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References from Plant counting: origins, development, and future paths. Local targets link to admitted publications; unresolved targets remain external evidence.
Plant phenotyping trinity: health checks for plants
2025 · External reference
NIH image to ImageJ: 25 years of image analysis
10.1038/nmeth.2089 · 2012 · External reference
CellProfiler: image analysis software for identifying and quantifying cell phenotypes
10.1186/gb-2006-7-10-r100 · 2006 · External reference
Learning to count objects in images
2010 · External reference
Single-image crowd counting via multi-column convolutional neural network
2016 · External reference
Deep-learning-based counting methods, datasets, and applications in agriculture: a review
10.1007/s11119-023-10034-8 · 2023 · External reference
High-throughput phenotyping of corn stand counts using a ground-based Mobile robot
2020 · External reference
Learning to count leaves in rosette plants
2015 · External reference
Rice tiller counting using a global-local approach on an interactively cognitive phenotyping robot
2022 · External reference
Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high-resolution RGB-labelled images to develop and benchmark wheat head detection methods
10.34133/2020/3521852 · 2020 · External reference
Ear density estimation from high resolution RGB imagery using deep learning technique
10.1016/j.agrformet.2018.10.013 · 2019 · External reference
Soybean pod counting based on YOLOX and attention mechanism
2023 · External reference
Fruit detection, segmentation and 3D localisation using the Fuji-SfM dataset
2020 · External reference
Deep machine learning provides state-of-the-art performance in image-based plant phenotyping
10.1093/gigascience/gix083 · 2017 · External reference
Deepcorn: a semi-supervised deep learning method for high-throughput image-based corn kernel counting and yield estimation
10.1016/j.knosys.2021.106874 · 2021 · External reference
Automatic kernel counting on maize ear using RGB images and morphological image processing
2020 · External reference
Automated stomata counting using density estimation
2023 · External reference
Automated tassel detection and anthesis scoring in maize fields using time-lapse imagery
2021 · External reference
Intelligent fruit yield estimation for orchards using deep learning based semantic segmentation techniques—a review
10.3389/fpls.2021.684328 · 2021 · External reference
Cross-scene crowd counting via deep convolutional neural networks
2015 · External reference
TasselNet: counting maize tassels in the wild via local counts regression network
10.1186/s13007-017-0224-0 · 2017 · External reference
Toward improved image-based root phenotyping: handling temporal and cross-site domain shifts in crop root segmentation models
2024 · External reference
Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high-resolution RGB-labelled images to develop and benchmark wheat head detection methods
2021 · External reference
MinneApple: a benchmark dataset for apple detection and segmentation
10.1109/lra.2020.2965061 · 2020 · External reference
A survey of deep learning-based object detection methods in crop counting
10.1016/j.compag.2023.108425 · 2023 · External reference
U-net: convolutional networks for biomedical image segmentation
2015 · External reference
You only look once: unified, real-time object detection
2016 · External reference
Bayesian loss for crowd count estimation with point supervision
2019 · External reference
Rethinking counting and localization in crowds: a purely point-based framework
2021 · External reference
Class-agnostic counting
2018 · External reference
Represent, compare, and learn: a similarity-aware framework for class-agnostic counting
2022 · External reference
Scale-prior deformable convolution for exemplar-guided class-agnostic counting
2022 · External reference
TasselNetV4: a vision foundation model for cross-scene, cross-scale, and cross-species plant counting
2025 · External reference
FoMo4Wheat: toward reliable crop vision foundation models with globally curated data
2025 · External reference
Rethinking the need for field trials
2025 · External reference
Development of methods to improve soybean yield estimation and predict plant maturity with an unmanned aerial vehicle based platform
10.1016/j.rse.2016.10.005 · 2016 · External reference
Study on the counting sensor for threshing control
2002 · External reference
Two fruit counting techniques for citrus mechanical harvesting machinery
10.1016/j.compag.2008.09.002 · 2009 · External reference
Sensor ranging technique for determining corn plant population
2008 · External reference
SmartGrain: high-throughput phenotyping software for measuring seed shape through image analysis
10.1104/pp.112.205120 · 2012 · External reference
Machine learning: trends, perspectives, and prospects
10.1126/science.aaa8415 · 2015 · External reference
Utilizing machine learning approaches to improve the prediction of leaf counts and individual leaf segmentation of rosette plant images
2015 · External reference
