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Potential Coffee Production Hot-spots Using Machine Learning Techniques: Nagaland and Manipur, India

Sharma, Nitish (2023) Potential Coffee Production Hot-spots Using Machine Learning Techniques: Nagaland and Manipur, India. Masters thesis, Dublin, National College of Ireland.

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Coffee is arguably one of the most consumed and traded beverages worldwide. The coffee plant is particularly climatically sensitive, requiring certain soil, altitude, and temperature conditions. The seeds of coffee fruits resembling cherries are roasted and prepared for export. As coffee’s demand has steadily increased over the years, poor yields can disrupt the supply chain. Less area remains suited for coffee plantations due to global warming. Therefore, crops must be relocated to new places to accommodate the rising demand. Only the states of Karnataka, Kerala, and Andhra Pradesh grow coffee in India, making it one of the world’s main coffee producers. The climate of the North East Indian States, Chhattisgarh, and sections of Maharashtra is also conducive to the growth of coffee plantations. On local PCs, classic research has employed GIS software and machine learning on raster data. This study investigates the use of geographical data and machine learning with cloud computing to identify alternative habitats. This discovery will enable the development of similar models for other crops and, as a result, the research will be scaled up using the cloud computing capability. While this approach largely eliminates the scalability difficulties of conventional geospatial analysis, cloud platforms are still undergoing development and offer fewer algorithms at present.

Item Type: Thesis (Masters)
Mulwa, Catherine
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Food Industry > Beverage industry
T Technology > T Technology (General) > Information Technology > Cloud computing
Q Science > Q Science (General) > Self-organizing systems. Conscious automata > Machine learning
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Tamara Malone
Date Deposited: 26 May 2023 12:05
Last Modified: 26 May 2023 12:05

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