Browsing by Author "Gamage, A.I"

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • 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
    Supervising Plant Growth in a Greenhouse
    (IEEE, 2022-11-30) Alexander, D.L; Hathnapitiya, G.A.G.; Gamage, A.I; Bandara, P.M.P.C; Giragama, G.W.M.N.U.I.B; Supunya Swamakantha, N.H.P.R
    Since the beginning of civilization, agriculture has played a significant part in the economy of a nation. Currently, as the population continues to increase at a rapid rate, arable lands are dwindling alongside urbanization. Even though farmers devote a substantial amount of time and effort to farming, environmental factors such as seasonal shifts can have a significant impact on the crop. Smart agriculture is implemented to boost the production of high-quality goods and address the lack of control over the farming process. The intelligent greenhouse technology proposed here is called “GSense,” and it could boost plant productivity by managing the greenhouse’s climate. In addition, this solution is useful for novices who are just beginning out in agriculture because it can make recommendations to its user. The execution of the solution is complemented by a mobile application and a desktop application via which the user may submit inputs and examine real-time sensor data