Plant Phenotyping

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Phenotyping. Plant Phenotyping.

Plant Phenotyping Datasets for Computer Vision,
WWW Link. Dataset, Plants. We present a collection of benchmark datasets in the context of plant phenotyping. We provide annotated imaging data and suggest suitable evaluation criteria for plant/leaf segmentation, detection, tracking as well as classification and regression problems. The figure symbolically depicts the data available together with ground truth segmentations and further annotations and metadata. Article in press.
See also Finely-grained annotated datasets for image-based plant phenotyping.

Subramanian, R.[Ram], Spalding, E.P.[Edgar P.], Ferrier, N.J.[Nicola J.],
A high throughput robot system for machine vision based plant phenotype studies,
MVA(24), No. 3, April 2013, pp. 619-636.
WWW Link. 1303

Minervini, M., Scharr, H., Tsaftaris, S.,
Image Analysis: The New Bottleneck in Plant Phenotyping,
SPMag(32), No. 4, July 2015, pp. 126-131.
[Applications Corner] Agriculture BibRef

Minervini, M.[Massimo], Fischbachb, A.[Andreas], Scharrb, H.[Hanno], Tsaftarisa, S.A.[Sotirios A.],
Finely-grained annotated datasets for image-based plant phenotyping,
PRL(81), No. 1, 2016, pp. 80-89.
Elsevier DOI
PDF File. The dataset:
See also Plant Phenotyping Datasets for Computer Vision. BibRef 1600

Scharr, H.[Hanno], Dee, H.[Hannah], French, A.P.[Andrew P.], Tsaftaris, S.A.[Sotirios A.],
Special issue on computer vision and image analysis in plant phenotyping,
MVA(27), No. 5, July 2016, pp. 607-609.
Springer DOI 1608

Golbach, F.[Franck], Kootstra, G.[Gert], Damjanovic, S.[Sanja], Otten, G.[Gerwoud], van de Zedde, R.[Rick],
Validation of plant part measurements using a 3D reconstruction method suitable for high-throughput seedling phenotyping,
MVA(27), No. 5, July 2016, pp. 663-680.
Springer DOI 1608

Kelly, D.[Derek], Vatsa, A.[Avimanyou], Mayham, W.[Wade], Ngô, L.[Linh], Thompson, A.[Addie], Kazic, T.[Toni],
An opinion on imaging challenges in phenotyping field crops,
MVA(27), No. 5, July 2016, pp. 681-694.
Springer DOI 1608

Cruz, J.A.[Jeffrey A.], Yin, X.[Xi], Liu, X.M.[Xiao-Ming], Imran, S.M.[Saif M.], Morris, D.D.[Daniel D.], Kramer, D.M.[David M.], Chen, J.[Jin],
Multi-modality imagery database for plant phenotyping,
MVA(27), No. 5, July 2016, pp. 735-749.
Springer DOI 1608

Pound, M.P.[Michael P.], French, A.P.[Andrew P.], Fozard, J.A.[John A.], Murchie, E.H.[Erik H.], Pridmore, T.P.[Tony P.],
A patch-based approach to 3D plant shoot phenotyping,
MVA(27), No. 5, July 2016, pp. 767-779.
Springer DOI 1608

Patrick, A.[Aaron], Li, C.Y.[Chang-Ying],
High Throughput Phenotyping of Blueberry Bush Morphological Traits Using Unmanned Aerial Systems,
RS(9), No. 12, 2017, pp. xx-yy.
DOI Link 1802

Asaari, M.S.M.[Mohd Shahrimie Mohd], Mishra, P.[Puneet], Mertens, S.[Stien], Dhondt, S.[Stijn], Inzé, D.[Dirk], Wuyts, N.[Nathalie], Scheunders, P.[Paul],
Close-range hyperspectral image analysis for the early detection of stress responses in individual plants in a high-throughput phenotyping platform,
PandRS(138), 2018, pp. 121-138.
Elsevier DOI 1804
Close-range hyperspectral imaging, Linear reflectance model, Standard normal variate, Spectral similarity measure, Plant stress BibRef

Hu, P.C.[Peng-Cheng], Guo, W.[Wei], Chapman, S.C.[Scott C.], Guo, Y.[Yan], Zheng, B.Y.[Bang-You],
Pixel size of aerial imagery constrains the applications of unmanned aerial vehicle in crop breeding,
PandRS(154), 2019, pp. 1-9.
Elsevier DOI 1907
Plant phenotyping, Ground coverage, Remote sensing, Pixel size, UAV BibRef

Sagan, V.[Vasit], Maimaitijiang, M.[Maitiniyazi], Sidike, P.[Paheding], Eblimit, K.[Kevin], Peterson, K.T.[Kyle T.], Hartling, S.[Sean], Esposito, F.[Flavio], Khanal, K.[Kapil], Newcomb, M.[Maria], Pauli, D.[Duke], Ward, R.[Rick], Fritschi, F.[Felix], Shakoor, N.[Nadia], Mockler, T.[Todd],
UAV-Based High Resolution Thermal Imaging for Vegetation Monitoring, and Plant Phenotyping Using ICI 8640 P, FLIR Vue Pro R 640, and thermoMap Cameras,
RS(11), No. 3, 2019, pp. xx-yy.
DOI Link 1902

