In the wake of significant advances in agronomy, biology, informatics, agricultural robots (Agri-robots), and artificial intelligence, modern agriculture is transforming from labor-intensive to data-driven mode. Precision agriculture (PA) is one of the most practical solutions for bridging the crop yield gap by performing the right treatments in the right place and at the right time. As a rising star among Agri-robots, unmanned aerial vehicles (UAVs) equipped with high-resolution onboard sensors and dedicated application systems are playing an increasingly vital role in collecting multi-scale agricultural information and implementing site-specific treatment. In this process, a large number of images are produced. However, considerable effort is required to extract high-value information from the explosively growing number of images. Over the past decade, deep learning (DL) has demonstrated unparalleled advantages in agricultural analytics, such as crop/weed classification, biotic/abiotic stress detection, crop growth monitoring, yield prediction, natural disaster assessment, etc. The combination of UAVs and DL is of great significance for agricultural information acquisition, processing, analysis, decision-making, and deployment. With the rapid development of UAVs, DL, and PA, this work firstly introduces the key components of PA, UAVs, and DL, respectively, and summarizes their major research progress. Subsequently, we focus on the successful applications of UAVs and DL in PA. Furthermore, based on our extensive literature survey, their current challenges and future development trends are sorted out. Ultimately, we hope this survey can draw more attention to the novel applications of UAVs and DL in PA among multidisciplinary scientists around the world and inspire more exciting and practical studies.

中文翻译：

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无人机和深度学习在精准农业中的应用综述


随着农学、生物学、信息学、农业机器人 （Agri-robots） 和人工智能的重大进步，现代农业正在从劳动密集型模式转变为数据驱动模式。精准农业 （PA） 是通过在正确的地点和正确的时间进行正确的处理来缩小作物产量差距的最实用解决方案之一。作为农业机器人中的后起之秀，配备高分辨率机载传感器和专用应用系统的无人机 （UAV） 在收集多尺度农业信息和实施特定地点处理方面发挥着越来越重要的作用。在此过程中，会产生大量图像。但是，需要付出相当大的努力才能从爆炸式增长的图像中提取高价值信息。在过去十年中，深度学习 （DL） 在农业分析方面展示了无与伦比的优势，例如作物/杂草分类、生物/非生物胁迫检测、作物生长监测、产量预测、自然灾害评估等。无人机与深度学习的结合对于农业信息的获取、处理、分析、决策和部署具有重要意义。随着 UAV、DL 和 PA 的快速发展，本文首先分别介绍了 PA、UAV 和 DL 的关键组成部分，并总结了它们的主要研究进展。随后，我们专注于无人机和 DL 在 PA 中的成功应用。此外，基于我们广泛的文献调查，梳理了他们当前面临的挑战和未来的发展趋势。 最终，我们希望这项调查能够引起全球多学科科学家对无人机和 DL 在 PA 中的新应用的更多关注，并激发更多令人兴奋和实用的研究。