Soil Classification and Harvest Proposal Implemented using Machine Learning Techniques
N.Saranya1, A.Mythili2

1Mrs. N. Saranya, Assistant Professor Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology, Coimbatore, (Tamil Nadu), India.
2Ms. A. Mythili, PG Scholar Department of Computer Science and Engineering Sri Shakthi Institute of Engineering and Technology, Coimbatore, (Tamil Nadu), India.

Manuscript received on September 05, 2020. | Revised Manuscript received on September 21, 2020. | Manuscript published on October 10, 2020. | PP: 19-22 | Volume-9 Issue-12, October 2020 | Retrieval Number: 100.1/ijitee.H6110069820  | DOI: 10.35940/ijitee.H6110.1091220
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract: The major source of living for the people of India is agriculture. It is considered as important economy for the country. India is one of the country that suffer from natural calamities like drought and flood that may destroy the crops which may lead to heavy loss for the people doing agriculture. Predicting the crop type can help them to cultivate the suitable crop that can be cultivated in that particular soil type. Soil is one major factor or agriculture. There are several types of soil available in our county. In order to classify the soil type we need to understand the characteristics of the soil. Data mining and machine learning is one of the emerging technology in the field of agriculture and horticulture. In order to classify the soil type and Provide suggestion of fertilizers that can improve the growth of the crop cultivated in that particular soil type plays major role in agriculture. For that here exploring Several machine learning algorithms such as Support vector machine(SVM),k-Nearest Neighbour(k-NN) and logistic regression are used to classify the soil type. 
Keywords: Machine Learning, Mining, Nutrients, Agriculture, Chemical Feature Classification, Accuracy, Prediction.