Silva, E.[Ewerton], da Silva Torres, R.[Ricardo], Alberton, B.[Bruna], Morellato, L.P.C.[Leonor Patricia C.], Silva, T.S.F.[Thiago S. F.],
A Change-Driven Image Foveation Approach for Tracking Plant Phenology,
RS(12), No. 9, 2020, pp. xx-yy.
DOI Link 2005

Atanbori, J.[John], French, A.P.[Andrew P.], Pridmore, T.P.[Tony P.],
Towards infield, live plant phenotyping using a reduced-parameter CNN,
MVA(31), No. 1, January 2020, pp. Article2.
WWW Link. 2001

Ward, D.[Daniel], Moghadam, P.[Peyman],
Scalable learning for bridging the species gap in image-based plant phenotyping,
CVIU(197-198), 2020, pp. 103009.
Elsevier DOI 2008

Manish, R.[Raja], Lin, Y.C.[Yi-Chun], Ravi, R.[Radhika], Hasheminasab, S.M.[Seyyed Meghdad], Zhou, T.[Tian], Habib, A.[Ayman],
Development of a Miniaturized Mobile Mapping System for In-Row, Under-Canopy Phenotyping,
RS(13), No. 2, 2021, pp. xx-yy.
DOI Link 2101

Koh, J.C.O.[Joshua C.O.], Spangenberg, G.[German], Kant, S.[Surya],
Automated Machine Learning for High-Throughput Image-Based Plant Phenotyping,
RS(13), No. 5, 2021, pp. xx-yy.
DOI Link 2103

Gao, T.[Tian], Zhu, F.Y.[Fei-Yu], Paul, P.[Puneet], Sandhu, J.[Jaspreet], Doku, H.A.[Henry Akrofi], Sun, J.X.[Jian-Xin], Pan, Y.[Yu], Staswick, P.[Paul], Walia, H.[Harkamal], Yu, H.F.[Hong-Feng],
Novel 3D Imaging Systems for High-Throughput Phenotyping of Plants,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106

Paturkar, A.[Abhipray], Gupta, G.S.[Gourab Sen], Bailey, D.[Donald],
Making Use of 3D Models for Plant Physiognomic Analysis: A Review,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106

Cao, M.Y.[Meng-Ying], Sun, Y.[Ying], Jiang, X.[Xin], Li, Z.[Ziming], Xin, Q.[Qinchuan],
Identifying Leaf Phenology of Deciduous Broadleaf Forests from PhenoCam Images Using a Convolutional Neural Network Regression Method,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106

Aslahishahri, M.[Masoomeh], Stanley, K.G.[Kevin G.], Duddu, H.[Hema], Shirtliffe, S.[Steve], Vail, S.[Sally], Stavness, I.[Ian],
Spatial Super Resolution of Real-World Aerial Images for Image-Based Plant Phenotyping,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106

Ma, D.D.[Dong-Dong], Rehman, T.U.[Tanzeel U.], Zhang, L.[Libo], Maki, H.[Hideki], Tuinstra, M.R.[Mitchell R.], Jin, J.[Jian],
Modeling of Environmental Impacts on Aerial Hyperspectral Images for Corn Plant Phenotyping,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107

Wang, H.Z.[Hao-Zhou], Duan, Y.L.[Yu-Lin], Shi, Y.[Yun], Kato, Y.[Yoichiro], Ninomiya, S.[Seishi], Guo, W.[Wei],
EasyIDP: A Python Package for Intermediate Data Processing in UAV-Based Plant Phenotyping,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107
Code, Plant Phenotype. BibRef

Khoroshevsky, F.[Faina], Khoroshevsky, S.[Stanislav], Bar-Hillel, A.[Aharon],
Parts-per-Object Count in Agricultural Images: Solving Phenotyping Problems via a Single Deep Neural Network,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107

Arun, P.V.[Pattathal V.], Karnieli, A.[Arnon],
Deep Learning-Based Phenological Event Modeling for Classification of Crops,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107

Hu, P.C.[Peng-Cheng], Chapman, S.C.[Scott C.], Jin, H.D.[Hui-Dong], Guo, Y.[Yan], Zheng, B.[Bangyou],
Comparison of Modelling Strategies to Estimate Phenotypic Values from an Unmanned Aerial Vehicle with Spectral and Temporal Vegetation Indexes,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107

de Lutio, R.[Riccardo], She, Y.H.[Yi-Hang], d'Aronco, S.[Stefano], Russo, S.[Stefania], Brun, P.[Philipp], Wegner, J.D.[Jan D.], Schindler, K.[Konrad],
Digital taxonomist: Identifying plant species in community scientists' photographs,
PandRS(182), 2021, pp. 112-121.
Elsevier DOI 2112
Species recognition, Community science, Hierarchical classification, Multimodal learning BibRef

Huang, X.[Xia], Zheng, S.Y.[Shun-Yi], Zhu, N.N.[Ning-Ning],
High-Throughput Legume Seed Phenotyping Using a Handheld 3D Laser Scanner,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201

