Research Papers - Dept of Computer Systems Engineering

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    On The Effectiveness of Using Machine Learning and Gaussian Plume Model for Plant Disease Dispersion Prediction and Simulation
    (IEEE, 2020-05-29) Miriyagalla, R; Samarawickrama, Y; Rathnaweera, D; Liyanage, L; Kasthurirathna, D; Nawinna, D; Wijekoon, J
    Agriculture plays a vital role in the economic development of the entire world. Similarly, in Sri Lanka, 6.9% of the national GDP is contributed by the agricultural sector and more than 25% of Sri Lankans are employed in the field of agriculture. But the frequent fluctuations of climate conditions have caused the spread of diseases such as late blight which eventually has led to the devastation of entire plantations of Sri Lankans. To this end, this paper proposes to forecast the possible dispersion pattern and assist the farmers in identifying the possibility of the disease getting dispersed to nearby crops to provide early warning. Eventually, it leads the farmers to take precautions to save the plants before reaching a critical stage. The yielded results show that the proposed method successfully performed disease diagnosis and disease progression level identification with 90-94 % accuracy and dispersion pattern analysis.
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    Simulation of the Influence of environmental factors related to Greenhouses using Augmented Reality
    (IEEE, 2019-12-05) Shaleendra, T; Wishvamali, B. T; Gunarathne, N; Hareendran, S; Abeygunawardhana, P. K. W
    With the vast growth of the population, increased production of agricultural products is necessary. Although the amount of land available for agriculture is limited, the demand for food products based on agriculture is expanding. The Organic food supply is scarce in the current food market; hence, their retail prices are higher than the other agricultural products which were produced with the use of pesticides. In this context, Greenhouse production is widely used all over the world with minimal pesticides and weedicides including Sri Lanka. Automated Greenhouses can be used to increase production with a minimum amount of human labor. With less use of human hours, it will produce more harvest than conventional Greenhouses which need constant human attention and care. The installation of automated Greenhouses is costly although their long-term benefits are higher than a conventional one. For this reason, introducing the concept to cultivators would be difficult, as they are reluctant to invest their money on unfamiliar technology. There is a hesitance to embrace technology since they don't have the first-hand experience in operating an automated Greenhouse. Therefore, in this paper, we present a simulated model of automated Greenhouse using Augmented reality, through which a client can visually experience the workings of IoT Greenhouse based on theoretical models beforehand to make an informed decision to invest in automated Greenhouses.
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    PublicationEmbargo
    On the effectiveness of using machine learning and Gaussian plume model for plant disease dispersion prediction and simulation
    (IEEE, 2019-12-05) Miriyagalla, R; Samarawickrama, Y; Rathnaweera, D; Liyanage, L; Kasthurirathna, D; Nawinna, D; Wijekoon, J. L
    Agriculture plays a vital role in the economic development of the entire world. Similarly, in Sri Lanka, 6.9% of the national GDP is contributed by the agricultural sector and more than 25% of Sri Lankans are employed in the field of agriculture. But the frequent fluctuations of climate conditions have caused the spread of diseases such as late blight which eventually has led to the devastation of entire plantations of Sri Lankans. To this end, this paper proposes to forecast the possible dispersion pattern and assist the farmers in identifying the possibility of the disease getting dispersed to nearby crops to provide early warning. Eventually, it leads the farmers to take precautions to save the plants before reaching a critical stage. The yielded results show that the proposed method successfully performed disease diagnosis and disease progression level identification with 90-94 % accuracy and dispersion pattern analysis.