Yang Biyun, Xu Yong
College of Mechanical and Electronic Engineering, Fujian Agriculture and Forestry University, 350002, Fuzhou, China.
Institute of Machine Learning and Intelligent Science, Fujian University of Technology, 33 Xuefu South Road, 350118, Fuzhou, China.
Hortic Res. 2021 Jun 1;8(1):123. doi: 10.1038/s41438-021-00560-9.
Deep learning is known as a promising multifunctional tool for processing images and other big data. By assimilating large amounts of heterogeneous data, deep-learning technology provides reliable prediction results for complex and uncertain phenomena. Recently, it has been increasingly used by horticultural researchers to make sense of the large datasets produced during planting and postharvest processes. In this paper, we provided a brief introduction to deep-learning approaches and reviewed 71 recent research works in which deep-learning technologies were applied in the horticultural domain for variety recognition, yield estimation, quality detection, stress phenotyping detection, growth monitoring, and other tasks. We described in detail the application scenarios reported in the relevant literature, along with the applied models and frameworks, the used data, and the overall performance results. Finally, we discussed the current challenges and future trends of deep learning in horticultural research. The aim of this review is to assist researchers and provide guidance for them to fully understand the strengths and possible weaknesses when applying deep learning in horticultural sectors. We also hope that this review will encourage researchers to explore some significant examples of deep learning in horticultural science and will promote the advancement of intelligent horticulture.
深度学习是一种用于处理图像和其他大数据的很有前景的多功能工具。通过吸收大量异构数据，深度学习技术为复杂和不确定的现象提供可靠的预测结果。最近，园艺研究人员越来越多地使用它来理解种植和收获后过程中产生的大量数据集。在本文中，我们简要介绍了深度学习方法，并回顾了71项近期的研究工作，这些研究将深度学习技术应用于园艺领域，用于品种识别、产量估计、质量检测、胁迫表型检测、生长监测和其他任务。我们详细描述了相关文献中报道的应用场景，以及应用的模型和框架、使用的数据和整体性能结果。最后，我们讨论了深度学习在园艺研究中的当前挑战和未来趋势。这篇综述的目的是帮助研究人员，为他们在园艺领域应用深度学习时充分理解其优势和可能存在的弱点提供指导。我们还希望这篇综述能鼓励研究人员探索深度学习在园艺科学中的一些重要实例，并推动智能园艺的发展。
