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 Mobile solution for color blindness - An application of image processing(Research Symposium on Pure and Applied Sciences, 2018 Faculty of Science, University of Kelaniya, Sri Lanka, 2018) Thanusanth, B.; Jeewanie, J. A.Color blindness is a deficiency of color vision which mostly appear as a genetic problem. Due to presence of color blindness, human eye becomes unable to differentiate colors from other colors. People who suffer from the color blindness fully or partially have trouble in differentiating certain colors, but the severity of the color deficiency is varying. Sometimes damage of an eye or disorder of eye and brain are also cause to color blindness. The people suffers from color blindness wear suitable spectacles to overcome the deficiency. There are some scientific studies currently going on to address this problem. They can be classified as computer aided solutions and non-computer aided solutions. On the non-computer aided side, there is just one technique used: colored filters. These filters come in different forms such as Lenses, Glasses, etc. In the Computer aided side, there are different tools available such as Ishihara Test, Farnsworth Lantern Test. In the modern world, people always carry many smart solutions with their mobile phones in their hands and many services available to them with a single touch. For example, weather information, train time table, alerts about important meetings etc. By following a design science research methodology, this research is to study the techniques for color blindness and, implement an algorithm to detect the color ranges using Convolution matrix. The main artifacts are algorithm and mobile application. The results are twofold. On one hand, the proposed solution is very useful for those who don’t like to wear spectacles or if they forget to bring the spectacles every time. On the other hand, there are some people who are still not aware about their color blindness. For them, the mobile application can be used to identify their color blindness. Images are captured using the camera of the mobile phone and they are matched with the RGB range for colorblindness. Basically image processing techniques are used to implement the solution. The Convolution Matrix class is used to sharpening the image when mapping. This mobile application has facilities to check the different colorblindness and also a test for colorblindness. The proposed solution is validated with a sample of 25 users.