Aerial imagery analysis–quantifying appearance and number of sorghum heads for applications in breeding and agronomy
10.3389/fpls.2018.01544 · 2018 · External reference
Computer vision problems in plant phenotyping: CVPPP 2017—Introduction to the CVPPP 2017 workshop papers
2017 · External reference
Recurrent instance segmentation
2016 · External reference
Leaf counting with deep convolutional and deconvolutional networks
2017 · External reference
Leveraging multiple datasets for deep leaf counting
2017 · External reference
Detection of maize tassels from UAV RGB imagery with faster R-CNN
10.3390/rs12020338 · 2020 · External reference
Toward real scenery: a lightweight tomato growth inspection algorithm for leaf disease detection and fruit counting
10.34133/plantphenomics.0174 · 2024 · External reference
WheatLFANet: in-field detection and counting of wheat heads with high-real-time global regression network
10.1186/s13007-023-01079-x · 2023 · External reference
SpikeSegNet-a deep learning approach utilizing encoder-decoder network with hourglass for spike segmentation and counting in wheat plant from visual imaging
10.1186/s13007-020-00582-9 · 2020 · External reference
Deep residual learning for image recognition
2016 · External reference
Faster R-CNN: towards real-time object detection with region proposal networks
10.1109/tpami.2016.2577031 · 2016 · External reference
Deep plant phenomics: a deep learning platform for complex plant phenotyping tasks
10.3389/fpls.2017.01190 · 2017 · External reference
The use of plant models in deep learning: an application to leaf counting in rosette plants
10.1186/s13007-018-0273-z · 2018 · External reference
TasselNetV3: explainable plant counting with guided upsampling and background suppression
2021 · External reference
Estimation of number of people in crowded scenes using perspective transformation
10.1109/3468.983420 · 2001 · External reference
A people-counting system using a hybrid RBF neural network
10.1023/a:1026226617974 · 2003 · External reference
A viewpoint invariant approach for crowd counting
2006 · External reference
Crowd counting using multiple local features
2009 · External reference
Estimating the number of people in crowded scenes by mid based foreground segmentation and head-shoulder detection
2008 · External reference
Machine vision system for determining citrus count and size on a canopy shake and catch harvester
10.13031/2013.27459 · 2009 · External reference
Detection of corn plant population and row spacing using computer vision
2011 · External reference
Automatic fruit recognition and counting from multiple images
10.1016/j.biosystemseng.2013.12.008 · 2014 · External reference
Unresolved reference
1998 · External reference
Privacy preserving crowd monitoring: counting people without people models or tracking
2008 · External reference
Counting people with low-level features and Bayesian regression
10.1109/tip.2011.2172800 · 2011 · External reference
People counting system in crowded scenes based on feature regression
2012 · External reference
Feature mining for localised crowd counting
2012 · External reference
Count-ception: counting by fully convolutional redundant counting
2017 · External reference
Deep spatial regression model for image crowd counting
2017 · External reference
Adaptive mixture regression network with local counting map for crowd counting
2020 · External reference
Decoupled two-stage crowd counting and beyond
10.1109/tip.2021.3055631 · 2021 · External reference
Counting objects by blockwise classification
10.1109/tcsvt.2019.2942970 · 2019 · External reference
Yolo-count: differentiable object counting for text-to-image generation
2025 · External reference
Deep count: fruit counting based on deep simulated learning
10.3390/s17040905 · 2017 · External reference
TasselNetv2: in-field counting of wheat spikes with context-augmented local regression networks
10.1186/s13007-019-0537-2 · 2019 · External reference
TasselNetV2+: a fast implementation for high-throughput plant counting from high-resolution RGB imagery
10.3389/fpls.2020.541960 · 2020 · External reference
Large-scale counting and localization of pineapple inflorescence through deep density-estimation
10.3389/fpls.2020.599705 · 2021 · External reference
Improved field-based soybean seed counting and localization with feature level considered
10.34133/plantphenomics.0026 · 2023 · External reference
Wheat head counting by estimating a density map with convolutional neural networks
2023 · External reference
Robust soybean seed yield estimation using high-throughput ground robot videos
10.3389/fpls.2025.1554193 · 2025 · External reference
A transformer-based symmetric diffusion segmentation network for wheat growth monitoring and yield counting
10.3390/agriculture15070670 · 2025 · External reference
Optimizing mask R-CNN for enhanced quinoa panicle detection and segmentation in precision agriculture
2025 · External reference
YOLOv5-lotus an efficient object detection method for lotus seedpod in a natural environment
10.1016/j.compag.2023.107635 · 2023 · External reference
