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...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.展开更多
基金the Fuzhou Science and Technology Major Project,grant number 2018NZ0002-2the Science and Technology Innovation Special Fund Project of Fujian Agricultural and Forestry University,grant number CXZX2018032.
文摘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.
