IRSPAS 2018
Permanent URI for this collectionhttp://repository.kln.ac.lk/handle/123456789/19084
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Item Identifying paddy diseases with image processing techniques in Sri Lankan context(Research Symposium on Pure and Applied Sciences, 2018 Faculty of Science, University of Kelaniya, Sri Lanka, 2018) Ahamed, M. I. S.; Dimithrie, P. S.; Rajapakse, R. A. C. P.Agriculture is one of the main sectors in Sri Lanka for ages and rice cultivation plays a major role in Sri Lankans' economy. Currently, farmers use traditional methods and they seek the advice of regional agricultural officers to recognize any unknown paddy disease. As a result, the efforts to increase the quality and quantity of rice production are obstructed by paddy diseases especially due to the lack of resources to identify them immediately. Thus, this study attempts to identify paddy diseases using machine learning techniques in relate with Image processing. Among many rice diseases, Rice Blast, Rice Sheath Blight and Bacterial Leaf Blight are focused to analyze further in detail as they are the leading diseases for major destructions in paddy cultivation. Several existing algorithms will be analyzed to select the suitable algorithms for accurate identification of the above three diseases and to suggest better solutions to overcome them as per the recommendations of the Department of Agriculture. Thus, the main object of the study is to analyze different machine learning techniques for the classification in image processing and to get the best technique which can be used effectively for the application. Increasing the disease diagnosing rate and to decreasing the crop destruction rate from these diseases are the main objectives of the study. The outcome of this study will be used by farmers in detecting paddy diseases without depending on others. The methodology includes gathering data from Rice Research and the Development Institute in Bathalagoda (RRDI) and some more images from field visits to the farms. Then MATLAB is to use for preprocessing the datasets to get qualitative images as a data preparation step. For this purpose, we have decided to use the hybrid version of a genetic-algorithm-segmentation based selective principal component analysis method for the feature extraction and develop a featured algorithm from the literature. After the feature extraction, classification will be done by analyzing Support Vector Machine (SVM), KNearest Neighbor (KNN) and Probabilistic neural network (PNN) from the literature and the best technique will be selected. The proposed solutions is to provide precise and scalable visual cues to identify diseases. Conclusively, this study will provide valuable information regarding the reduction of crop destruction from paddy diseases for a better future.Item Adaptive green time allocation method for traffic congestion based on cell transmission model and genetic algorithm(Research Symposium on Pure and Applied Sciences, 2018 Faculty of Science, University of Kelaniya, Sri Lanka, 2018) Priyasad, H. A. D.; Kulatunga, D. D. S.Traffic congestion is defined as a physical phenomenon relating to the manner in which vehicles impede each other’s progression as demand for limited road space approaches full capacity. This makes trip time longer and increasing queuing. Also it causes serious problems for the day to day lives of people, massive financial and man-hour loss, environment pollution, some diseases etc. In Sri Lanka, traffic congestion in a given area occurs for many reasons. The main reason is that the demand of road does not match to road capacity. In Sri Lanka, although an increase of 10% per year road demand is expected, it can increase road capacity by around 2% to 3% per year. Other important reasons are the existing traffic control system and traffic intersections. Traffic control systems play a central role of traffic management in Sri Lankan cities. Existing traffic light system mainly controls the traffic light change in constant cycle time. But road conditions in a given area vary day by day. If the traffic control system does not deal with these variations, then traffic control system will create bottlenecks and delays. Therefore, the control of traffic requires adequate adjustments to these variations. This research focused on studying and applying cell transmission model to dynamic traffic signal controlling procedure. Basic cell transmission model is used to model the dynamic changes of vehicular traffic flow and to estimate the total delay of vehicles in a given region within a given time interval under different green time allocations. To find an optimal signal timing plan, the Genetic Algorithm is used. The proposed model is applied with certain assumptions to find an optimal time plan to a signalized intersection in main Kandy - Colombo road which has heavy traffic congestion in the morning hours in weekdays. A section of this region is selected to minimize the total delay and to find an optimal dynamic time plan for the signal lights analyzing the actual data collected in this region using four video cameras. The results are compared with the existing pre-timed signal time plan and the corresponding total delay. It is observed that the proposed dynamic signal timing plan will reduce average delay by 6.2675% and it can be proposed as an alternative for the existing system.