A Comprehensive Analysis on AI Driven Mathematical Regression Framework for Predictive Maintenance in Drone Based Crop Monitoring System

The increased use of AI (Artificial Intelligence) in precision agriculture has led to tremendous developments in predictive maintenance and crop monitoring systems, particularly by drone-based systems. This literature review aims to discuss recent developments in artificial intelligence based mathematical regression models, particularly in improving efficiency in maintenance and crop monitoring systems. The discussion in this literature review will particularly focus on recent developments in machine learning and deep learning techniques, particularly in improving efficiency in maintenance and crop monitoring systems. This literature review will also focus on recent developments in integrating Internet of Things technology, drones, sensors, and edge computing technology, particularly in improving efficiency in acquiring data in real time by applying techniques in explainable AI, hybrid learning techniques, and digital twins, particularly in improving efficiency in decision making processes in maintenance and crop monitoring systems. In addition, recent developments in applying thermal imaging technology, computer vision technology, and anomaly detection techniques will be discussed in improving efficiency in identifying faults in equipment by drones in crop monitoring systems. Sustainable and energy efficient techniques in applying AI technology will also be discussed in improving efficiency in climate resilient crop monitoring systems. Thus, this literature review aims to discuss recent developments in technology and methodology, data driven maintenance strategies in drone assisted crop monitoring systems.