Research Papers - Dept of Information Technology
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Publication Embargo Analyzing Fisheries Market, Shrimp Farming & Identifying Fish Species using Image Processing(IEEE, 2022-12-09) Sumeera, S; Pesala, N; Thilani, M; Gamage, A; Bandara, PThe fisheries industry is vital to the Sri Lankan economy because it provides a living for more than 2.5 million coastal communities and meets more than half of the country’s animal protein needs. Today, the fishery community in Sri Lanka is facing several grant problems. Among them, not getting a decent fish price for their harvesting, the inability to identify diseases in shrimp cages in the early stages, and the inability to identify fish species by observing their external appearance. This research developed a prototype mobile application “Malu Malu” to avoid the above-mentioned problems. It facilitates to the prediction of market fish prices, identifying shrimp diseases in their early stages, and identifying fish species by observing their external appearance. The proposed predictive models of the “Malu Malu” contains three main models developed using inseption V3 Convolutional Neural Network (CNN) model for image classification and Linear Regression is used for creating a model for predictions. The experimental results of these models showed above 85% of accuracy.Publication Embargo Image Processing and IoT-based Fish Diseases Identification and Fish Tank Monitoring System(IEEE, 2022-12-09) Ranaweera, I.U.; Weerakkody, G.K; Balasooriya, B.M.Eranda Kasun; Swarnakantha, N.H.P.Ravi SupunyaEvery person has their way of relaxing and having fun. The most well-liked approach to do it is to own a pet. When most individuals work from home and anxiety levels are high, people have certain restrictions on going outdoors and engaging in activities due to the existing COVID scenario. Consequently, we developed a product called AquaScanner. The problems that come with the aquarium environment can all be handled by our product. Our product primarily consists of an application that can regulate and monitor aquarium tanks by regulating feeding routines, fish disease detection, and water quality monitoring. The AquaScanner focuses on recognizing two significant illnesses, Fin Rot and Fungi bacteria, under the heading of disease identification. Additionally, the product will recommend treatments for the illness and provide two distinct methods for feeding the fish manually and automatically through the application. The AquaScanner can regulate feeding operations. Also, AquaScanner can independently monitor all key water parameters as part of the water quality measurement system. A user-friendly interface connects these three key elements. Owners of aquariums may manage and keep an eye on their beloved aquariums from anywhere in the world.Publication Embargo System to Improve the Quality of Water Resources in Sri Lanka Using Machine Learning and Image Processing(IEEE, 2022-12-09) Liyanage, M. H. S; Gajanayake, G.M.B. S; Wijewickrama, O; Fernando A, S.D.S. A; Wijendra, D; Gamage, A. IWater covers approximately 71% of the earth’s surface, but only 1.2% of it can be used for drinking. However, due to the amount of waste water released into water resources, the presence of harmful microorganisms, and natural occurrences such as eutrophication, even that water cannot be used directly for drinking purposes without purification. One method of purifying water is chlorination. However, if the chlorine level exceeds the standard, it can cause both long-term and short-term illnesses. As a result, a system is imposed to solve four problems: predicting the pH value of chlorinated drinking water, determining the quantification value of active sludge in a wastewater plant, detecting microorganisms in drinking water, and predicting the percentage of eutrophication in a water resource.Publication Embargo Plant Diseases Detection Using Image Processing and Suggest Pesticides and Managements(IEEE, 2022-07-18) Gamage, A; Sritharan, L; Anjanan, MVarious plant diseases affect farmers all over the world and there is a very small amount of solutions available online for free in order to assist. In Sri Lanka, in order to address this issue, we have done a study which outputs a mobile application which utilizes image processing and recommend pesticides according to corresponding disease. The disease detection method includes image acquisition, image pre-processing, image segmentation, feature extraction, and classification. This study looked at methods for identifying plant ailments using photos of their leaves. This work also presented unique segmentation and feature extraction techniques for plant disease identification. For feature extraction, the CNN algorithm is utilized. This research paper may be a revolutionary approach to diagnosing plant illnesses by employing a deep convolutional neural network that has been trained and fine-tuned to suit a database of a plant's leaves gathered independently for distinct plant diseases. At the end of the study we achieved an accuracy of 98 percent in detecting the plant diseases and further on implemented mobile system which can suggest pesticide accordingly.Publication Embargo Secure Smart Parking