ReviewPlant phenomics (Washington, D.C.)2020
Convolutional Neural Networks for Image-Based High-Throughput Plant Phenotyping: A Review.
Review in Plant phenomics (Washington, D.C.), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 128 papers, 1 of them a synthesis that pooled it.
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
128 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Practices and Applications of Convolutional Neural Network-Based Computer Vision Systems in Animal Farming: A Review.Sensors (Basel, Switzerland) · 2021Pooled it
- SAM-CLIP-Thermal: Leveraging large multimodal models for reliable and scalable annotation in thermal image segmentation for field plant phenotyping.Plant phenomics (Washington, D.C.) · 2026Article
- RootQuantV2: Adapting a Vision Foundation Model for Root-Trait Regression from Minirhizotron Imagery.ArXiv · 2026Article
- Advanced Remote Sensing and AI Techniques in Agriculture and Forestry.Plants (Basel, Switzerland) · 2026Article
- Thermal image segmentation in weedy fields via synthetic RGB-trained models and GAN-based cross-modality alignment.Plant phenomics (Washington, D.C.) · 2026Article
- From phenoscope to GreenLab model of Arabidopsis to decipher genotype and treatment effects.Plant phenomics (Washington, D.C.) · 2026Article
- Computer Vision from Tea Cultivation to Quality Evaluation.Foods (Basel, Switzerland) · 2026Review
- Rapid Classification and Deep Learning-Based Development Estimation of the Seeds ofPlants (Basel, Switzerland) · 2026Article
- Application of deep learning in crop research: From genomics to phenomics.The plant genome · 2026Review
- Advancements in 3D Reconstruction for Plant Phenotyping: Technologies, Applications, Challenges, and Future Directions.Sensors (Basel, Switzerland) · 2026Review
- Decoding plant volatile stress signals across scales: from molecular responses to ecosystem dynamics.Frontiers in plant science · 2026Review
- A crop cultivation monitoring platform for evaluating the early growth of cucumbers.Frontiers in plant science · 2026Article
- LBD-PointNet++: a point cloud segmentation network for phenotypic trait extraction of broccoli seedlings.Frontiers in plant science · 2026Article
- Multi-scale feature fusion-based vision mamba for robust plant disease image classification on field-acquired plantdoc data.Frontiers in plant science · 2026Article
- Deep learning-based high-throughput phenotyping for tiller quantification in interspecific bentgrass hybrids using YOLOv8.Frontiers in plant science · 2026Article
- Chili-Seg: a high-precision detection and instance segmentation model for chili flowers in complex environments.Frontiers in plant science · 2026Article
- Perceptual graph kernels for image-derived plant trait interaction analysis in precision agriculture.Frontiers in plant science · 2026Article
- Aerial imagery and Segment Anything Model for architectural trait phenotyping to support genetic analysis in peanut breeding.Plant phenomics (Washington, D.C.) · 2025Article
- Deep learning for three-dimensional (3D) plant phenomics.Plant phenomics (Washington, D.C.) · 2025Review
- Deep learning in plant phenotyping: the first ten years.Plant phenomics (Washington, D.C.) · 2025Review
68 more citing papers are in PubMed but not listed here.
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
Plant phenotyping has been recognized as a bottleneck for improving the efficiency of breeding programs, understanding plant-environment interactions, and managing agricultural systems. In the past five years, imaging approaches have shown great potential for high-throughput plant phenotyping, resulting in more attention paid to imaging-based plant phenotyping. With this increased amount of image data, it has become urgent to develop robust analytical tools that can extract phenotypic traits accurately and rapidly. The goal of this review is to provide a comprehensive overview of the latest studies using deep convolutional neural networks (CNNs) in plant phenotyping applications. We specifically review the use of various CNN architecture for plant stress evaluation, plant development, and postharvest quality assessment. We systematically organize the studies based on technical developments resulting from imaging classification, object detection, and image segmentation, thereby identifying state-of-the-art solutions for certain phenotyping applications. Finally, we provide several directions for future research in the use of CNN architecture for plant phenotyping purposes.
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.