Research Publications Authored by SLIIT Staff

Permanent URI for this communityhttps://rda.sliit.lk/handle/123456789/4195

This collection includes all SLIIT staff publications presented at external conferences and published in external journals. The materials are organized by faculty to facilitate easy retrieval.

Browse

Filters

2022 - 20253

Advanced Search

Filter by

Settings

Author: search.filters.author.Vidhanaarachchi, S

Search Results

Now showing 1 - 3 of 3
  • Thumbnail Image
    PublicationOpen Access
    Early Diagnosis and Severity Assessment of Weligama Coconut Leaf Wilt Disease and Coconut Caterpillar Infestation Using Deep Learning-Based Image Processing Techniques
    (Institute of Electrical and Electronics Engineers Inc., 2025-02-03) Vidhanaarachchi, S; Wijekoon, J. l; Abeysiriwardhana, W. A. S.P; Wijesundara, M
    Global Coconut (Cocos nucifera (L.)) cultivation faces significant challenges, including yield loss, due to pest and disease outbreaks. In particular, Weligama Coconut Leaf Wilt Disease (WCWLD) and Coconut Caterpillar Infestation (CCI) damage coconut trees, causing severe coconut production loss in Sri Lanka and nearby coconut-producing countries. Currently, both WCWLD and CCI are detected through on-field human observations, a process that is not only time-consuming but also limits the early detection of infections. This paper presents a study conducted in Sri Lanka, demonstrating the effectiveness of employing transfer learning-based Convolutional Neural Network (CNN) and Mask Region-based-CNN (Mask R-CNN) to identify WCWLD and CCI at their early stages and to assess disease progression. Further, this paper presents the use of the You Only Look Once (YOLO) object detection model to count the number of caterpillars distributed on leaves with CCI. The introduced methods were tested and validated using datasets collected from Matara, Puttalam, and Makandura, Sri Lanka. The results show that the proposed methods identify WCWLD and CCI with an accuracy of 90% and 95%, respectively. In addition, the proposed WCWLD disease severity identification method classifies the severity with an accuracy of 97%. Furthermore, the accuracies of the object detection models for calculating the number of caterpillars in the leaflets were: YOLOv5-96.87%, YOLOv8-96.1%, and YOLO11-95.9%.
  • Thumbnail Image
    PublicationEmbargo
    IoT-Enabled Smart Solution for Rice Disease Detection, Yield Prediction, and Remediation
    (IEEE, 2023-06-26) Wanninayake, K.M.I.S; Bambaranda, L.G.S. W; Wickramaarachchi, T.I; Pathirana, U.C.S.L; Vidhanaarachchi, S; Nanayakkara, A.A.E.; Gunapala, K.R.D.; Sarathchandra, S.R.; Gamage, A.I; De Silva, D.I
    Sri Lanka's rice cultivation is a vital industry supporting over 1.8 million cultivators and providing staple sustenance for 21.8 million people. According to Sri Lanka's Central Bank, rice cultivation contributed 2.7% to the country's GDP in 2020 [3]. Pests and diseases, particularly rice thrips damage and rice blast disease, are a challenge for the industry, as they cause yield loss. This paper describes an intelligent solution that aids stakeholders by detecting and classifying the disease, forecasting its dispersion, and providing remedies. The proposed solution is approached with deep learning techniques for real-time detection and classification of the disease, location tracking of infected areas, and pesticide application on the target. In addition, it predicts the spread of disease based on the locations of infected individuals. In addition, the solution enables Machine-learning algorithms to recommend appropriate rice varieties and predict yields. In controlled experiments utilizing data from Sri Lankan paddy fields, the proposed method obtained high accuracy rates of 89%-98% in identifying disease and rice varieties and yield prediction. This system has the potential to increase rice production and productivity, decrease yield loss, and benefit the Sri Lankan rice industry and producers.
  • Thumbnail Image
    PublicationEmbargo
    Effectiveness of Using Radiology Images and Mask R-CNN for Stomatology
    (IEEE, 2022-12-09) Jayasinghe, H; Pallepitiya, N; Chandrasiri, A; Heenkenda, C; Vidhanaarachchi, S; Kugathasan, A; Rathnayaka, K; Wijekoon, J
    Dental health-related disorders have proliferated worldwide due to the excessive intake of fast food and sugary foods, which was followed by bad oral hygiene practices. The cost of dental examinations may change based on how critical the condition is, regardless of whether they are not regular. For a person, diagnosing an oral health problem, particularly locating the disease’s underlying cause, can be challenging. To properly diagnose and treat such conditions, advanced dental diagnostic techniques may be necessary. By offering convenience and enhancing their oral health knowledge, the system seeks to serve as a prediction tool that regular people can utilize to detect potential tooth illnesses at an early stage. It is encompassed as a mobile application where a Mask R-CNN model is used in the core that accepts a dental radiograph as the input. The trained model will be able to identify diseases related to the bone and teeth. Based on the performance evaluations, the accuracy of the results that are obtained in tooth type, restoration quality, dental caries, and periodontal disease identification falls in the range of 75%-80%.