On the public citation graph
CropQuant: An automated and scalable field phenotyping platform for crop monitoring and trait measurements to facilitate breeding and digital agriculture
0 signed · 127 inferred · view as graph
Inferred edges come from two machine sources: sweeps of open repository metadata (mostly SSRN and Zenodo), and a bulk OpenAlex citation snapshot. Every row below is labelled with the one it came from. Neither is a complete citation index — absence here is not evidence of absence.
Is this your work? Sign in with ORCID and put your name on the
claims that are yours.
Reference health. See whether every reference in this work resolves to a
known work and none has been retracted — and get a dated CiteStamped
badge you can embed.
What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1007/s00122-009-1046-xopenalex-snapshot
Meta-QTL analysis of the genetic control of ear emergence in elite European winter wheat germplasm
inferredcites →https://doi.org/10.1007/s00122-013-2066-0openalex-snapshot
Next-generation phenotyping: requirements and strategies for enhancing our understanding of genotype–phenotype relationships and its relevance to crop improvement
inferredcites →https://doi.org/10.1007/s00138-015-0728-4openalex-snapshot
An opinion on imaging challenges in phenotyping field crops
inferredcites →https://doi.org/10.1007/s10681-007-9594-0openalex-snapshot
A multi-trait multi-environment QTL mixed model with an application to drought and nitrogen stress trials in maize (Zea mays L.)
inferredcites →https://doi.org/10.1007/s10994-009-5103-0openalex-snapshot
NP-hardness of Euclidean sum-of-squares clustering
inferredcites →https://doi.org/10.1016/j.compag.2016.01.017openalex-snapshot
Development of a field-based high-throughput mobile phenotyping platform
inferredcites →https://doi.org/10.1016/j.cviu.2006.11.012openalex-snapshot
Color image histogram equalization by absolute discounting back-off
inferredcites →https://doi.org/10.1016/j.eja.2011.05.001openalex-snapshot
Simulation of winter wheat yield and its variability in different climates of Europe: A comparison of eight crop growth models
inferredcites →https://doi.org/10.1016/j.eja.2015.07.004openalex-snapshot
Low-altitude, high-resolution aerial imaging systems for row and field crop phenotyping: A review
inferredcites →https://doi.org/10.1016/j.fcr.2012.04.003openalex-snapshot
Field-based phenomics for plant genetics research
inferredcites →https://doi.org/10.1016/j.fcr.2012.08.008openalex-snapshot
The use of satellite data for crop yield gap analysis
inferredcites →https://doi.org/10.1016/j.future.2013.01.010openalex-snapshot
Internet of Things (IoT): A vision, architectural elements, and future directions
inferredcites →https://doi.org/10.1016/j.pbi.2009.12.011openalex-snapshot
Molecular breeding in developing countries: challenges and perspectives
inferredcites →https://doi.org/10.1016/j.plantsci.2015.07.010openalex-snapshot
Next generation breeding
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/10.1016/s0168-1923(97)00027-0openalex-snapshot
Growing degree-days: one equation, two interpretations
inferredcites →https://doi.org/10.1017/cbo9781107415324.004openalex-snapshot
An Ichthyosaurus (Reptilia, Ichthyosauria) with gastric contents from Charmouth, England:first report of the genus from the Pliensbachian
inferredcites →https://doi.org/10.1023/a:1023030907417openalex-snapshot
Regularized Laplacian Zero Crossings as Optimal Edge Integrators
inferredcites →https://doi.org/10.1038/527s14aopenalex-snapshot
Deep phenotyping: The details of disease
inferredcites →https://doi.org/10.1038/nature11650openalex-snapshot
Analysis of the bread wheat genome using whole-genome shotgun sequencing
inferredcites →https://doi.org/10.1038/nature22011openalex-snapshot
Genomic innovation for crop improvement
inferredcites →https://doi.org/10.1038/ncomms5105openalex-snapshot
Mesoporous silicon sponge as an anti-pulverization structure for high-performance lithium-ion battery anodes
inferredcites →https://doi.org/10.1038/ncomms6087openalex-snapshot
Combining high-throughput phenotyping and genome-wide association studies to reveal natural genetic variation in rice
inferredcites →https://doi.org/10.1038/nmeth1012-1031aopenalex-snapshot
Erratum: Corrigendum: Biological imaging software tools
inferredcites →https://doi.org/10.1038/nplants.2015.193openalex-snapshot
Growing innovations for the bioeconomy
inferredcites →https://doi.org/10.1038/s41477-017-0083-8openalex-snapshot
Speed breeding is a powerful tool to accelerate crop research and breeding