Segmentation and counting of wheat spike grains based on deep learning and textural feature
10.1186/s13007-023-01062-6 · 2023 · External reference
A segmentation-guided deep learning framework for leaf counting
2022 · External reference
Mask R-CNN refitting strategy for plant counting and sizing in UAV imagery
10.3390/rs12183015 · 2020 · External reference
Automated sunflower head detection and yield estimation from high-resolution UAV imagery using YOLOv11 for precision agriculture
10.3390/su18021026 · 2026 · External reference
Real-time strawberry ripeness classification and counting: an optimized YOLOv8s framework with class-aware multi-object tracking
10.3390/agriculture15181906 · 2025 · External reference
Barley head detection using UAV imagery and YOLOv10
2025 · External reference
A soybean pod accuracy detection and counting model based on improved YOLOv8
10.3390/agriculture15060617 · 2025 · External reference
COTTON-YOLO: enhancing cotton boll detection and counting in complex environmental conditions using an advanced YOLO model
10.3390/app14156650 · 2024 · External reference
YOLO-Ginseng: a detection method for ginseng fruit in natural agricultural environment
10.3389/fpls.2024.1422460 · 2024 · External reference
Detection and counting of small target apples under complicated environments by using improved YOLOv7-tiny
10.3390/agronomy13051419 · 2023 · External reference
YOLO-P: an efficient method for pear fast detection in complex orchard picking environment
10.3389/fpls.2022.1089454 · 2023 · External reference
Deep-learning-based automated palm tree counting and geolocation in large farms from aerial geotagged images
10.3390/agronomy11081458 · 2021 · External reference
Detection of maize tassels from UAV RGB imagery with faster R-CNN
10.3390/rs12020338 · 2020 · External reference
Real-time pear fruit detection and counting using YOLOv4 models and Deep-SORT
10.3390/s21144803 · 2020 · External reference
TasselNetV4: a vision foundation model for cross-scene, cross-scale, and cross-species plant counting
10.1016/j.isprsjprs.2025.11.017 · 2026 · External reference
LKNet: enhancing rice canopy panicle counting accuracy with an optimized point-based framework
10.1016/j.plaphe.2025.100003 · 2025 · External reference
CTHNet: a network for wheat ear counting with local-global features fusion based on hybrid architecture
10.3389/fpls.2024.1425131 · 2024 · External reference
One to all: toward a unified model for counting cereal crop heads based on few-shot learning
10.34133/plantphenomics.0271 · 2024 · External reference
SPCN: an innovative soybean pod counting network based on HDC strategy and attention mechanism
10.3390/agriculture14081347 · 2024 · External reference
WheatLFANet: in-field detection and counting of wheat heads with high-real-time global regression network
10.1186/s13007-023-01079-x · 2023 · External reference
Accurate segmentation of green fruit based on optimized mask RCNN application in complex orchard
10.3389/fpls.2022.955256 · 2022 · External reference
DeepCorn: a semi-supervised deep learning method for high-throughput image-based corn kernel counting and yield estimation
10.1016/j.knosys.2021.106874 · 2021 · External reference
Tomato fruit detection and counting in greenhouses using deep learning
10.3389/fpls.2020.571299 · 2020 · External reference
P2PNet for rice panicle counting: a point-based approach
2022 · External reference
Unresolved reference
2018 · External reference
Plant counting of cotton from UAS imagery using object detection
2020 · External reference
YOLOPOD: a high-performance model for soybean pod detection
2023 · External reference
Microalgae cell counting using U-Net density estimation
2021 · External reference
Pollen grain recognition using deep learning
2020 · External reference
Deep learning for mango detection and counting
2019 · External reference
Image based mango fruit detection, localisation and yield estimation using multiple view geometry
10.3390/s16111915 · 2016 · External reference
Robust fruit counting: combining deep learning, tracking, and structure from motion
2019 · External reference
Embrapa wine grape instance segmentation dataset – embrapa WGISD
2019 · External reference
Rose flower counting in greenhouse using YOLOv3
2020 · External reference
The role and regulation of the anthesis–silking interval in maize
2000 · External reference
Unresolved reference
2023 · External reference
Finely-grained annotated datasets for image-based plant phenotyping
2016 · External reference
Temporal regression for maize tassel counting using time-lapse cameras
2023 · External reference
Registration of spatio-temporal point clouds of plants for phenotyping
10.1371/journal.pone.0247243 · 2021 · External reference
PhenoTrack3D: an automatic high-throughput phenotyping pipeline to track maize organs over time
10.1186/s13007-022-00961-4 · 2022 · External reference
Visual growth tracking for automated leaf stage monitoring based on image sequence analysis
10.3390/rs13050961 · 2021 · External reference
Learning to count leaves in rosette plants
2015 · External reference
