Enhancing Seed Selection and Providing Guidance for Cultivation using Random Forest Technique
Ayushi Gupta1, Nikhil Narayan2, Kanmani Sivagar3
1Ayushi Gupta*, CSE, SRM Institute of Science and Technology, Chennai, India.
2Nikhil Narayan, CSE, SRM Institute of Science and Technology, Chennai, India.
3Kanmani Sivagar, CSE, SRM Institute of Science and Technology, Chennai, India.
Manuscript received on April 06, 2020. | Revised Manuscript received on April 14, 2020. | Manuscript published on May 30, 2020. | PP: 189-192 | Volume-9 Issue-1, May 2020. | Retrieval Number: A1458059120/2020©BEIESP | DOI: 10.35940/ijrte.A1458.059120
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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: Seed Selection is a very challenging job because for a selection of a seed multifarious parameters are to be taken under consideration. Also seed analysis require a prediction of which seed is suitable which needs a great accuracy as there are numerous things to be taken into account like soil type, ph of soil, nutrient content of soil, elevation of land, weather of the area, etc. Several algorithms have been devised from time to time but each of the methods differs in their own way. The algorithms, which are discussed, are K-Means Algorithm, K-Nearest Neighbor Algorithm, Naïve Bayes Classifier, Decision Tree, Regression Model, etc. Data mining techniques can overcome this challenging job.
Keywords: Classification, Decision, Estimation, Features, Knowledge, Management, Monitoring, Parameters.
Scope of the Article: Classification