inferredcites →https://doi.org/10.1071/fp13126openalex-snapshot
Development and evaluation of a field-based high-throughput phenotyping platform
inferredcites →https://doi.org/10.1071/fp16123openalex-snapshot
Comparison of ground cover estimates from experiment plots in cotton, sorghum and sugarcane based on images and ortho-mosaics captured by UAV
inferredcites →https://doi.org/10.1071/fp16163openalex-snapshot
Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring
inferredcites →https://doi.org/10.1071/fpv44n1_foopenalex-snapshot
Plant phenotyping: increasing throughput and precision at multiple scales
inferredcites →https://doi.org/10.1073/pnas.0701890104openalex-snapshot
Adapting agriculture to climate change
inferredcites →https://doi.org/10.1073/pnas.0912953109openalex-snapshot
Green Revolution: Impacts, limits, and the path ahead
inferredcites →https://doi.org/10.1093/jxb/erq135openalex-snapshot
Why work and discuss the basic principles of plant modelling 50 years after the first plant models?
inferredcites →https://doi.org/10.1093/jxb/erv251openalex-snapshot
LeasyScan: a novel concept combining 3D imaging and lysimetry for high-throughput phenotyping of traits controlling plant water budget
inferredcites →https://doi.org/10.1093/pcp/pci086openalex-snapshot
PSEUDO-RESPONSE REGULATORS, PRR9, PRR7 and PRR5, Together Play Essential Roles Close to the Circadian Clock of Arabidopsis thaliana
inferredcites →https://doi.org/10.1104/pp.108.118232openalex-snapshot
Molecular Plant Breeding as the Foundation for 21st Century Crop Improvement
inferredcites →https://doi.org/10.1105/tpc.114.129601openalex-snapshot
Dissecting the Phenotypic Components of Crop Plant Growth and Drought Responses Based on High-Throughput Image Analysis
inferredcites →https://doi.org/10.1109/34.868688openalex-snapshot
Normalized cuts and image segmentation
inferredcites →https://doi.org/10.1109/72.991427openalex-snapshot
A comparison of methods for multiclass support vector machines
inferredcites →https://doi.org/10.1109/jproc.2012.2196249openalex-snapshot
Using High-Resolution Airborne and Satellite Imagery to Assess Crop Growth and Yield Variability for Precision Agriculture
inferredcites →https://doi.org/10.1109/massp.1984.1162257openalex-snapshot
Gauss and the history of the fast fourier transform
inferredcites →https://doi.org/10.1109/proc.1984.13079openalex-snapshot
Editorial note on "Communication in the presence of noise"
inferredcites →https://doi.org/10.1109/tsmc.1973.4309314openalex-snapshot
Textural Features for Image Classification
inferredcites →https://doi.org/10.1109/tsmc.1979.4310076openalex-snapshot
A Threshold Selection Method from Gray-Level Histograms
inferredcites →https://doi.org/10.1111/j.1365-313x.2012.04971.xopenalex-snapshot
The impact of photoperiod insensitive Ppd‐1a mutations on the photoperiod pathway across the three genomes of hexaploid wheat (Triticum aestivum)
inferredcites →https://doi.org/10.1111/j.1365-3180.1974.tb01084.xopenalex-snapshot
A decimal code for the growth stages of cereals
inferredcites →https://doi.org/10.1111/j.1744-7348.1991.tb04895.xopenalex-snapshot
A uniform decimal code for growth stages of crops and weeds
inferredcites →https://doi.org/10.1111/nph.12192openalex-snapshot
Large‐scale characterization of drought pattern: a continent‐wide modelling approach applied to the Australian wheatbelt – spatial and temporal trends
inferredcites →https://doi.org/10.1111/nph.13266openalex-snapshot
Barley: a translational model for adaptation to climate change
inferredcites →https://doi.org/10.1111/nph.14053openalex-snapshot
The growth of vegetative and reproductive structures (leaves and silks) respond similarly to hydraulic cues in maize
inferredcites →https://doi.org/10.1117/1.1631315openalex-snapshot
Survey over image thresholding techniques and quantitative performance evaluation
inferredcites →https://doi.org/10.1126/science.1183700openalex-snapshot
Breeding Technologies to Increase Crop Production in a Changing World
inferredcites →https://doi.org/10.1146/annurev-arplant-050312-120137openalex-snapshot
Future Scenarios for Plant Phenotyping
inferredcites →https://doi.org/10.1186/s13007-015-0078-2openalex-snapshot
Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize
inferredcites →https://doi.org/10.1371/journal.pone.0079459openalex-snapshot
Mutant Alleles of Photoperiod-1 in Wheat (Triticum aestivum L.) That Confer a Late Flowering Phenotype in Long Days
inferredcites →https://doi.org/10.2307/2290746openalex-snapshot
Introduction to Linear Regression Analysis.
