Science

Now showing 1 - 3 of 3

CNN based deep learning model for tomato crop disease detection
(Faculty of Science, University of Kelaniya, Sri Lanka, 2020) Gunarathna, M.M.; Rathnayaka, R.M.K.T.
Tomato is one of the most commonly cultivated solanaceous short duration vegetable crops, grown in outdoor and indoor conditions, worldwide. However, unfortunately, many diseases affect these crops which have an impact on quality and the quantity of the produce, agricultural productivity, and causes considerable economic losses to the producers and to the contribution to the growth of the agricultural sector. Therefore, continuous monitoring of the crop is required throughout the growing stage to identify the diseases. The most traditional way of identifying diseases is naked eye observation, which is tedious and time-consuming. Today, advances in computer vision paved by deep learning have led to a situation where disease diagnosis is based on automated recognition. The main objective of this study is to develop an accurate tomato disease classification model which eliminates human error when identifying diseases. Due to a variety of similar disease and pathological problems, even experienced agronomists and plant pathologists often fail to recognize the correct disease. This computer vision system will assist agronomists in detecting a variety of tomato crop diseases. The proposed algorithm consists of four main steps; data collection, data pre-processing, CNN model creation, and evaluation of performance metrics. A leaf is a good indicator of the tomato’s health. Therefore, tomato leaf images belonging to 10 different classes with a resolution of 256x256 were collected from the Internet to build, validate, and test the model. Collected images were normalized and image augmentation techniques were applied to increase the size of the training data set in the preprocessing phase. The CNN model of the study was built from scratch using the Keras library, which runs top of the Tensorflow backend. The model comprises four convolutional blocks followed by batch normalization, max pooling, and dropout layers. Two dense and flatten layers were also included at the end. A time-based learning rate scheduler was used with an initial learning rate of 0.001, momentum of 0.5, an epoch of 15 and a batch size of 27. The final model was able to achieve a training accuracy of 94% and a testing accuracy of 92%. This proposed system would encourage tomato cultivators to detect diseases at an early stage and start treatments without relying on experts. In the future, we hope to build an ensemble approach to classify plant diseases with real-time images towards the development of a decision support system.
Computer aided segmentation approach for Melanoma skin cancer detection
(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Sivathmeega, S.; Kariapper, R.K.A.R.; Rathnayaka, R.M.K.T.
Skin cancer is the most common type of cancer in the world and nowadays, this incidence is increasing rapidly. In recent years, there has been a fairly rapid increment in melanoma skin cancer patients. Melanoma, this the deadliest form of skin cancer, must be diagnosed earlier as soon as possible for effective treatment. To diagnose melanoma earlier, skin lesion should be segmented accurately. However, the segmentation of the melanoma skin cancer lesion in traditional approach is a challenging task due to the number of false positives is large and time consuming in prediction. Hence, the development of automated computer vision system becoming as an essential tool to segment the skin lesion from given photograph of patient’s cancer affected area and to overcome those difficulties, which were found in the earlier methods. This work was done through image processing techniques. Some of these techniques are widely used in similar applications, as is the case of the canny edge detection for finding the lesion boundary. Other techniques are watershed segmentation for segmenting the lesion from skin, multilevel thresholding for merging the lesion, and active contour for increasing the accuracy. Though the personnel in the medical field had introduced new methodologies to improve the accuracy by addressing the challenges and mainly focusing on the accuracy, the approach in this study achieved 97.54% sensitivity, 97.69% specificity, and 97.56% accuracy.
A New Financial Time Series Approach for Volatility Forecasting
(Department of Statistics & Computer Science, University of Kelaniya, Sri Lanka, 2016) Rathnayaka, R.M.K.T.; Seneiratna, D.M.K.N.; Arumawadu, H.I.
The investment in capital market is easiest, fastest and securable way for building healthy financial foundation today. Because of the economic outlooks causing directly on these market fluctuations, the making decisions in the equity market has been regarding as one of the biggest challenges in the modern economy. The main purpose of this study is to take an attempt to understand the behavioral patterns and seek to develop a new hybrid forecasting approach under the volatility. The results are successfully implemented on Colombo stock exchange (CSE), Sri Lanka over the three year period from January 2013 to December 2015. The empirical results indicated that the new proposed hybrid approach is more suitable for forecasting price indices than traditional time series forecasting methodologies under the high volatility.

Science

Browse

Filters

Settings

Sort By

Results per page

Search Results