Browsing by Author "Udayakumara, EPN"

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    Artificial neural network to estimate the paddy yield prediction using remote sensing, weather and non weather variable in Ampara district, Sri Lanka
    (Faculty of Information Technology, University of Moratuwa., 2020-12) Wanninayaka, WMRK; Rathnayaka, RMKT; Udayakumara, EPN; Karunananda, AS; Talagala, PD
    In Sri Lanka, seasonal paddy area mapping and rice prediction is based on the traditional methods with poor technologies. Ampara district has been chosen as the study area because its contribution is considered as the second highest paddy yield to the Sri Lankan rice harvest. This study focuses on developing models for precise mapping paddy and predicting the harvest of rice in the Ampara district. It helps the government and persons of authority to take decisions about how to manage the economy based on the rice quantity. Research includes the imageries of satellites sentinel-1 and sentinel-2 the period from April to September 2019. The two classification methods, Divisional Secretory Division (DSD) and maximum likelihood classification were used to identify the real paddy area. The accuracy rates of these classifications were 0.92 and 0.86 respectively. Artificial Neural Network (ANN) model was used to predict paddy rice harvest using sentinel 2 features extracts and round truth data. Mean square error of the model is 0.106 and mean absolute error is 0.245. Increasing the remote sensing imagery directly affects to enhance accuracy. Increasing the number of sample classes and number of classes in various types will raise-up higher accuracy than in here.
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    Explainable ai techniques for deep convolutional neural network based plant disease identification
    (Information Technology Research Unit, Faculty of Information Technology, University of Moratuwa., 2023-12-07) Kiriella, S; Fernando, S; Sumathipala, S; Udayakumara, EPN; Piyatilake, ITS; Thalagala, PD; Ganegoda, GU; Thanuja, ALARR; Dharmarathna, P
    Deep learning-based computer vision has shown improved performance in image classification tasks. Due to the complexities of these models, they have been referred as opaque models. As a result, users need justifications for predictions to enhance trust. Thus, Explainable Artificial Intelligence (XAI) provides various techniques to explain predictions. Explanations play a vital role in practical application, to apply the exact treatment for a plant disease. However, application of XAI techniques in plant disease identification is not popular. This paper discusses the key concerns and taxonomies available in XAI and summarizes the recent developments. Also, it develops a tomato disease classification model and uses different XAI techniques to validate model predictions. It includes a comparative analysis of XAI techniques and discusses the limitations and usefulness of the techniques in plant disease symptom localization.