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Article | Green Intelligent Systems and Applications | Volume 3 - Issue 2 - 2023 | 86-97 | https://doi.org/10.53623/gisa.v3i2.313
© 2023 by the author(s), and is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) License Creative Commons Attribution 4.0 International (CC BY 4.0) License

The Utilization of Machine Learning Algorithms for Precision Agriculture: Enhancing Crop Selection

Author(s): Suhas Kakade 1 , Rohan Kulkarni 2 , Somesh Dhawale 3 , Muhammed Fasil C 4
Author(s) information:
1 Department of Electrical Engineering COEP Tech. University, Pune, India.
2 Department of Electrical Engineering, KJCOEMR, Pune, Maharashtra, India
3 Department of Electrical Engineering COEP Tech. University, Pune, Maharashtra, India
4 Department of Applied Electronics and Instrumentation Engineering, GEC Kozhikode, Kerala, India

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Green Intelligent Systems and Applications

Volume 3 - Issue 2 - 2023

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Agriculture stands as a crucial economic driver, playing a pivotal role in fostering economic progress. Understanding the dynamics of the agricultural system is imperative for ensuring food security. Even as technological strides like vertical farming emerge, conventional farming practices and beliefs continue to hold sway. This study delves into fundamental aspects such as soil composition, pH levels, humidity, and rainfall, employing a range of machine learning models including kernel naive Bayes, Gaussian naive Bayes, linear support vector machine (SVM), quadratic discriminant analysis, and quadratic SVM. The primary objective is to provide insightful crop recommendations to farmers. Accurate crop forecasting is paramount for optimizing agricultural methodologies and maintaining a consistent food supply. By leveraging historical weather trends, soil quality, and crop production data, machine learning algorithms proficiently anticipate crop yields. The outcomes of this investigation have the potential to refine crop management practices and reinforce food security measures.

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SUBMITTED: 13 September 2023
ACCEPTED: 19 October 2023
PUBLISHED: 3 November 2023
SUBMITTED to ACCEPTED: 37 days
DOI: https://doi.org/10.53623/gisa.v3i2.313

Cite this article
Kakade, S., Kulkarni, R., Dhawale, S., & Fasil C, M. . (2023). The Utilization of Machine Learning Algorithms for Precision Agriculture: Enhancing Crop Selection . Green Intelligent Systems and Applications, 3(2), 86–97. https://doi.org/10.53623/gisa.v3i2.313
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