Applications of deep-learning approaches in horticultural research: a review

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.

Automated summary

Deep learning is a promising tool for processing images and big data, providing reliable prediction results for complex phenomena. It has been increasingly used in horticultural research for variety recognition, yield estimation, quality detection, stress phenotyping detection, growth monitoring, and more. This review aims to help researchers understand the strengths and weaknesses of applying deep learning in horticultural research and encourages exploration of its significant examples for the advancement of intelligent horticulture.