Research Papers - Dept of Computer Systems Engineering

Permanent URI for this collection https://rda.sliit.lk/handle/123456789/1253

Browse

Filters

2020 - 20226

Advanced Search

Filter by

Settings

Start date: 2020Subject: search.filters.subject.Sri Lanka

Search Results

Now showing 1 - 6 of 6
  • Thumbnail Image
    PublicationEmbargo
    A Smart Aquaponic System for Enhancing The Revenue of Farmers in Sri Lanka
    (IEEE, 2022-10-19) Ekanayake, D; de Alwis, P; Harshana, P; Munasinghe, D; Jayakody, A; Gamage, N
    Sri Lanka's agricultural sector confronts serious challenges from fertilizer shortages and agriculture-related chemical scarcity. Innovations comparable to aquaponic systems may be offered to Sri Lankan farmers to overcome these difficulties using IoT and ML technology. This research scope is to implement a smart and secure aquaponic environment monitoring system to forecast plant and fish growth factors, provide Sri Lankan farmers with insights into the environment's behaviors, and take measures according to the predictions utilizing control mechanisms. In this research, more exact predictions have been generated by the Random Forest algorithm model rather than the LSTM model, and most of the investigated parameters given good accuracy according to the absolute mean error (Media TDS-1.95, Media pH-0.06, Media Temperature-0.49, Env. Temperature- 0.94, Env. Humidity-2.70) except the environment light intensity (64.11). The ML solution studied in this research paper would increase the quality of traditional agriculture in Sri Lanka for greater productivity and economic benefit.
  • Thumbnail Image
    PublicationOpen Access
    Machine Learning Modelling of the Relationship between Weather and Paddy Yield in Sri Lanka
    (Hindawi, 2021-05) Ekanayake, P; Rankothge, W; Weliwatta, R; Jayasinghe, J. M. J. W
    This paper presents the development of crop-weather models for the paddy yield in Sri Lanka based on nine weather indices, namely, rainfall, relative humidity (minimum and maximum), temperature (minimum and maximum), wind speed (morning and evening), evaporation, and sunshine hours. The statistics of seven geographical regions, which contribute to about two-thirds of the country’s total paddy production, were used for this study. The significance of the weather indices on the paddy yield was explored by employing Random Forest (RF) and the variable importance of each of them was determined. Pearson’s correlation and Spearman’s correlation were used to identify the behavior of correlation in a positive or negative direction. Further, the pairwise correlation among the weather indices was examined. The results indicate that the minimum relative humidity and the maximum temperature during the paddy cultivation period are the most influential weather indices. Moreover, RF was used to develop a paddy yield prediction model and four more techniques, namely, Power Regression (PR), Multiple Linear Regression (MLR) with stepwise selection, forward (step-up) selection, and backward (step-down) elimination, were used to benchmark the performance of the machine learning technique. Their performances were compared in terms of the Root Mean Squared Error (RMSE), Correlation Coefficient (R), Mean Absolute Error (MAE), and the Mean Absolute Percentage Error (MAPE). As per the results, RF is a reliable and accurate model for the prediction of paddy yield in Sri Lanka, demonstrating a very high R of 0.99 and the least MAPE of 1.4%.
  • Thumbnail Image
    PublicationEmbargo
    Influencial Factors of Adopting Digital Banking by Users in Western Province of Sri Lanka
    (IEEE, 2020-12-10) Rathnaweera, L; Karunasena, A
    Information Technology has transformed entire society while changing our day to day lives immensely in the past few decades. The emergence of digital technologies enables new business models and inline with that establishes much potential in transforming banking industry. In the process of innovation, banks have adopted numerous cutting-edge technologies to develop computer frameworks, computer system networks, and digital communication infrastructures. Distinct to many developed countries, digital banking is a new phenomenon for Sri Lankan context as majority of banking service providers and clients appears to be fairly unacquainted with different aspects of this service. The purpose of this study is to identify influential factors for adopting digital banking of customers residing in Western Province of Sri Lanka. For the above purpose, technology acceptance model (TAM) has been used to define the research framework and the study employs the quantitative research methods for data collection and analysis. The results of the research show perceived usefulness, perceived ease of use, security and trust, attitude towards digital banking and subjective norm has a great influence towards to adopting digital banking.
  • Thumbnail Image
    PublicationEmbargo
