Abstract
Abstract This project deals with the study of soil properties, crop and the regional influences along with their dependencies which would be further used for a digital map. Both classification and regression algorithms were carried out and a decision tree as well as a decision regressor tree was plotted to finalise the results. Out of the 6 classification algorithms applied decision tree gave the highest accuracy of 95.24% and linear regression gave the best accurate results of 100% among the 3 regression algorithms.
Highlights
Algorithms: Result obtained for different classification algorithms are as follows: For decision tree classifier had an accuracy of 95.24% was obtained, Random forest classifier, Gaussian Nb, K nearest neighbors classifier and Logistic regression obtained 92.8 %
From the analysis it is clear that decision tree is the best classifier for classification of the yield and linear regression is the accurate regressor for predicting the yield
From the decision tree and decision regressor tree it is visible that the variables,storm,and shallow deep terrace soil are the most contributing features to the yield class
Summary
Economy is highly dependent on agriculture which is the basic building block of the economic system of a country.Along with providing of food and raw materials it is a source of various employment opportunities as well. Soil is an important feature when it comes to agriculture which provides sufficient nutrients for a successful cultivation.Likewise, weather plays an important role in agricultural production. Weather aberrations may cause physical damage to crops and erosion. The quality of crop produced during movement from field to storage and transport to plug depends on weather. The primary function of this technology is to supply maps that give accurate representations of a specific area, detailing major road arteries and other points of interest Digital mapping is used world wide to map soil classes and its features. The introduction of machine learning (ML) algorithms in DSM is a game changing way for scientists in the production of maps. Machine learning is widely used to map soil properties or classes just like in other fields of study
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