inferredcites →https://doi.org/10.2307/500917openalex-snapshot
Agricultural Origins and Dispersals. By Carl O. Sauer.
inferredcites →https://doi.org/10.3354/cr034071openalex-snapshot
Utility of dynamical seasonal forecasts in predicting crop yield
inferredcites →https://doi.org/10.3389/fphys.2012.00326openalex-snapshot
Bridging the phenotypic and genetic data useful for integrated breeding through a data annotation using the Crop Ontology developed by the crop communities of practice
inferredcites →https://doi.org/10.3390/agronomy4030349openalex-snapshot
Proximal Remote Sensing Buggies and Potential Applications for Field-Based Phenotyping
inferredcites →https://doi.org/10.48550/arxiv.1201.0490openalex-snapshot
Scikit-learn: Machine Learning in Python
inferredcites →https://doi.org/10.5281/zenodo.19712698openalex-snapshot
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
inferredcites →https://doi.org/10.5860/choice.48-5140openalex-snapshot
Computer vision: algorithms and applications
inferredcites →https://doi.org/10.7717/peerj.270openalex-snapshot
System wide analyses have underestimated protein abundances and the importance of transcription in mammals
inferredcites →https://doi.org/10.7717/peerj.453openalex-snapshot
scikit-image: image processing in Python
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
inferred← citeshttps://doi.org/10.1002/9781119312994.apr0651openalex-snapshot
Field Phenotyping for the Future
inferred← citeshttps://doi.org/10.1002/9781119711148.ch11openalex-snapshot
Current Trends and Future Scope for the Internet of Things
inferred← citeshttps://doi.org/10.1002/9781394192595.ch07openalex-snapshot
High‐throughput Phenotyping of Traits Associated with Climate Resilience in Crop Plants
inferred← citeshttps://doi.org/10.1002/9781394330485.ch4openalex-snapshot
Advancements in Phenotyping and High‐Throughput Imaging
inferred← citeshttps://doi.org/10.1007/978-3-030-20912-4_1openalex-snapshot
SpikeletFCN: Counting Spikelets from Infield Wheat Crop Images Using Fully Convolutional Networks
inferred← citeshttps://doi.org/10.1007/978-3-030-65414-6_23openalex-snapshot
Unsupervised Domain Adaptation for Plant Organ Counting
inferred← citeshttps://doi.org/10.1007/978-3-031-13276-6_3openalex-snapshot
Managing Data in Breeding, Selection and in Practice: A Hundred Year Problem That Requires a Rapid Solution
inferred← citeshttps://doi.org/10.1007/978-3-031-67450-1_5openalex-snapshot
Impact of Advanced Sensing Technologies in Agriculture with Soil, Crop, Climate and Farmland-Based Approaches Using Internet of Things
inferred← citeshttps://doi.org/10.1007/978-981-99-5890-0_14openalex-snapshot
Salinity Stress in Pearl Millet: From Physiological to Molecular Responses
inferred← citeshttps://doi.org/10.1007/s00521-022-07392-1openalex-snapshot
Counting spikelets from infield wheat crop images using fully convolutional networks
inferred← citeshttps://doi.org/10.1007/s10661-020-08712-5openalex-snapshot
Vegetation height estimation using ubiquitous foot-based wearable platform
inferred← citeshttps://doi.org/10.1007/s10681-026-03728-3openalex-snapshot
Phenotyping for physiological traits under plant stress: methods, challenges, and future perspectives
inferred← citeshttps://doi.org/10.1007/s11103-026-01710-0openalex-snapshot
Quantitative research from the perspective of mathematical and physical crop science: a review of phenotyping, mechanics, and modeling
inferred← citeshttps://doi.org/10.1007/s12892-025-00334-yopenalex-snapshot
High throughput phenotyping techniques in accelerated plant breeding
inferred← citeshttps://doi.org/10.1007/s43657-022-00048-zopenalex-snapshot