    Smart Agriculture Prediction System for Vegetables Grown in Sri Lanka
    (IEEE, 2021-10-27) Gamage, R; Rajapaksa, H; Sangeeth, A; Hemachandra, G; Wijekoon, J; Nawinna, D. P
    Agriculture planning plays a dominant role in the economic growth and food security of agriculture-based countries such as Sri Lanka. Even though agriculture plays a vital role, there are still several major complications to be addressed. Some of the major complications are lack of knowledge about yield and price resulting in the farmers selecting crops based on experience. Machine learning has a great potential to solve these complications. To this end, this paper proposes a novel system comprises of a mobile application, SMS (Short Message Service), and API (Application Programming Interface) with yield prediction, price prediction, and crop optimization. Several machine learning algorithms were used for yield and price predictions while a generic algorithm was used to optimize crops. The yield was predicted considering the environmental factors while the price was predicted considering supply and demand, import and export, and seasonal effect. To select the best suitable crops to cultivate, the output of yield and price prediction have been used. Yield prediction has been implemented using elastic net, ridge, and multilinear regression. R2 of yield prediction is varied from 0.74 to 0.89 while RMSE value is between 15.69 and 35.05. Price prediction has been implemented using the algorithms of Gradient Boosting Tree, Random Forest, Facebook Prophet, and R2 is varied from 0.72 to 0.92 while RMSE value is between 26.81 and 140.72. Crop optimization has been implemented using the genetic algorithm.
  • Thumbnail Image
    PublicationOpen Access
    IOT-based Monitoring System for Oyster Mushroom Farms in Sri Lanka
    (KDU IRC, 2022-01-10) Surige, Y. D; Perera, W. S. M; Gunarathna, P. K. M; Ariyarathna, K. P. W; Gamage, N. D. U; Nawinna, D. P
    Oyster Mushrooms are a type of a fungus which is very sensitive to the environmental factors and vulnerable to diseases and pest attacks which directly effects local trade and export strength. Mushroom is a climacteric type of food which continues its cycle even after harvesting. The mushroom farming process still uses manual mode such as the identification of diseases uses a farmers eye visually, harvesting of mushrooms are decided based on the visual appearance while the environmental factors are decided based on gut feelings. These methods has its limitations which requires more potential to improve both the quality and capacity of mushroom production. With the advancements of technology, this farming process can be performed with the aid of an IoT device and deep learning model. This research applies Convolutional Neural Networks (CNN) with Mobile Net V2 model to detect mushroom harvest time and any disease spread with an accuracy of 92% and 99% respectively. Long Short-Term memory (LSTM) to analyze the detected environmental factors with an accuracy of 89% and this system predicts the yield of mushroom production with the support of LSTM model with an accuracy of 97%. This developed system which aids mushroom farming activities is connected with the farmers through s mobile application
  • Thumbnail Image
    PublicationEmbargo
    Smart Agriculture Prediction System for Vegetables Grown in Sri Lanka
    (IEEE, 2021-10-27) Gamage, R; Rajapaksa, H; Sangeeth, A; Hemachandra, G; Wijekoon, J; Nawinna, D
    Agriculture planning plays a dominant role in the economic growth and food security of agriculture-based countries such as Sri Lanka. Even though agriculture plays a vital role, there are still several major complications to be addressed. Some of the major complications are lack of knowledge about yield and price resulting in the farmers selecting crops based on experience. Machine learning has a great potential to solve these complications. To this end, this paper proposes a novel system comprises of a mobile application, SMS (Short Message Service), and API (Application Programming Interface) with yield prediction, price prediction, and crop optimization. Several machine learning algorithms were used for yield and price predictions while a generic algorithm was used to optimize crops. The yield was predicted considering the environmental factors while the price was predicted considering supply and demand, import and export, and seasonal effect. To select the best suitable crops to cultivate, the output of yield and price prediction have been used. Yield prediction has been implemented using elastic net, ridge, and multilinear regression. R2 of yield prediction is varied from 0.74 to 0.89 while RMSE value is between 15.69 and 35.05. Price prediction has been implemented using the algorithms of Gradient Boosting Tree, Random Forest, Facebook Prophet, and R2 is varied from 0.72 to 0.92 while RMSE value is between 26.81 and 140.72. Crop optimization has been implemented using the genetic algorithm.