Faculty of Computing

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    AgroPro: Optimizer for Traditional Agricultural System in Sri Lanka
    (IEEE, 2022-12-09) De Silva, D.I.; Suriyawansa, G.M. T. K. D. S.; Senevirathna, M.R. U. M. T.; Balasuriya, I.D. I.; Deshapriya, A. G. S. P.; Gadiarachchi, G. A. D. K. M.
    Today, in many countries around the world, big data analysis and machine learning methods are used for industrial development. However, such techniques are rarely used in Sri Lankan agricultural industry. The success of agriculture depends heavily on the selection of the right crop. Choosing the right crop depends primarily on predicting future yields. Machine learning methods can be used very successfully to make future predictions about crop yields. Crop prediction mainly depends on the soil, geography, and climate of the growing location. Hence historical data with agricultural facts such as temperature, humidity, pH, and rainfall are used to predict yield as parameters in the machine learning function. Sri Lanka uses a traditional approach to distribute fertilizers among farmers. Not having an organized way to distribute fertilizers to the needed areas leads to many abnormalities along the way. As a result, the country is facing economic losses and resource wastage. Having an optimized distribution network is the key to overcoming those abnormalities. This research assesses the efficiency of the fertilizer distribution system and consists of time-series predictions on fertilizer usage to gain future value. The aim is to identify performance gaps in distribution management that lead to delayed fertilizer distribution affecting agricultural productivity.
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    Smart Crop and Fertilizer Prediction System
    (IEEE, 2019-12-05) Wickramasinghe, C. P; Lakshitha, P. L. N; Hemapriya, H. P. H. S; Jayakody, A; Ranasinghe, P. G. N. S
    Agricultural industry plays a major role in the process of economic development as well as the Gross Domestic Product of Sri Lanka. One of the significant issues in the industry is lacking an accurate way to identify the best crop that can be grown with the available soil fertility in a particular land. Since most of the farmers have a lack of knowledge about soil nutrients, they start cultivations by believing myths in society and few of them use scientific approaches. This research mainly focuses on suggesting the best crop according to soil fertility of land and also it recommends a fertilizer plan to optimize the amount of fertilizers applied for suggested crops. The paper presents a tool with embedded sensors that measure soil fertility and developed a cross- platform mobile application to suggest the best crops according to available soil fertility. Further, a fertilizer plan will be suggested to optimize fertilizer usage in order to increase profitability and avoid soil degradation. To evaluate the final product, the same soil sample was tested in the lab and using sensors embedded tool. Results obtained by those tests proven that both generate approximately equal Nitrogen (N), Phosphorus (P) and Potassium (K) values.