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Simulated Plant Images Improve Maize Leaf Counting Accuracy

https://doi.org/10.1101/706994
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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1007/s00138-015-0737-3openalex-snapshot
Leaf segmentation in plant phenotyping: a collation study
inferredcites →https://doi.org/10.1007/s11104-018-3595-8openalex-snapshot
Measuring root system traits of wheat in 2D images to parameterize 3D root architecture models
inferredcites →https://doi.org/10.1016/j.compag.2016.07.028openalex-snapshot
Temporal dynamics of maize plant growth, water use, and leaf water content using automated high throughput RGB and hyperspectral imaging
inferredcites →https://doi.org/10.1016/j.molp.2015.06.005openalex-snapshot
A Versatile Phenotyping System and Analytics Platform Reveals Diverse Temporal Responses to Water Availability in Setaria
inferredcites →https://doi.org/10.1016/j.patrec.2015.10.013openalex-snapshot
Finely-grained annotated datasets for image-based plant phenotyping
inferredcites →https://doi.org/10.1016/j.tplants.2011.09.005openalex-snapshot
Phenomics – technologies to relieve the phenotyping bottleneck
inferredcites →https://doi.org/10.1016/j.tplants.2013.09.008openalex-snapshot
Field high-throughput phenotyping: the new crop breeding frontier
inferredcites →https://doi.org/10.1038/nrg2897openalex-snapshot
Phenomics: the next challenge
inferredcites →https://doi.org/10.1071/fp09095openalex-snapshot
Simultaneous phenotyping of leaf growth and chlorophyll fluorescence via GROWSCREEN FLUORO allows detection of stress tolerance in Arabidopsis thaliana and other rosette plants
inferredcites →https://doi.org/10.1093/aob/mci251openalex-snapshot
Co-ordination between Leaf Initiation and Leaf Appearance in Field-grown Maize (Zea mays): Genotypic Differences in Response of Rates to Temperature
inferredcites →https://doi.org/10.1093/gigascience/gix083openalex-snapshot
Deep machine learning provides state-of-the-art performance in image-based plant phenotyping
inferredcites →https://doi.org/10.1093/gigascience/gix117openalex-snapshot
Conventional and hyperspectral time-series imaging of maize lines widely used in field trials
inferredcites →https://doi.org/10.1104/pp.112.202762openalex-snapshot
Rosette Tracker: An Open Source Image Analysis Tool for Automatic Quantification of Genotype Effects
inferredcites →https://doi.org/10.1104/pp.17.00707openalex-snapshot
A High-Throughput, Field-Based Phenotyping Technology for Tall Biomass Crops
inferredcites →https://doi.org/10.1109/iccvw.2017.243openalex-snapshot
Leveraging Multiple Datasets for Deep Leaf Counting
inferredcites →https://doi.org/10.1109/iccvw.2017.244openalex-snapshot
Leaf Counting with Deep Convolutional and Deconvolutional Networks
inferredcites →https://doi.org/10.1109/igarss.2018.8519034openalex-snapshot
Sorghum Biomass Prediction Using Uav-Based Remote Sensing Data and Crop Model Simulation
inferredcites →https://doi.org/10.1111/cgf.12282openalex-snapshot
Inverse Procedural Modelling of Trees
inferredcites →https://doi.org/10.1111/j.1365-313x.2005.02591.xopenalex-snapshot
Maize association population: a high‐resolution platform for quantitative trait locus dissection
inferredcites →https://doi.org/10.1111/j.1469-8137.2009.02884.xopenalex-snapshot
A comparative view of the evolution of grasses under domestication
inferredcites →https://doi.org/10.1145/2661229.2661252openalex-snapshot
Windy trees
inferredcites →https://doi.org/10.1186/s13007-017-0246-7openalex-snapshot
A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform
inferredcites →https://doi.org/10.1186/s13007-018-0273-zopenalex-snapshot
The use of plant models in deep learning: an application to leaf counting in rosette plants
inferredcites →https://doi.org/10.1186/s13007-018-0278-7openalex-snapshot
Citizen crowds and experts: observer variability in image-based plant phenotyping
inferredcites →https://doi.org/10.1371/journal.pcbi.1006337openalex-snapshot
Crowdsourcing image analysis for plant phenomics to generate ground truth data for machine learning
inferredcites →https://doi.org/10.1371/journal.pone.0130855openalex-snapshot
Integrating Crop Growth Models with Whole Genome Prediction through Approximate Bayesian Computation
inferredcites →https://doi.org/10.2134/agronj1990.00021962008200020033xopenalex-snapshot
Temperature and Solar Radiation Effects on Potential Maize Yield across Locations
inferredcites →https://doi.org/10.2307/20042685openalex-snapshot
The Next Challenge
inferredcites →https://doi.org/10.25739/f12d-tt60openalex-snapshot
Real and simulated maize images for leaf counting study
inferredcites →https://doi.org/10.3389/fpls.2016.01419openalex-snapshot
Using Deep Learning for Image-Based Plant Disease Detection
inferredcites →https://doi.org/10.3389/fpls.2017.00786openalex-snapshot
Crops In Silico: Generating Virtual Crops Using an Integrative and Multi-scale Modeling Platform
inferredcites →https://doi.org/10.3389/fpls.2017.01190openalex-snapshot
Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks
inferredcites →https://doi.org/10.3390/s17040905openalex-snapshot
Deep Count: Fruit Counting Based on Deep Simulated Learning
inferredcites →https://doi.org/10.3847/1538-4357/aabfedopenalex-snapshot
Deep Learning Identifies High-z Galaxies in a Central Blue Nugget Phase in a Characteristic Mass Range
inferredcites →https://doi.org/10.48550/arxiv.1807.10931openalex-snapshot
Deep Leaf Segmentation Using Synthetic Data
inferredcites →https://doi.org/10.5220/0006196204790486openalex-snapshot
Comparing Local Descriptors and Bags of Visual Words to Deep Convolutional Neural Networks for Plant Recognition
inferredcites →https://doi.org/10.5244/c.29.cvppp.1openalex-snapshot
Learning to Count Leaves in Rosette Plants
inferredcites →https://doi.org/10.7717/peerj.4088openalex-snapshot
PlantCV v2: Image analysis software for high-throughput plant phenotyping

