Machine Learning based Soil Moisture and Mineral Detection using Satellite and Aerial Imagery

Shivam Sharma

Shivam Sharma BS Abdur Rahman Crescent Institute of Science and Technology, Chennai, India

Abstract

Soil moisture identification is an essential topic in the agricultural sector and geological research. It is a critical factor in determining the health and productivity of plants, as it affects the availability of nutrients, the growth of plants and vegetation, and the overall water balance of the ecosystem. Farmers’ traditional techniques are insufficient to meet the increasing demands, so they interfere with the cultivation of the soil. To accomplish good and modern farming, investing in expensive labor and equipment is necessary. Machine learning based soil moisture using satellite imagery brings a new solution to this problem that not only saves labor costs but also reduces errors. This paper talks about the detection of soil moisture using satellite images. In this paper, we explore the use of various machine learning algorithms, including random forests, Support Vector Machines (SVM), and neural networks like Convolutional Neural Network (CNN) and Artificial Neural Network (ANN), to predict soil moisture values from satellite and aerial imagery. Our approach has the potential to provide more accurate soil moisture information than traditional manual methods. The study was conducted in March (spring season) in the land of Punjab state of India. An accuracy of 79.35% was obtained through the Random Forest Algorithm.

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