A Study of Cloud-Enabled Deep Learning for Monitoring and Predicting Soil Health in Agriculture

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This is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.

Year : 2025 | Volume : 16 | Issue : 02 | Page : –
    By

    Ritik Sahu,

  • Vinay Lowanshi,

  • Nitya Khare,

  1. M.Tech Scholar, Department of computer science and Engineering , Sagar Institute of Research & Technology – Excellence (SIRTE)Bhopal, Madhya Pradesh, India
  2. Assistant Professor, Department of computer science and engineering , Sagar Institute of Research & Technology – Excellence (SIRTE)Bhopal, Madhya Pradesh, India
  3. Assistant Professor, Department of computer science and engineering , Sagar Institute of Research & Technology – Excellence (SIRTE)Bhopal, Madhya Pradesh, India

Abstract

Soil health is a critical factor in ensuring sustainable agricultural practices and food security. Traditional methods for soil health assessment are often time-consuming, localized, and lack scalability. This study explores the integration of cloud-enabled deep learning techniques to monitor and predict soil health efficiently. Leveraging data from IoT sensors, satellite imagery, and lab-based analyses, a cloud-based framework is proposed to process and analyze soil health parameters such as pH, moisture content, and nutrient levels. Two advanced deep learning models, convolutional neural networks (CNNs) and recurrent neural networks (RNNs), are used to predict soil conditions and identify trends that impact crop yield. The use of cloud computing platforms ensures real-time data processing, scalability, and cost-effective deployment. The framework is a useful tool for precision agriculture because of its excellent accuracy and dependability in forecasting soil health, as shown by experimental findings. This study highlights the potential of combining cloud technologies and artificial intelligence to address the challenges in sustainable farming and paves the way for future research in this domain.

Keywords: Soil Health, Convolutional Neural Networks, Recurrent Neural Networks

[This article belongs to Journal of Remote Sensing & GIS ]

How to cite this article:
Ritik Sahu, Vinay Lowanshi, Nitya Khare. A Study of Cloud-Enabled Deep Learning for Monitoring and Predicting Soil Health in Agriculture. Journal of Remote Sensing & GIS. 2025; 16(02):-.
How to cite this URL:
Ritik Sahu, Vinay Lowanshi, Nitya Khare. A Study of Cloud-Enabled Deep Learning for Monitoring and Predicting Soil Health in Agriculture. Journal of Remote Sensing & GIS. 2025; 16(02):-. Available from: https://journals.stmjournals.com/jorsg/article=2025/view=209786


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Regular Issue Subscription Review Article
Volume 16
Issue 02
Received 25/03/2025
Accepted 08/05/2025
Published 15/05/2025
Publication Time 51 Days
Total Visits: 103


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