A Machine Learning Approach to Smart Farming
Shailaja Udtewar
, Prajakta Dagade , Piyush Khatpe , Rohit Shembekar
Machine Learning, Random Forest, Decision Tree, Naive Bayes, K-Nearest Neighbors, Logistic Regression, Comparative Analysis, Performance Metrics.
Five popular machine learning algorithms are thoroughly compared in this study: Random Forest, Decision Tree, Naive Bayes, K-Nearest Neighbors (KNN), and Logistic Regression. The objective of the study is to assess and compare these algorithms’ performance on various datasets, including factors like accuracy, precision, recall, and F1 score. All algorithms were built, adjusted, and trained in accordance with standard protocols, and experimentation was used to identify performance metrics. The outcomes demonstrate the algorithms’ performance. High-dimensional, complex data sets work well for Random Forest, but Decision Trees are easier to grasp. When analyzing categorical data, Naive Bayes is robust against extraneous characteristics. When local patterns are significant, KNN works well; nevertheless, for binary data, logistic regression becomes an invaluable tool.
"A Machine Learning Approach to Smart Farming", IJSDR - International Journal of Scientific Development and Research (www.IJSDR.org), ISSN:2455-2631, Vol.9, Issue 2, page no.156 - 172, February-2024, Available :https://ijsdr.org/papers/IJSDR2402024.pdf
Volume 9
Issue 2,
February-2024
Pages : 156 - 172
Paper Reg. ID: IJSDR_209974
Published Paper Id: IJSDR2402024
Downloads: 000347236
Research Area: Engineering
Country: Mumbai, Maharashtra, India
ISSN: 2455-2631 | IMPACT FACTOR: 9.15 Calculated By Google Scholar | ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 9.15 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator
Publisher: IJSDR(IJ Publication) Janvi Wave
