Adaptive autonomous UAV scouting for rice lodging assessment using edge computing with deep learning EDANet

•Adaptive autonomous UAV (AUAV) scouting is proposed using edge computing and deep learning with EDANet for rice lodging assessments.•Adaptive AUAV scouting includes different flying heights in one mission for comprehensive lodging assessments.•A prior simulation considering energy consumption for flight action composition is performed for mission planning.•EDANet is employed to provide preliminary classification on coarse resolution UAV images as a basis for adaptive AUAV scouting.•Adaptive AUAV scouting can optimize cost and accuracy efficiently for practical lodging assessments. Rice is a globally important crop that will continue to play an essential role in feeding our world as we grapple with climate change and population growth. Lodging is a primary threat to rice production, decreasing rice yield, and quality. Lodging assessment is a tedious task and requires heavy labor and a long duration due to the vast land areas involved. Newly developed autonomous crop scouting techniques have shown promise in mapping crop fields without any human interaction. By combining autonomous scouting and lodged rice detection with edge computing, it is possible to estimate rice lodging faster and at a much lower cost than previous methods. This study presents an adaptive crop scouting mechanism for Autonomous Unmanned Aerial Vehicles (UAV). We simulate UAV crop scouting of rice fields at multiple levels using deep neural networks and real UAV energy profiles, focusing on areas with high lodging. Using the proposed method, we can scout rice fields 36% faster than conventional scouting methods at 99.25% accuracy.