) enhance road accidents prevention awareness using a game-based learning approach, and (4) enhance road accidents response awareness using a game-based learning approach. We have used supervised/unsupervised learning, optimization techniques, and game-based learning approaches to implement the above-mentioned modules. Our results show that, using our proposed modules, the road safety related services can be automated efficiently and effectively.