ReviewHorticulture research2021
Applications of deep-learning approaches in horticultural research: a review.
Review in Horticulture research, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed.
- From linear models to deep learning: statistical advances in genomic selection for animal breeding.Journal of animal science and biotechnology · 2026Review
- Multidimensional Perspective on the Quality of Green Onion: Between the Visible, the Measurable, and the Perceived.Food science & nutrition · 2026Article
- Physiological and image-based characterization ofFrontiers in plant science · 2026Article
- OrchardQuant-3D: combining drone and LiDAR to perform scalable 3D phenotyping for characterising key canopy and floral traits in fruit orchards.Plant biotechnology journal · 2025Article
- Advanced technologies in plant factories: exploring current and future economic and environmental benefits in urban horticulture.Horticulture research · 2025Article
- A Comprehensive Review of Deep Learning in Computer Vision for Monitoring Apple Tree Growth and Fruit Production.Sensors (Basel, Switzerland) · 2025Review
- A high-throughput ResNet CNN approach for automated grapevine leaf hair quantification.Scientific reports · 2025Article
- Multimodal cross-attention network for overgrowth detection in strawberry seedlings.Frontiers in plant science · 2025Article
- Utilising artificial intelligence for cultivating decorative plants.Botanical studies · 2024Article
- Continuous Growth Monitoring and Prediction with 1D Convolutional Neural Network Using Generated Data with Vision Transformer.Plants (Basel, Switzerland) · 2024Article
- Recognition and Localization of Maize Leaf and Stalk Trajectories in RGB Images Based on Point-Line Net.Plant phenomics (Washington, D.C.) · 2024Article
- A CNN-LSTM-att hybrid model for classification and evaluation of growth status under drought and heat stress in chinese fir (Cunninghamia lanceolata).Plant methods · 2023Article
- Detecting stress caused by nitrogen deficit using deep learning techniques applied on plant electrophysiological data.Scientific reports · 2023Article
- Advancement of non-destructive spectral measurements for the quality of major tropical fruits and vegetables: a review.Frontiers in plant science · 2023Review
- Attention-aided lightweight networks friendly to smart weeding robot hardware resources for crops and weeds semantic segmentation.Frontiers in plant science · 2023Article
- Process-Based Crop Modeling for High Applicability with Attention Mechanism and Multitask Decoders.Plant phenomics (Washington, D.C.) · 2023Article
- Non-Destructive Monitoring of Crop Fresh Weight and Leaf Area with a Simple Formula and a Convolutional Neural Network.Sensors (Basel, Switzerland) · 2022Article
- Quantitative phenotyping and evaluation for lettuce leaves of multiple semantic components.Plant methods · 2022Article
- High-throughput image-based plant stand count estimation using convolutional neural networks.PloS one · 2022Article
- Automatic instance segmentation of orchard canopy in unmanned aerial vehicle imagery using deep learning.Frontiers in plant science · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.