Research Publications

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Open Access
Paddy Disease Identification and Impact Calculation Using Machine Learning
(SLIIT Business School, 2023-12-14) Sandeepanie, N; Rathnayake, S; Gunasinghe, A
Rice is a crucial staple crop globally, providing over half of humanity's caloric intake. It supports the livelihoods of small-scale farmers and landless laborers worldwide. With the growing population, there is a high demand for rice production. Sri Lanka is renowned for its high- quality rice and has a long history of paddy cultivation. However, not all the country's 708,000 hectares of land dedicated to paddy cultivation are utilized due to water scarcity and unstable terrain. The objective of this paper is to explore the ways of enhancing the quality of the paddy crop during its vegetative phase by early identification of diseases through the utilization of emerging technologies. The vegetative phase constitutes a critical stage in the growth of paddy, exerting significant influence on the overall yield, resistance to pests and diseases, nutrient assimilation, and the environmental implications of agricultural practices. The primary emphasis of this paper is to identify diseases to which paddy crops are susceptible during the vegetative phase and subsequently present avisual representation of their locations on a map, serving as the output for end-users. Early identification of paddy diseases is crucial for effective crop management and high yields. These diseases, caused by different pathogens, can significantly hinder plant growth and productivity if not detected and treated promptly. Identifying them early allows farmers and experts to take timely and targeted actions, like applying suitable fungicides or implementing cultural practices, to control their spread and minimize crop damage.
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Non-Communicable Diseases Detection System
(IEEE, 2021-12-09) Thudawehewa, H. R; Jayawardhana, W. A. P. T; Wellehewa, C. G; Silva, C; Rathnayake, P
This research paper presents a Non-communicable Diseases Detection System which is a centralized medical system designed for general public usage. The system aims to provide help for people with non-communicable diseases. In a pandemic situation like this where people find it difficult to reach medical facilities and staff, the system is more advantageous. The system covers areas related to the medical report analysis, BMI value prediction, and breast cancer analysis related to non-communicable diseases. Presently health reports are taken for every disease. BMI is a factor essential to everyone to lead a healthy life. The majority of women suffer from breast cancer. As per the findings of the report, the report analysis predicts possible diseases that can occur in the person concerned. In BMI prediction, particularly the possible BMI value and weight value for the next month is predicted. In Mammogram detection, it gives the current status of the breast. The report analysis model has 90.6% accuracy while the BMI prediction model has 99.7% accuracy. The mammogram detection model proved that it has 96.5% accuracy. All the aforesaid procedures were carried out by analyzing related data systematically. Machine learning, Deep learning, and Image processing techniques were used to develop this system. The main purpose of this system is to make the persons aware of their current health status and to prevent them from having non-communicable diseases.

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