Mango fruit detection and counting using MangoYOLO and SORT
2021 · External reference
Ear density estimation from high resolution RGB imagery using deep learning technique
10.1016/j.agrformet.2018.10.013 · 2019 · External reference
Deep regression versus detection for counting in precision agriculture
2023 · External reference
A multi-view apple fruit detection and counting method based on fusing point cloud and deep learning
2021 · External reference
Simple online and realtime tracking
2016 · External reference
Simple online and realtime tracking with a deep association metric
2017 · External reference
ByteTrack: multi-object tracking by associating every detection box
2022 · External reference
Fruit counting of apple trees based on trunk tracking and shot filtering
2022 · External reference
Real-time detection and counting of dragon fruit based on YOLOv5 and DeepSORT
2023 · External reference
Automated fruit counting in apple orchards using LiDAR and GNSS
2023 · External reference
Kiwifruit counting based on video object tracking and spatio-temporal trajectory analysis
2024 · External reference
Row-based kiwifruit counting using YOLOv8 and ByteTrack with edge constraints
2025 · External reference
Maturity detection and counting of blueberries in real orchards using a novel STF-YOLO model integrated with ByteTrack
10.3389/fpls.2025.1682024 · 2025 · External reference
Dynamic soybean yield estimation via ground robot video analysis and 3D reconstruction
2025 · External reference
Deep learning-based tracking of shoot apical meristem growth in arabidopsis under stress conditions
2023 · External reference
Field rice panicle detection and counting based on deep learning
2022 · External reference
Rice panicle number counting based on improved CSRNet
2025 · External reference
A soybean pod detection and counting model based on improved YOLOv8
2024 · External reference
Improved field-based soybean seed counting and localization with feature level considered
10.34133/plantphenomics.0026 · 2023 · External reference
Cotton stand counting from UAS imagery using MobileNet and CenterNet
10.3390/rs13142822 · 2021 · External reference
YOLOv4-based pear fruit detection and counting
2021 · External reference
Deep fruit detection in orchards
2016 · External reference
Grape detection, segmentation, and tracking using deep neural networks and three-dimensional association
10.1016/j.compag.2020.105247 · 2020 · External reference
Grape bunch detection and counting in the wild
2023 · External reference
Fruit detection and yield mass estimation from a UAV based RGB dense cloud for an apple orchard
10.3390/drones9010060 · 2025 · External reference
AppleGrowthVision: a large-scale stereo dataset for phenological analysis, fruit detection, and 3D reconstruction
2025 · External reference
Fuji-SfM dataset: a collection of annotated images and point clouds for Fuji apple detection and location using structure-from-motion photogrammetry
10.1016/j.dib.2020.105591 · 2020 · External reference
Potato tuber counting and sizing using 3D instance segmentation
2022 · External reference
Deep learning for three-dimensional (3D) plant phenomics
10.1016/j.plaphe.2025.100107 · 2025 · External reference
Unresolved reference
2024 · External reference
Seed counting and classification using deep learning
2021 · External reference
X-ray computed tomography for 3D plant imaging
10.1016/j.tplants.2021.07.010 · 2021 · External reference
Three-dimensional visualization of plant tissues and organs by X-ray micro-computed tomography
10.1093/jmicro/dfad026 · 2023 · External reference
High-throughput three-dimensional visualization of root system architecture of rice using X-ray computed tomography
10.1186/s13007-020-00612-6 · 2020 · External reference
A workflow for segmenting soil and plant X-ray computed tomography images with deep learning in Google's colaboratory
10.3389/fpls.2022.893140 · 2022 · External reference
4DRoot: root phenotyping software for temporal 3D scans by X-ray computed tomography
10.3389/fpls.2022.986856 · 2022 · External reference
Use of X-ray micro computed tomography imaging to analyze the morphology of wheat grain through its development
10.1186/s13007-019-0468-y · 2019 · External reference
Automated extraction of pod phenotype data from micro-computed tomography
10.3389/fpls.2023.1120182 · 2023 · External reference
Automatic 3D cell segmentation of fruit parenchyma tissue from X-ray micro CT images using deep learning
10.1186/s13007-024-01137-y · 2024 · External reference
Agricultural robot dataset for plant classification, localization and mapping on sugar beet
10.1177/0278364917720510 · 2017 · External reference
weedNet: dense semantic weed classification using multispectral images and MAV for smart farming
10.1109/lra.2017.2774979 · 2018 · External reference
WeedMap: a large-scale semantic weed mapping framework using aerial multispectral imaging and deep neural network for precision farming
10.3390/rs10091423 · 2018 · External reference
Weed–crop discrimination using remote sensing: a detached leaf experiment
10.1614/wt02-179 · 2003 · External reference