What points at this work

Incoming claims: where this work appears in other papers’ reference lists and arguments.

inferred← citeshttps://doi.org/10.1002/pld3.255openalex-snapshot
Voxel carving‐based 3D reconstruction of sorghum identifies genetic determinants of light interception efficiency
inferred← citeshttps://doi.org/10.1002/ppj2.20022openalex-snapshot
Automation of leaf counting in maize and sorghum using deep learning
inferred← citeshttps://doi.org/10.1007/978-3-031-47969-4_42openalex-snapshot
Synthetically Labeled Images for Maize Plant Detection in UAS Images
inferred← citeshttps://doi.org/10.1021/acsestengg.1c00269openalex-snapshot
Dynamically Controlled Environment Agriculture: Integrating Machine Learning and Mechanistic and Physiological Models for Sustainable Food Cultivation
inferred← citeshttps://doi.org/10.1038/s41524-025-01756-6openalex-snapshot
Accelerating domain-aware electron microscopy analysis using deep learning models with synthetic data and image-wide confidence scoring
inferred← citeshttps://doi.org/10.1042/etls20200273openalex-snapshot
High-throughput image segmentation and machine learning approaches in the plant sciences across multiple scales
inferred← citeshttps://doi.org/10.1093/insilicoplants/diab039openalex-snapshot
L-system models for image-based phenomics: case studies of maize and canola
inferred← citeshttps://doi.org/10.1101/2020.09.09.289769openalex-snapshot
Automatic Traits Extraction and Fitting for Field High-throughput Phenotyping Systems
inferred← citeshttps://doi.org/10.1117/12.2644452openalex-snapshot
Rapid identification of mature Xanthoceras sorbifolium bunge
inferred← citeshttps://doi.org/10.3390/s24072172openalex-snapshot
Image Filtering to Improve Maize Tassel Detection Accuracy Using Machine Learning Algorithms
inferred← citeshttps://doi.org/10.34133/2020/4216373openalex-snapshot
Semantic Segmentation of Sorghum Using Hyperspectral Data Identifies Genetic Associations
inferred← citeshttps://doi.org/10.34133/2020/7481687openalex-snapshot
A High-Throughput Phenotyping Pipeline for Image Processing and Functional Growth Curve Analysis
inferred← citeshttps://doi.org/10.34133/2021/9805489openalex-snapshot
KAT4IA: K -Means Assisted Training for Image Analysis of Field-Grown Plant Phenotypes
inferred← citeshttps://doi.org/10.3934/bdia.2021003openalex-snapshot
Phenotypic trait extraction of soybean plants using deep convolutional neural networks with transfer learning
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