Faculty of Computing

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Author: search.filters.author.Nawinna, D. PSubject: search.filters.subject.Sri Lanka

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    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.
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    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
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    PublicationEmbargo
    Android based e-Learning solution for early childhood education in Sri Lanka
    (IEEE, 2013-04-26) Priyankara, K. W. T. G. T; Mahawaththa, D. C; Nawinna, D. P; Jayasundara, J. M. A; Tharuka, K. D. N; Rajapaksha, S. K
    Preschool age is critical for a child's development. The parents of competitive society today are challenged to meet learning needs of children. They are unable to dedicate time and are not up-to-date with change of technology. The need for easy to use and effective learning aids has become vital. This research investigates how to support self-learning of modern-day preschoolers. Kids Training e-Learning System (KTeLs) is a learning tool that facilitate self learning of preschool kids. It is based on a strong theoretical foundation and allows kids to develop cognitive and psychomotor skills such as drawing, writing, recognition of numbers, basic shapes and colors and logical thinking. It incorporates a special algorithm to detect and guide the kid to write a letter in the correct direction without guidance of parents. It comes with kids-friendly navigation. The tool was designed as an Android application for tablets and was tested with a focus group. The backgrounds, sounds and colors are especially designed to maintain the attention of kids.