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.

Noninvasive Abiotic Stress Phenotyping of Vascular Plant in Each Vegetative Organ View

The last decades have witnessed a rapid development of noninvasive plant phenotyping,capable of detecting plant stress scale levels from the subcellular to the whole population scale.However,even with such a broad range,most phenotyping objects are often just concerned with leaves.This review offers a unique perspective of noninvasive plant stress phenotyping from a multi-organ view.First,plant sensing and responding to abiotic stress from the diverse vegetative organs(leaves,stems,and roots)and the interplays between these vital components are analyzed.Then,the corresponding noninvasive optical phenotyping techniques are also provided,which can prompt the practical implementation of appropriate noninvasive phenotyping techniques for each organ.Furthermore,we explore methods for analyzing compound stress situations,as field conditions frequently encompass multiple abiotic stressors.Thus,our work goes beyond the conventional approach of focusing solely on individual plant organs.The novel insights of the multi-organ,noninvasive phenotyping study provide a reference for testing hypotheses concerning the intricate dynamics of plant stress responses,as well as the potential interactive effects among various stressors.