Optimizing Agricultural Drone Performance: An Environmental-Aware Approach to Battery Status Prediction

This research investigates the prediction of battery status in agricultural drones to enhance operational reliability and efficiency in precision farming applications. As autonomous drones become critical tools for crop monitoring and management, accurate battery status prediction is essential for preventing mid-flight failures that can damage crops and equipment. We utilized a comprehensive dataset of 10,000 records collected from agricultural monitoring flights, containing environmental variables, operational parameters, and battery metrics across diverse agricultural environments. Our methodology included detailed feature engineering to identify key predictors, mathematical modeling of battery behavior, and evaluation of five machine learning algorithms for predicting battery status. Results demonstrated that Random Forest achieved superior performance (94.2% accuracy) compared to other models, with battery temperature, ambient temperature, and flight duration emerging as the most influential predictors. The integration of environmental and operational features significantly improved prediction reliability in agricultural settings. This study establishes a mathematical framework for enhancing drone reliability in precision agriculture and identifies promising directions for future research, including season-specific models for extreme conditions, real-time adaptive prediction systems, and integration with crop monitoring applications to optimize agricultural drone operations.