Integrating Predictive Models and Remote Sensing for Sustainable Agriculture, Energy, and Environmental Management

This paper focuses on a critical analysis of the application of integrated works of predictive models and remote sensing in agriculture and renewable energy, and environmental conservation. This method involves the use of deep learning approaches, UAV data collection and hyperspectral imagery which gives the researchers an understanding on the real world effectiveness of the systems and technologies analyzed; on predictive models and resource utilization efficiency.

I. Introduction

Technological breakthroughs in remote sensing and climatology in the past few decades identified new opportunities in utilizing these tools in resource management, sustainably in agriculture, energy and the environment. Combination of UAVs and more specifically hyperspectral sensors and MLEs-CNNs has shown to be beneficial in numerous domains such as crop yield assessment, soil moisture estimation, and renewable energy generation prognosis. This paper therefore seeks to examine these technologies in relation to their use, and in relation to how they can improve predictive capability and operations in varied environmental settings [1]–[10].