Solution Using Image Processing and Machine Learning(IEEE, 2022-07-18) Balasuriya, A. I. P; Dilitha, A.G. A. D; Perera, P. A. M. M; Jayaweera, D.K. S; Swarnakantha, N. H. P. R. S; Rajapaksha, U. U. SWith the IoT connecting the world, finding parking lots is much easier with smart parking solutions. Some of the parking areas in the world use at least some kind of smart parking solution these days. There are existing and broader solutions when it comes to parking a vehicle. So, in this project, we are mainly focusing on developing these existing solutions a step further. I hear we are mainly focusing on a solution that increases the effectiveness of the management processes in the currently existing solutions and developing a relevant and integrated security solution. The project will include a refined prediction system that can predict the outcomes of some of the main processes that are done inside a parking area so that smart parking management and be done much more smoothly and effectively. Prediction systems are designed on the idea of getting possible security issues, vehicle types on peak hours as an outcome. And even in the aspect of security rather than a separate system, there is an integrated one coming with this project that has improved functionality with regards to a parking area. The main possible security threats that happen in a parking area are being identified by us and implemented separate functionalities on detecting them. Furthermore, the project includes a decision-making system that can deduce and take the decision on slot management in a parking area so it can work effectively and with minimum human interaction.Publication Embargo Coconut Disease Prediction System Using Image Processing and Deep Learning Techniques(Institute of Electrical and Electronics Engineers Inc., 2020-12-09) Nesarajan, D; Kunalan, L; Logeswaran, M; Kasthuriarachchi, S; Lungalage, DCoconut production is the most important and one of the main sources of income in the Sri Lankan economy. The recent time it has been observed that most of the coconut trees are affected by the diseases which gradually reduces the strength and production of coconut. Most of the tree leaves are affected by pest diseases and nutrient deficiency. Our main intensive is to enhance the livelihood of coconut leaves and identify the diseases at the early stage so that farmers get more benefits from coconut production. This paper proposes the detection of pest attack and nutrient deficiency in the coconut leaves and analysis of the diseases. Coconut leaves monitorization has been taken place after the use of pesticides and fertilizer with the help of the finest machine learning and image processing techniques. Rather than human experts, automatic recognition will be beneficial and the fastest approach to identify the diseases in the coconut leaves very efficiently. Thus, in this project, we developed an android mobile application to identify the pests by their food behaviors, pest diseases and the nutrition deficiencies in the coconut trees. As an initial step, all datasets for image processing technology met pre-processing steps such as converting RGB to greyscale, filtering, resizing, horizontal flip and vertical flip. After completing the above steps, the classification was performed by analyzing several algorithms in the literature review. SVM and CNN were chosen as the best and appropriate classifier with 93.54% and 93.72% of accuracy respectively. The outcome of this project will help the farmers to increase the coconut production and undoubtedly will make a revolution in the agriculture sector.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 Mobile Medical Assistant and Analytical System for Dengue Patients(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Jayampathi, K.T.K.; Jananjaya, M.A.C.; Fernando, E.P.C.; Liyanage, Y.A.; Pemadasa, M.G.N.M.; Gunarathne, G.W.D.A.Dengue fever is a vector-borne viral disease spread by the mosquito Aedes Aegypti. It is a public health problem, with an estimated 50-500 million infections each year and no effective vaccination. People's hectic schedules may not have enough time to see a doctor every time they have a fever. They may overlook their disease, believing it to be a common ailment. Prior medical assistance for dengue patients with fever to check their conditions reliably is a major problem. There is no easily accessible proper system to identify dengue patients at an early stage. This paper presents a mobile medical assistant and analytical system for dengue patients. With a novel approach, using the most appropriate technologies, the mobile application supports identifying dengue patients using the chatbot, analyzing skin conditions, analyzing blood reports, and analyzing dengue-infected areas' functionalities. The registered users can log in to the system and check their dengue condition. The development is carried out with Natural Language Processing, Artificial Neural Network (ANN), Machine Learning, Image Processing, Convolutional Neural Network (CNN), and Android technologies. A mobile application prototype is created and tested, with the possibility of future testing and implementation. The results show effective performances in analyzing dengue conditions.