ReviewGigaScience2019
Computer vision-based phenotyping for improvement of plant productivity: a machine learning perspective.
Review in GigaScience, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 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
61 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Advancing crop improvement through GWAS and beyond in mung bean.Frontiers in plant science · 2024Pooled it
- Rapid Classification and Deep Learning-Based Development Estimation of the Seeds ofPlants (Basel, Switzerland) · 2026Article
- Microalgae-Driven Circular Agriculture: System Integration, Nutrient Recovery, and AI-Assisted Optimization.Microorganisms · 2026Review
- Phenotypic feature-based identification of tea geographical origin using lightweight deep learning.NPJ science of food · 2026Article
- Flora-YOLO: A lightweight framework for real-time rose grading on edge devices via structural optimization and feature refinement.PloS one · 2026Article
- Perceptual graph kernels for image-derived plant trait interaction analysis in precision agriculture.Frontiers in plant science · 2026Article
- AI-integrated digital breeding for crop improvement.Frontiers in plant science · 2026Review
- Integrating Drought Stress Signaling and Smart Breeding for Climate-Resilient Crops: Regulatory Mechanisms and Genetic Strategies.Plants (Basel, Switzerland) · 2025Review
- APTES: a high-throughput deep learning-based Arabidopsis phenotypic trait estimation system for individual leaves and siliques.aBIOTECH · 2025Article
- Dual-Phase Severity Grading of Strawberry Angular Leaf Spot Based on Improved YOLOv11 and OpenCV.Plants (Basel, Switzerland) · 2025Article
- Penalized factorial regression as a flexible and computationally attractive reaction norm model for prediction in the presence of GxE.TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik · 2025Article
- Identification of maize kernel varieties based on interpretable ensemble algorithms.Frontiers in plant science · 2025Article
- MobileNet-GDR: a lightweight algorithm for grape leaf disease identification based on improved MobileNetV4-small.Frontiers in plant science · 2025Article
- FHB-Net: a severity level evaluation model for wheatFrontiers in plant science · 2025Article
- Prediction of Salicornia europaea L. biomass using a computer vision system to distinguish different salt-tolerant populations.BMC plant biology · 2024Article
- High-Precision Automated Soybean Phenotypic Feature Extraction Based on Deep Learning and Computer Vision.Plants (Basel, Switzerland) · 2024Article
- GRABSEEDS: extraction of plant organ traits through image analysis.Plant methods · 2024Article
- Integrating genomics, phenomics, and deep learning improves the predictive ability for Fusarium head blight-related traits in winter wheat.The plant genome · 2024Article
- Development of an Automated Low-Cost Multispectral Imaging System to Quantify Canopy Size and Pigmentation.Sensors (Basel, Switzerland) · 2024Article
- Machine Learning-Based Tomato Fruit Shape Classification System.Plants (Basel, Switzerland) · 2024Article
1 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
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Employing computer vision to extract useful information from images and videos is becoming a key technique for identifying phenotypic changes in plants. Here, we review the emerging aspects of computer vision for automated plant phenotyping. Recent advances in image analysis empowered by machine learning-based techniques, including convolutional neural network-based modeling, have expanded their application to assist high-throughput plant phenotyping. Combinatorial use of multiple sensors to acquire various spectra has allowed us to noninvasively obtain a series of datasets, including those related to the development and physiological responses of plants throughout their life. Automated phenotyping platforms accelerate the elucidation of gene functions associated with traits in model plants under controlled conditions. Remote sensing techniques with image collection platforms, such as unmanned vehicles and tractors, are also emerging for large-scale field phenotyping for crop breeding and precision agriculture. Computer vision-based phenotyping will play significant roles in both the nowcasting and forecasting of plant traits through modeling of genotype/phenotype relationships.
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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.