ReviewiScience2024
Computer vision-based plants phenotyping: A comprehensive survey.
Review in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 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
12 citing papers in PubMed.
- ZCAT: Zero-shot cross-crop annotation transfer-A new paradigm leveraging plant organ similarity.Plant phenomics (Washington, D.C.) · 2026Article
- FG-LCNet: A two-stage foreground-guided network for whole-tree litchi counting.Plant phenomics (Washington, D.C.) · 2026Article
- Article
- Digitalised phenotyping of pepper (Capsicum spp.) using effective RGB imaging and optimised camera positioning.Scientific reports · 2026Article
- Identification of genetic factors regulating anther size in wheat under heat stress environment.BMC plant biology · 2026Article
- Soy-RepViT: a lightweight deep learning framework for fine-grained recognition of soybean growth habits.Frontiers in plant science · 2026Article
- Predicting wheat yield and grain quality with UAV multispectral imagery and deep learning.Frontiers in plant science · 2026Article
- Advancing plant leaf disease detection integrating machine learning and deep learning.Scientific reports · 2025Article
- Comparison of YOLO-based sorghum spike identification detection models and monitoring at the flowering stage.Plant methods · 2025Article
- Unlocking regeneration potential: harnessing morphogenic regulators and small peptides for enhanced plant engineering.The Plant journal : for cell and molecular biology · 2025Review
- Prediction of Salicornia europaea L. biomass using a computer vision system to distinguish different salt-tolerant populations.BMC plant biology · 2024Article
- Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight-related traits in winter wheat.The plant genome · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increasing demand for food production due to the growing population is raising the need for more food-productive environments for plants. The genetic behavior of plant traits remains different in different growing environments. However, it is tedious and impossible to look after the individual plant component traits manually. Plant breeders need computer vision-based plant monitoring systems to analyze different plants' productivity and environmental suitability. It leads to performing feasible quantitative analysis, geometric analysis, and yield rate analysis of the plants. Many of the data collection methods have been used by plant breeders according to their needs. In the presented review, most of them are discussed with their corresponding challenges and limitations. Furthermore, the traditional approaches of segmentation and classification of plant phenotyping are also discussed. The data limitation problems and their currently adapted solutions in the computer vision aspect are highlighted, which somehow solve the problem but are not genuine. The available datasets and current issues are enlightened. The presented study covers the plants phenotyping problems, suggested solutions, and current challenges from data collection to classification steps.
Indexed as
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.