Soil Analysis and Moisture Prediction using Machine Learning: A Review

---

Abstract

This India’s agricultural sector employs the most people. Agricultural engagement reports for around 60% of India's population and 18% of its GDP. Low production is due to a absence of research in this industry. Waterlogging, soil erosion, nitrogen shortage, and other issues drive Indian agricultural land. Computational research and machine learning play a important role in advancing the agricultural industry. Different scientific and technological researcher has been performed on soil moisture prediction. Numerous methodologies have been proposed for addressing this challenge, including deep learning, machine learning, Internet of Things (IoT), statistical approaches, and image processing techniques. This paper presents a comprehensive survey of various research works focused on soil moisture prediction utilizing soft computing, data mining machine and learning, techniques. These include support vector machines, neural networks, rough set theory, fuzzy logic, k-means clustering, genetic algorithms, and K-NN (k-nearest neighbors). Additionally, this paper presents existing works and suggestions for future research directions.

---

Keywords

Soil Moisture, Soft Computing, Prediction, Neural Network, Data Mining Convolutional Neural Network, Machine Learning,