Publication:
Agro-Mate: A Virtual Assister to Maximize Crop Yield in Agriculture Sector

dc.contributor.authorDayalini, S.
dc.contributor.authorSathana, M.
dc.contributor.authorNavodya, P. R. N.
dc.contributor.authorWeerakkodi, R.W. A. I. M. N.
dc.contributor.authorJayakody, A.
dc.contributor.authorGamage, N.
dc.date.accessioned2022-02-07T06:28:08Z
dc.date.available2022-02-07T06:28:08Z
dc.date.issued2021-12-09
dc.description.abstractInformation Technology plays a vital role in the agriculture industry. The main goal of the project is to develop a mobile application to support farmers to take accurate decisions and help them with activities such as soil quality determination, best crop selection, rice disease prediction, and disaster prediction for the wet zone of Sri Lanka. To achieve the main goal the project has incorporated advanced technologies such as Deep Learning, Image Processing (IP), Internet of Things (IoT), and Machine Learning that can support farmers or investors in a way to maximize yield. ‘Agro-Mate’ application is developed in a way to facilitate the agriculture industry. ‘Agro-Mate’ consists of four components such as soil quality determination and fertilizer recommendation, best crop selection, rice disease prediction and recommendation, and natural disaster prediction and providing the recommendation. Also, the application suggests fertilizer when soil is lacking quality and provides recommendations whenever rice diseases or natural disasters are identified. The usage of android mobile devices in agriculture is one of the key components of the sector's growth, which facilitates the farmer's inaccurate decision-making to gain more quality and quantity of crops. Agro-mate’ is more likely to increase the productivity of crops and indirectly increase the GDP of Sri Lanka.en_US
dc.description.sponsorshipCo-Sponsor:Institute of Electrical and Electronic Engineers (IEEE) Academic sponsor:SLIIT UNI Gold Sponsor :London Stock Exchange Group (LSEG)en_US
dc.identifier.doi10.1109/ICAC54203.2021.9671140en_US
dc.identifier.issn978-1-6654-0862-2/21
dc.identifier.urihttps://rda.sliit.lk/handle/123456789/959
dc.language.isoenen_US
dc.publisher2021 3rd International Conference on Advancements in Computing (ICAC), SLIITen_US
dc.subjectImage processingen_US
dc.subjectMachine learningen_US
dc.subjectInternet of thingen_US
dc.titleAgro-Mate: A Virtual Assister to Maximize Crop Yield in Agriculture Sectoren_US
dc.typeArticleen_US
dspace.entity.typePublication

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