A Comprehensive Review of High Throughput Phenotyping and Machine Learning for Plant Stress Phenotyping
inferred← citeshttps://doi.org/10.1016/b978-0-323-85797-0.00002-1openalex-snapshot
Conventional, genomics, and post-genomics era of pulses breeding: Current status and future prospects
inferred← citeshttps://doi.org/10.1016/b978-0-443-21793-7.00023-4openalex-snapshot
Role of artificial intelligence and modern tools in rice crop production: The future of abiotic stress management
inferred← citeshttps://doi.org/10.1016/bs.agron.2020.02.002openalex-snapshot
Advanced analytics, phenomics and biotechnology approaches to enhance genetic gains in plant breeding
inferred← citeshttps://doi.org/10.1016/j.asoc.2022.109761openalex-snapshot
A survey on deep learning applications in wheat phenotyping
inferred← citeshttps://doi.org/10.1016/j.atech.2024.100516openalex-snapshot
Application of hyper-automation in farming – an analysis
inferred← citeshttps://doi.org/10.1016/j.biosystemseng.2021.09.015openalex-snapshot
Artificial intelligence approaches to predict growth, harvest day, and quality of lettuce (Lactuca sativa L.) in a IoT-enabled greenhouse system
inferred← citeshttps://doi.org/10.1016/j.cj.2021.03.015openalex-snapshot
High-throughput phenotyping: Breaking through the bottleneck in future crop breeding
inferred← citeshttps://doi.org/10.1016/j.compag.2020.105354openalex-snapshot
BerryIP embedded: An embedded vision system for strawberry crop
inferred← citeshttps://doi.org/10.1016/j.compag.2022.107439openalex-snapshot
Wheat spike localization and counting via hybrid UNet architectures
inferred← citeshttps://doi.org/10.1016/j.compag.2023.107623openalex-snapshot
AutoOLA: Automatic object level augmentation for wheat spikes counting
inferred← citeshttps://doi.org/10.1016/j.compag.2023.108014openalex-snapshot
Improved 3D point cloud segmentation for accurate phenotypic analysis of cabbage plants using deep learning and clustering algorithms
inferred← citeshttps://doi.org/10.1016/j.isprsjprs.2020.11.006openalex-snapshot
Lidar sheds new light on plant phenomics for plant breeding and management: Recent advances and future prospects
inferred← citeshttps://doi.org/10.1016/j.molp.2020.01.008openalex-snapshot
Crop Phenomics and High-Throughput Phenotyping: Past Decades, Current Challenges, and Future Perspectives
inferred← citeshttps://doi.org/10.1016/j.plantsci.2018.06.015openalex-snapshot
What is cost-efficient phenotyping? Optimizing costs for different scenarios
inferred← citeshttps://doi.org/10.1079/9781789247534.0018openalex-snapshot
Deep Learning in Plant Omics: Object Detection and Image Segmentation
inferred← citeshttps://doi.org/10.1079/9781800628649.0004openalex-snapshot
Artificial Intelligence in Plant Systems Biology
inferred← citeshttps://doi.org/10.1093/aob/mcaa097openalex-snapshot
Estimation of maize plant height and leaf area index dynamics using an unmanned aerial vehicle with oblique and nadir photography
inferred← citeshttps://doi.org/10.1093/gigascience/giz009openalex-snapshot
CropSight: a scalable and open-source information management system for distributed plant phenotyping and IoT-based crop management
inferred← citeshttps://doi.org/10.1093/jxb/erac242openalex-snapshot
High-throughput unmanned aerial vehicle-based phenotyping provides insights into the dynamic process and genetic basis of rapeseed waterlogging response in the field
inferred← citeshttps://doi.org/10.1093/plphys/kiab324openalex-snapshot
Large-scale field phenotyping using backpack LiDAR and CropQuant-3D to measure structural variation in wheat
inferred← citeshttps://doi.org/10.1109/access.2019.2932609openalex-snapshot
