Fertilizer Prediction Using Machine Learning

Year : 2024 | Volume :11 | Issue : 02 | Page : 26-35
By

Shreyash J. Mokashi,

Anuradha M. Bhandari,

Sakshi A. Gaikwad,

  1. Student Department of Computer Engineering, RD’Shri Chhatrapati Shivajiraje College of Engineering Maharashtra India
  2. Student Department of Computer Engineering, RD’Shri Chhatrapati Shivajiraje College of Engineering Maharashtra India
  3. Student Department of Computer Engineering, RD’Shri Chhatrapati Shivajiraje College of Engineering Maharashtra India

Abstract

Fertilizer prediction is a critical aspect of modern agriculture, aimed at optimizing resource utilization while maximizing crop yields. In recent years, machine learning (ML) techniques have emerged as powerful tools for addressing this challenge by leveraging data-driven approaches to predict the optimal type and quantity of fertilizer required for different crops and soil conditions. This research paper provides a comprehensive review of the existing literature and methodologies employed in fertilizer prediction using machine learning. It begins by outlining the importance of fertilizer prediction in agriculture and the potential benefits of ML-based approaches. The paper then delves into the various methodologies involved, including data collection, preprocessing, feature selection, model selection, and evaluation metrics. It analyzes the use of several machine learning techniques including neural networks, support vector machines, decision trees, regression, and random forests, for services related fertilizer prediction.. Real- world applications and case studies of ML-based fertilizer prediction systems are highlighted to showcase their effectiveness in improving agricultural practices. Additionally, the paper addresses challenges such as data quality, model interpretability, and scalability, and proposes future research directions to overcome these obstacles and further enhance the utility of ML in fertilizer prediction for sustainable agriculture.

Keywords: Fertilizer prediction, machine learning, agriculture, crop yield optimization, data preprocessing, feature selection, model selection, evaluation metrics, sustainability

[This article belongs to Journal of Mechatronics and Automation(joma)]

How to cite this article: Shreyash J. Mokashi, Anuradha M. Bhandari, Sakshi A. Gaikwad. Fertilizer Prediction Using Machine Learning. Journal of Mechatronics and Automation. 2024; 11(02):26-35.
How to cite this URL: Shreyash J. Mokashi, Anuradha M. Bhandari, Sakshi A. Gaikwad. Fertilizer Prediction Using Machine Learning. Journal of Mechatronics and Automation. 2024; 11(02):26-35. Available from: https://journals.stmjournals.com/joma/article=2024/view=170242

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Regular Issue Subscription Original Research
Volume 11
Issue 02
Received May 27, 2024
Accepted June 22, 2024
Published September 3, 2024
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