Machine Learning Techniques for Predicting Industries Based on Region

Year : 2023 | Volume : 01 | Issue : 01 | Page : 1-8
By

    Supriya Kapase

  1. Chaitanya Bari

  2. Chaitrali Bhambure

  3. Pallavi Chopade

  4. Padmini Kondhalkar

  1. Professor, Department of Computer Engineering, Nbn Sinhgad Technical Institutes Campus, Pune, India
  2. Student, Department of Computer Engineering, Nbn Sinhgad Technical Institutes Campus, Pune, India
  3. Student, Department of Computer Engineering, Nbn Sinhgad Technical Institutes Campus, Pune, India
  4. Student, Department of Computer Engineering, Nbn Sinhgad Technical Institutes Campus, Pune, India
  5. Student, Department of Computer Engineering, Nbn Sinhgad Technical Institutes Campus, Pune, India

Abstract

In recent years, there has been a growing interest and emphasis on agricultural land preparation and its implementation among researchers, primarily due to various factors. These factors include an increased focus within the research community, a rising demand for agricultural land, and the significance of assessing soil health for ensuring robust crop production. Picture request is one such philosophy for soil and land prosperity examination. It is a staggering measure having the effects of various dimensions. This paper has suggested an investigation into the stream, analyzing the problems it addresses, as well as its future possibilities. The emphasis is focused on the intelligent examination of various advanced and successful gathering frameworks and methodology. Here, various components have been taking into account and these techniques have been directed to work on the accuracy of the portrayal. Suitable use of the features of remotely recognized data and picking the best sensible classifier are by and large huge for working on the accuracy of the data collected. The data-based game plan or non-parametric classifier like brain network has procured pervasiveness for multisource data gathering lately. Nevertheless, there is at this point the degree of extra investigation, to decrease weaknesses in the improvement of accuracy of the image gathering instruments. Support vector machine calculation is utilized to suggest the harvests in light of the soil conditions. Within this project, we strongly advocate for the adoption of the K nearest neighbor model by industries as well. We have created a skilled dataset for industries. We have worked on five regions, namely Konkan, Marathwada, Vidarbha, Nashik, and Pune.

Keywords: Convolutional neural network, support vector machine, K nearest neighborhood, crop prediction system

[This article belongs to International Journal of Algorithms Design and Analysis Review(ijadar)]

How to cite this article: Supriya Kapase, Chaitanya Bari, Chaitrali Bhambure, Pallavi Chopade, Padmini Kondhalkar Machine Learning Techniques for Predicting Industries Based on Region ijadar 2023; 01:1-8
How to cite this URL: Supriya Kapase, Chaitanya Bari, Chaitrali Bhambure, Pallavi Chopade, Padmini Kondhalkar Machine Learning Techniques for Predicting Industries Based on Region ijadar 2023 {cited 2023 Jul 29};01:1-8. Available from: https://journals.stmjournals.com/ijadar/article=2023/view=116568

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Regular Issue Subscription Review Article
Volume 01
Issue 01
Received June 7, 2023
Accepted June 23, 2023
Published July 29, 2023