Machine Learning Application in G.I.S. and Remote Sensing: An Overview

Abstract

Machine learning (ML) is a subdivision of artificial intelligence in which the machine learns from machine-readable data and information. It uses data, learns the pattern and predicts the new outcomes. Its popularity is growing because it helps to understand the trend and provides a solution that can be either a model or a product. Applications of ML algorithms have increased drastically in G.I.S. and remote sensing in recent years. It has a broad range of applications, from developing energy-based models to assessing soil liquefaction to creating a relation between air quality and mortality. Here, in this paper, we discuss the most popular supervised ML models (classification and regression) in G.I.S. and remote sensing. The motivation for writing this paper is that ML models produce higher accuracy than traditional parametric classifiers, especially for complex data with many predictor variables. This paper provides a general overview of some popular supervised non-parametric ML models that can be used in most of the G.I.S. and remote sensing-based projects. We discuss classification (Naïve Bayes (NB), Support Vector Machine (SVM), Random Forest (RF), Decision Trees (DT)) and regression models (Random Forest (RF), Support Vector Machine (SVM), Linear and Non-Linear) here. Therefore, the article can be a guide to those interested in using ML models in their G.I.S. and remote sensing-based projects

AI-Enhanced Precision Irrigation in Legume Farming: Optimizing Water Use Efficiency

“…Artificial Intelligence (AI) offers a novel solution for addressing these issues. Unlike traditional irrigation techniques that operate on pre-established schedules or manual observations (Reddy et al, 2017), AI-driven precision irrigation employs the capabilities of machine learning, data analytics and real-time monitoring to imitate the details of natural ecosystems (Akhter et al, 2022;Yang et al, 2021;Shah, 2009;Upreti, 2022). It adapts to the ever-changing variables in a field, much like how the human body regulates its temperature in response to external conditions.…”