Machine Learning for Soil Moisture Detection: Introduction, Approaches and Challenges

Year : 2025 | Volume : 14 | Issue : 03 | Page : 88 96
    By

    Arwinder Singh,

  • Bhawna Jindal,

  1. Assistant Professor, Department of Computer Science, University College, Ghanaur, Punjabi University, Patiala, Punjab, India
  2. Assistant Professor, , Department of Computer Science, Guru Kashi University, Talwandi Sabo., Punjab, India

Abstract

The demand for agricultural is increasing day by day as the population of the world is increasing. So, it becomes necessary for us to increase the production of agricultural products. Traditional ways of agriculture cannot meet such requirements. Nowadays, machine learning based technologies are being used to develop models for agriculture. Machine learning-based applications are very fast and produce high-quality results. It includes recurrent neural networks (RNN), convolution neural networks (CNN) and current state-of-the-art transformer-based models. These technologies have achieved great success in developing models of soil moisture detection, smart greenhouse, disease detection, aerial images, drones and weather forecast. Though, there are various models available in modern agriculture. Still, there is a vacant space in soil moisture detection. Review soil moisture is so important because the soil plays the main role in good production of crops. This chapter aims to introduce various branches of agriculture where machine learning based algorithms are being used. Another important aspect of this chapter is that it presents a review of machine learning based approaches for soil moisture detection. An introduction to machine learning, deep learning and transformer architecture is also explained in this chapter. There are various challenges to implementing AI in agriculture, those challenges are also discussed in this chapter.

Keywords: Soil Moisture Prediction, Machine Learning in Agriculture, Deep Learning Models, Remote Sensing, Smart Farming

[This article belongs to Research & Reviews : Journal of Agricultural Science and Technology ]

How to cite this article:
Arwinder Singh, Bhawna Jindal. Machine Learning for Soil Moisture Detection: Introduction, Approaches and Challenges. Research & Reviews : Journal of Agricultural Science and Technology. 2025; 14(03):88-96.
How to cite this URL:
Arwinder Singh, Bhawna Jindal. Machine Learning for Soil Moisture Detection: Introduction, Approaches and Challenges. Research & Reviews : Journal of Agricultural Science and Technology. 2025; 14(03):88-96. Available from: https://journals.stmjournals.com/rrjoast/article=2025/view=233927


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Regular Issue Subscription Review Article
Volume 14
Issue 03
Received 28/05/2025
Accepted 10/09/2025
Published 10/12/2025
Publication Time 196 Days
137

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