Open AccessEnhancing Seed Selection and Providing Guidance for Cultivation using Random Forest Technique
Author(s) -
Ayushi Gupta*,
Nikhil Narayan,
Kanmani Sivagar
Publication year - 2020
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.a1458.059120
Subject(s) - random forest , selection (genetic algorithm) , decision tree , naive bayes classifier , computer science , machine learning , k nearest neighbors algorithm , classifier (uml) , soil nutrients , bayes' theorem , data mining , artificial intelligence , support vector machine , environmental science , soil science , soil water , bayesian probability
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.