Internet-of-Things (IoT)-Based Smart Agriculture: Toward Making the Fields Talk
inferred← citeshttps://doi.org/10.1109/iotais56727.2022.9975929openalex-snapshot
Industrial Internet of Things (IoT) and 3D Reconstruction Empowered Smart Agriculture System
inferred← citeshttps://doi.org/10.1109/ist50367.2021.9651407openalex-snapshot
Wheat Spike Counting Using Regression and Localization Approaches
inferred← citeshttps://doi.org/10.1109/ojim.2022.3178468openalex-snapshot
A Review on Sensing Technologies for High-Throughput Plant Phenotyping
inferred← citeshttps://doi.org/10.1111/nph.16736openalex-snapshot
SeedGerm: a cost‐effective phenotyping platform for automated seed imaging and machine‐learning based phenotypic analysis of crop seed germination
inferred← citeshttps://doi.org/10.1186/s13007-017-0266-3openalex-snapshot
Leaf-GP: an open and automated software application for measuring growth phenotypes for arabidopsis and wheat
inferred← citeshttps://doi.org/10.22541/au.166758437.70063358/v1openalex-snapshot
Development of IoT-based camera system for automated in-field monitoring to support crop breeding Programs
inferred← citeshttps://doi.org/10.25195/ijci.v50i1.464openalex-snapshot
Smart Farming Platform Using IoT and UAVs
inferred← citeshttps://doi.org/10.29138/ijebd.v5i3.1818openalex-snapshot
Systematic Literature Review: An overview of Digital Agriculture for Food Sustainability
inferred← citeshttps://doi.org/10.31083/j.fbl2901020openalex-snapshot
Machine and Deep Learning: Artificial Intelligence Application in Biotic and Abiotic Stress Management in Plants
inferred← citeshttps://doi.org/10.3389/fbioe.2020.623705openalex-snapshot
High-Throughput Plant Phenotyping Platform (HT3P) as a Novel Tool for Estimating Agronomic Traits From the Lab to the Field
inferred← citeshttps://doi.org/10.3389/fpls.2023.1219983openalex-snapshot
CropQuant-Air: an AI-powered system to enable phenotypic analysis of yield- and performance-related traits using wheat canopy imagery collected by low-cost drones
inferred← citeshttps://doi.org/10.3390/agriculture12101745openalex-snapshot
Smart Farming: Internet of Things (IoT)-Based Sustainable Agriculture
inferred← citeshttps://doi.org/10.3390/agronomy13112832openalex-snapshot
Field Phenotyping Monitoring Systems for High-Throughput: A Survey of Enabling Technologies, Equipment, and Research Challenges
inferred← citeshttps://doi.org/10.3390/su13094883openalex-snapshot
Current Progress and Future Prospects of Agriculture Technology: Gateway to Sustainable Agriculture
inferred← citeshttps://doi.org/10.34133/plantphenomics.0245openalex-snapshot
Auto-LIA: The Automated Vision-Based Leaf Inclination Angle Measurement System Improves Monitoring of Plant Physiology
inferred← citeshttps://doi.org/10.34133/plantphenomics.0270openalex-snapshot
From Images to Loci: Applying 3D Deep Learning to Enable Multivariate and Multitemporal Digital Phenotyping and Mapping the Genetics Underlying Nitrogen Use Efficiency in Wheat
inferred← citeshttps://doi.org/10.4018/979-8-3693-1794-5.ch007openalex-snapshot
Internet of Things Framework for Sustainable Agriculture in Smart Farming
inferred← citeshttps://doi.org/10.5220/0006580403460355openalex-snapshot
Automatic Counting of Wheat Spikes from Wheat Growth Images
inferred← citeshttps://doi.org/10.5772/intechopen.104611openalex-snapshot
Strawberry Cultivation Techniques
Editorial review for independent work. ICSAC — the Institute for Complexity
Science and Advanced Computing — runs a structured editorial panel for researchers
publishing outside journals, and publishes on CiteStamp rails. Submission is free
and goes through ORCID: icsacinstitute.org/submit.