The cropandweed dataset: a multi-modal learning approach for efficient crop and weed manipulation
2023 · External reference
PhenoBench — a large dataset and benchmarks for semantic image interpretation in the agricultural domain
10.1109/tpami.2024.3419548 · 2024 · External reference
Hyperspectral image analysis techniques for the detection and classification of the early onset of plant disease and stress
10.1186/s13007-017-0233-z · 2017 · External reference
Hyperspectral sensors and imaging technologies in phytopathology: state of the art
10.1146/annurev-phyto-080417-050100 · 2018 · External reference
Chlorophyll fluorescence: a probe of photosynthesis in vivo
10.1146/annurev.arplant.59.032607.092759 · 2008 · External reference
Thermal and chlorophyll-fluorescence imaging distinguish plant-pathogen interactions at an early stage
10.1093/pcp/pch097 · 2004 · External reference
High throughput quantitative phenotyping of plant resistance using chlorophyll fluorescence image analysis
10.1186/1746-4811-9-17 · 2013 · External reference
A simple and sensitive high-throughput GFP screening in woody and herbaceous plants
10.1007/s00299-008-0657-z · 2009 · External reference
Hyperspectral imaging and 3D technologies for plant phenotyping: from satellite to close-range sensing
10.1016/j.compag.2020.105621 · 2020 · External reference
Generating 3D multispectral point clouds of plants with fusion of snapshot spectral and RGB-D images
10.34133/plantphenomics.0040 · 2023 · External reference
Unresolved reference
2019 · External reference
Agricultural robot dataset for plant classification, localization and mapping on sugar beet
10.1177/0278364917720510 · 2017 · External reference
CottonWeedDet12: a large-scale cotton weed detection dataset
2023 · External reference
A dataset for bayberry tree detection and counting
2019 · External reference
MrMT: a multi-resolution multi-task network for tassel detection
10.3390/books978-3-0365-7226-0 · 2023 · External reference
Detection and segmentation of wheat spikes in field conditions
2018 · External reference
wGrapeUNIPD-DL: an open dataset for white grape bunch detection
10.1016/j.dib.2022.108466 · 2022 · External reference
Maize tassels detection: a benchmark of the state of the art
10.1186/s13007-020-00651-z · 2020 · External reference
KFuji RGB-DS database: fuji apple multi-modal images for fruit detection with color, depth and range-corrected IR data
10.1016/j.dib.2019.104289 · 2019 · External reference
A benchmark dataset for citrus fruit detection
10.1109/lra.2024.3474473 · 2024 · External reference
Detection, counting, and maturity assessment of blueberries in canopy images using YOLOv8 and YOLOv9
2024 · External reference
URCAL: a dataset for rice counting
2024 · External reference
Outdoor plant segmentation with deep learning for high-throughput field phenotyping on a diverse wheat dataset
10.3389/fpls.2021.774068 · 2022 · External reference
Segment anything
2023 · External reference
The Cancer Imaging Archive (TCIA): maintaining and operating a public information repository
10.1007/s10278-013-9622-7 · 2013 · External reference
PhysioNet: a research resource for studies of complex physiologic and biomedical signals
2000 · External reference
Plant taxonomy meets plant counting: a fine-grained, taxonomic dataset for counting hundreds of plant species
2026 · External reference
Learning transferable visual models from natural language supervision
2021 · External reference
Class-agnostic few-shot object counting
2021 · External reference
Few-shot object counting with similarity-aware feature enhancement
2023 · External reference
Apple orchard production estimation using deep learning strategies: a comparison of tracking-by-detection algorithms
10.1016/j.compag.2022.107513 · 2023 · External reference
CropTrack: a tracking with Re-Identification framework for precision agriculture
2025 · External reference
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10.64898/2026.03.11.711099 · External reference
What makes multi-modal learning better than single (provably)
2021 · External reference
What makes training multi-modal classification networks hard?
2020 · External reference
SPROUT: A scalable diffusion foundation model for agricultural vision
2026 · External reference
The blessing of depth anything: an almost unsupervised approach to crop segmentation with depth-informed pseudo labeling
10.1016/j.plaphe.2025.100005 · 2025 · External reference
DepthCropSeg++: scaling a crop segmentation foundation model with depth-labeled data
2026 · External reference
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10.52202/079017-1547 · External reference
Unresolved reference
External reference
Three-view cotton flower counting through multi-object tracking and multi-modal imaging
2023 · External reference
Fields of the world: a machine learning benchmark dataset for global agricultural field boundary segmentation
2024 · External reference
Global models and predictions of plant diversity based on advanced machine learning techniques
10.1111/nph.18533 · 2023 · External reference
