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Application of Machine Learning Techniques for Soil Type Classification of Karanataka

Raza Ansari, Shozab (2018) Application of Machine Learning Techniques for Soil Type Classification of Karanataka. Masters thesis, Dublin, National College of Ireland.

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Soil science and its integration with machine learning has been into practice since the past few decades. Within the agriculture domain soil classification is an essential work that has to be conducted so as to provide good classificaction systmes for the soil types. Karnataka state has registerd the highest suicides in India. With the data about soil health of Karnataka state the different types of soils was analyzed and classified using different machine learning techniques. This research study for classification of soil types was conducted using tree-based model Decision Tree (C5.0), Random Forest (RF). Support Vector Machines (SVM) and eXtreme Gradient Boosting (XGBOOST). Accuracy and Kappa values suggested XGBOOST performed the best whereas the time of execution for these models differed and Random Forest had the most effient compution time with relatively comparable accuracy.

Item Type: Thesis (Masters)
Subjects: Q Science > QA Mathematics > Electronic computers. Computer science
T Technology > T Technology (General) > Information Technology > Electronic computers. Computer science
Q Science > QA Mathematics > Computer software
T Technology > T Technology (General) > Information Technology > Computer software
H Social Sciences > HD Industries. Land use. Labor > Specific Industries > Agriculture Industry
G Geography. Anthropology. Recreation > GE Environmental Sciences > Environment
Divisions: School of Computing > Master of Science in Data Analytics
Depositing User: Caoimhe Ní Mhaicín
Date Deposited: 06 Nov 2018 11:52
Last Modified: 06 Nov 2018 11:52

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