Faculty of Computing

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Analyzing Fisheries Market, Shrimp Farming & Identifying Fish Species using Image Processing
(IEEE, 2022-12-09) Sumeera, S; Pesala, N; Thilani, M; Gamage, A; Bandara, P
The 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.
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tAssessee: Automatically Assessing Quality of Tea Leaves using Image Processing Techniques
(IEEE, 2022-11-30) Sivalingam, J; Sivachandrabose, L.N; Loganathan, M; Sivakumaran, J; Panchendrarajan, R
Sri Lanka is one of the well-known international’s pinnacle tea exporters with a high global demand attracting millions of foreign exchanges, which strengthens the economy of the country. Despite the fact that tea brings a good source of foreign exchange, the tea industry lacks efficiency and effectiveness during the assessment of plucked tea leaves which compromises the significant quality of tea. While studies have revealed various factors affecting the tea quality, key factors are identified as the presence of tea diseases, pest attacks, the mixture of fresh and mature tea leaves, and the mixture of tea grades present in the tea sack. In this paper, we focus on automatically assessing the quality of tea leaves for a single tea leaf and bulk tea leaves before initiating the tea manufacturing process. The proposed tAssessee system allows the user to upload the image of a single tea leaf or bulk tea leaves to automatically assess four different quality factors of tea leaves such as disease, pest attack, freshness, and grade using Convolutional Neural Network based models and using various image processing techniques. This will assist the tea supervisors in the tea factories to automatically assess the quality of tea leaves where the manufacturing process can be segregated according to the quality of tea leaves and determine the pricing accordingly. Extensive experiments performed using the tea leaves images gathered in tea factories reveal, that the proposed tAssessee system can assess the quality of single tea leaf and bulk tea leaves with the accuracy range of 87% - 98% and 91% - 100% respectively.
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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 Supunya
Every 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.
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. I
Water 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.
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Plant Diseases Detection Using Image Processing and Suggest Pesticides and Managements
(IEEE, 2022-07-18) Gamage, A; Sritharan, L; Anjanan, M
Various 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.
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. S
With 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.
Embargo
Interactive Game Application for Garbage Disposal using Image Processing and Location Based Services
(IEEE, 2018-12-21) Nanayakkara, P; Rathnaweera, T; Weerasingha, S; Pieris, Y
Waste disposal is one of the major contemporary topics which is being discussed globally. The main reason for this issue to have such prominence is due to the fact that people are not aware how far-reaching this topic has become. In this paper we propose ScrapWrap, an interactive game for garbage disposal where the main objective is to make people aware how imperative it is to dispose garbage and assist people to have a change of attitude towards this complication. In Scrapwrap, people will be awarded considering several facts such as for notifying littered areas, for cleaning the area, for verifying the cleaned area, and for arranging cleaning campaigns. A chat-based community is created within ScrapWrap so that players could interact with each other, and this strategy is useful when arranging campaigns or scavenger hunt activities to clean the waste. An assistive chat bot is introduced to solve the problems a player might have, and to remind the event details and guide the player throughout the game flawlessly. The long-term goal of introducing Scrapwrap is to let people be aware of the importance of garbage disposal in an interactive manner and to improve the cleanliness of the surroundings.
Embargo
Smart Platform for Film Shooting Management
(IEEE, 2019-12-06) Senarath, S. M. M. M; Perera, M. T. K; Viduranga, D. G. R; Wijayananda, H. M. C. S; Rankothge, W
Producing a movie involves difficult and time-consuming phases, specially, pre-production and production. It's a challenging task to find out suitable locations for each scene and building a schedule without any clashes. We have proposed and implemented a platform for film shooting management with following modules: (1) identify required background for each scene, (2) classify available film shooting locations, (3) compare the required background and available film shooting locations and (4) schedule the shooting of each scene. We have used natural language processing, image processing, string matching algorithms and optimization techniques to implement the above-mentioned modules. Our results show that, using our proposed modules, the film shooting management related services can be automated efficiently and effectively.
Embargo
DFOG-Image Processing Application for Real-Time Defogging
(IEEE, 2020-11-04) Indiketiya, I. H. O. H; Kulasekara, K. M. R. A; Thomas, J. M; Gamage, I; Thilakarathna, T
The enhancement of real-time video taken under bad visibility or bad weather is a vital necessity in consumer transport industry and computer vision applications. During the past decade, many researchers have been devoted to the problem of how to remove fog noise from real-time video. Nowadays vehicle industry uses various computing systems to assist in the transport of travelers from one location to another .now most of the cars have revers camera front cameras and sensors who give the signal when the vehicle is near to another object. These detections and identification are useful for the safe operation of vehicles. When looking through vehicle accident history, many accidents caused bad weather conditions. Fog, haze, rain, and other natural weather conditions cannot remove physically. Fog and haze block vison above 1 kilometer. There is a defogger in the windscreen, but it is only removed Mist on the windscreen. For the driver's vision above the rode, there is no such thing for that. The purpose of this research paper is introducing a new system to remove fog from real-time video and give detailed visual to the driver in foggy or other bad weather condition. This D-Fog system includes functions such as give clear realtime visual in bad weather condition, recognize, and give details about the object above the road, give the distance between objects and vehicle. In this system, main function is producing real-time defogged, clear video. Combination of Ha and Hoon method and Dark channel priority method used to get this real-time defog video. To recognize the object, this system has use thermal sensors and heat maps. To get the distance between object and vehicle this system has use LIDAR sensors. Because of this facility, we can name this system as three in one system.
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, D
Coconut 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.

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