Ultra-Wideband Localization and Deep-Learning-Based Plant Monitoring Using Micro Air Vehicles

Abstract

Micro air vehicles (MAVs) are ideal for indoor precision farming due to their agility and minimal impact on surrounding objects. To achieve autonomous operations, these vehicles hardly rely on the commonly used Global Positioning System for indoor navigation and monitor plants with severely limited payloads. This paper proposes an autonomous MAV system that incorporates an ultra-wideband (UWB) system and a deep learning method to perform indoor navigation and localize unhealthy plants. The position of the MAV is estimated using an extended Kalman filter based on the trilateration results of the ranging measurements obtained from the UWB system. A multiple bounding box prediction strategy is used to analyze all the leaves of the plants and quickly identify their health conditions. Several deep learning models were selected and trained to detect unhealthy plants using field condition images. The model that achieved the desired combination of the accuracy and efficiency was fine-tuned with various image resolutions and sizes of negative samples to further improve its detection accuracy. Several real-world flight tests were performed successfully using the UWB system, and the plants were classified correctly with the selected model.