Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/1028
Title: Predictive Analytics Platform for Organic Cultivation Management
Authors: Rathnayake, R. M. S. M
Ekanayake, E. W. L. M. B
Kahandawala, K. A. I. P
de Silva, W. G. S. C
Nawinna, D. P
Kasthurirathna, D
Keywords: Predictive Analytics
Analytics Platform
Organic Cultivation
Management
Issue Date: 5-Dec-2019
Publisher: IEEE
Citation: R. M. S. M. Rathnayake, E. W. L. M. B. Ekanayake, K. A. I. P. Kahandawala, W. G. S. C. de Silva, D. P. Nawinna and D. Kasthurirathna, "Predictive Analytics Platform for Organic Cultivation Management," 2019 International Conference on Advancements in Computing (ICAC), 2019, pp. 204-209, doi: 10.1109/ICAC49085.2019.9103344.
Series/Report no.: 2019 International Conference on Advancements in Computing (ICAC);Pages 204-209
Abstract: There is an increasing demand for organic farming as an environmentally friendly alternative to industrial agricultural system. It is a method of farming that does not involve pesticides, fertilizers, genetically modified organisms, and growth hormones. Organic farming yields vital benefits such as preservation of soil's organic composition, fertility, structure and biodiversity, reduce erosion and reduce the risks of human, animal, and environmental exposure to toxic materials. This paper presents design and development of a software platform for supporting sustainability of organic agriculture system, which has been implemented as a proof of concept in Sri Lanka. The predictive analytics based service platform that not only supports farming decisions of organic farmers but also offers an electronic market place for organic foods. The proposed system is capable of predicting organic harvests, prices and provide decision support on crop selection for upcoming cultivations. To implement this system, machine learning and optimization techniques have been used. In addition, it uses block chain technology to maintain authentication and identity management of organic farmers so that the consumers can trust they get genuine organic food.
URI: http://rda.sliit.lk/handle/123456789/1028
ISBN: 978-1-7281-4170-1
Appears in Collections:1st International Conference on Advancements in Computing (ICAC) | 2019
Department of Computer Science and Software Engineering -Scopes
Research Papers - IEEE
Research Papers - SLIIT Staff Publications

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