National Innovation Center for Digital Fishery, China Agricultural University, Beijing 100083, China.
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China.
Sensors (Basel). 2020 Mar 10;20(5):1520. doi: 10.3390/s20051520.
Deep Learning (DL) is the state-of-the-art machine learning technology, which shows superior performance in computer vision, bioinformatics, natural language processing, and other areas. Especially as a modern image processing technology, DL has been successfully applied in various tasks, such as object detection, semantic segmentation, and scene analysis. However, with the increase of dense scenes in reality, due to severe occlusions, and small size of objects, the analysis of dense scenes becomes particularly challenging. To overcome these problems, DL recently has been increasingly applied to dense scenes and has begun to be used in dense agricultural scenes. The purpose of this review is to explore the applications of DL for dense scenes analysis in agriculture. In order to better elaborate the topic, we first describe the types of dense scenes in agriculture, as well as the challenges. Next, we introduce various popular deep neural networks used in these dense scenes. Then, the applications of these structures in various agricultural tasks are comprehensively introduced in this review, including recognition and classification, detection, counting and yield estimation. Finally, the surveyed DL applications, limitations and the future work for analysis of dense images in agriculture are summarized.
深度学习（DL）是一种最先进的机器学习技术，在计算机视觉、生物信息学、自然语言处理等领域表现出优异的性能。特别是作为一种现代图像处理技术，DL 已经成功地应用于各种任务，如目标检测、语义分割和场景分析。然而，随着现实中密集场景的增加，由于严重的遮挡和物体的小尺寸，密集场景的分析变得特别具有挑战性。为了克服这些问题，DL 最近越来越多地应用于密集场景，并开始应用于密集的农业场景。本综述的目的是探讨 DL 在农业密集场景分析中的应用。为了更好地阐述这一主题，我们首先描述了农业中的密集场景类型以及所面临的挑战。接下来，我们介绍了在这些密集场景中使用的各种流行的深度神经网络。然后，在本综述中全面介绍了这些结构在各种农业任务中的应用，包括识别和分类、检测、计数和产量估计。最后，总结了调查的 DL 应用、局限性以及未来在农业密集图像分析方面的工作。
