Adaptive autonomous UAV scouting for rice lodging assessment using edge computing with deep learning EDANet
Adaptive autonomous UAV scouting for rice lodging assessment using edge computing with deep learning EDANet
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DOI：
10.1016/j.compag.2020.105817
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发表时间：
2020-12-01
影响因子：
8.3
通讯作者：
Stewart, Christopher C.
中科院分区：
文献类型：
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作者：
Yang, Ming-Der;Boubin, Jayson G.;Stewart, Christopher C.
水稻是一种全球重要的作物，在我们努力应对气候变化和人口增长的同时，它将继续在养活我们的世界方面发挥重要作用。倒伏是水稻生产的主要威胁，降低了水稻产量和品质。由于涉及的土地面积很大，因此住房评估是一项繁琐的工作，需要大量的劳动力和很长的时间。新开发的自主作物侦察技术在绘制农田地图方面显示出了希望，而无需任何人工交互。通过将自主侦察和倒伏水稻检测与边缘计算相结合，可以更快地估计水稻倒伏，并且成本比以前的方法低得多。提出了一种适用于无人机的自适应作物侦察机制。我们使用深度神经网络和真实的无人机能量分布模拟了多个级别的稻田无人机作物侦察，重点关注高倒伏地区。使用所提出的方法，我们可以侦察稻田比传统的侦察方法快36%，准确率为99.25%。
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.