Rincón, M.G.[Manuel García], Mendez, D.[Diego], Colorado, J.D.[Julian D.],
Four-Dimensional Plant Phenotyping Model Integrating Low-Density LiDAR Data and Multispectral Images,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201

Rehman, T.U.[Tanzeel U.], Zhang, L.[Libo], Ma, D.D.[Dong-Dong], Jin, J.[Jian],
Common Latent Space Exploration for Calibration Transfer across Hyperspectral Imaging-Based Phenotyping Systems,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201

Taylor, S.D.[Shawn D.], Browning, D.M.[Dawn M.],
Classification of Daily Crop Phenology in PhenoCams Using Deep Learning and Hidden Markov Models,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link 2201

Li, D.W.[Da-Wei], Shi, G.L.[Guo-Liang], Li, J.S.[Jin-Sheng], Chen, Y.L.[Ying-Liang], Zhang, S.Y.[Song-Yin], Xiang, S.Y.[Shi-Yu], Jin, S.C.[Shi-Chao],
PlantNet: A dual-function point cloud segmentation network for multiple plant species,
PandRS(184), 2022, pp. 243-263.
Elsevier DOI 2202
Plant phenotyping, Point cloud, Semantic segmentation, Instance segmentation, Deep learning BibRef

Li, C.[Cheng], Zou, Y.Y.[Yu-Yang], He, J.F.[Jian-Feng], Zhang, W.[Wen], Gao, L.[Lulu], Zhuang, D.F.[Da-Fang],
Response of Vegetation Phenology to the Interaction of Temperature and Precipitation Changes in Qilian Mountains,
RS(14), No. 5, 2022, pp. xx-yy.
DOI Link 2203

Basak, R.[Rinku], Wahid, K.A.[Khan A.],
A Rapid, Low-Cost, and High-Precision Multifrequency Electrical Impedance Tomography Data Acquisition System for Plant Phenotyping,
RS(14), No. 13, 2022, pp. xx-yy.
DOI Link 2208

Cao, H.Q.[He-Qin], Hua, Y.[Yan], Liang, X.[Xin], Long, Z.[Zexu], Qi, J.Z.[Jin-Zhe], Wen, D.[Dusu], Roberts, N.J.[Nathan James], Su, H.J.[Hai-Jun], Jiang, G.S.[Guang-Shun],
Wavelet Analysis Reveals Phenology Mismatch between Leaf Phenology of Temperate Forest Plants and the Siberian Roe Deer Molting under Global Warming,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208

Li, M.Y.[Meng-Yu], Yang, W.[Wei], Kondoh, A.[Akihiko],
Improving Remote Estimation of Vegetation Phenology Using GCOM-C/SGLI Land Surface Reflectance Data,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link 2208

Du, R.M.[Rui-Ming], Ma, Z.H.[Zhi-Hong], Xie, P.[Pengyao], He, Y.[Yong], Cen, H.Y.[Hai-Yan],
PST: Plant segmentation transformer for 3D point clouds of rapeseed plants at the podding stage,
PandRS(195), 2023, pp. 380-392.
Elsevier DOI 2301
3D deep learning, Point cloud segmentation, Handheld laser scanning, Plant phenotyping BibRef

Esser, F.[Felix], Klingbeil, L.[Lasse], Zabawa, L.[Lina], Kuhlmann, H.[Heiner],
Quality Analysis of a High-Precision Kinematic Laser Scanning System for the Use of Spatio-Temporal Plant and Organ-Level Phenotyping in the Field,
RS(15), No. 4, 2023, pp. xx-yy.
DOI Link 2303

Gobin, A.[Anne], Sallah, A.H.M.[Abdoul-Hamid Mohamed], Curnel, Y.[Yannick], Delvoye, C.[Cindy], Weiss, M.[Marie], Wellens, J.[Joost], Piccard, I.[Isabelle], Planchon, V.[Viviane], Tychon, B.[Bernard], Goffart, J.P.[Jean-Pierre], Defourny, P.[Pierre],
Crop Phenology Modelling Using Proximal and Satellite Sensor Data,
RS(15), No. 8, 2023, pp. 2090.
DOI Link 2305

Ayankojo, I.T.[Ibukun T.], Thorp, K.R.[Kelly R.], Thompson, A.L.[Alison L.],
Advances in the Application of Small Unoccupied Aircraft Systems (sUAS) for High-Throughput Plant Phenotyping,
RS(15), No. 10, 2023, pp. xx-yy.
DOI Link 2306

Zhang, Y.[Ying], Su, W.[Wei], Tao, W.C.[Wan-Cheng], Li, Z.Q.[Zi-Qian], Huang, X.[Xianda], Zhang, Z.Y.[Zi-Yue], Xiong, C.[Caisen],
Completing 3D Point Clouds of Thin Corn Leaves for Phenotyping Using 3D Gridding Convolutional Neural Networks,
RS(15), No. 22, 2023, pp. 5289.
DOI Link 2311

Victor, B.[Brandon], Nibali, A.[Aiden], Newman, S.J.[Saul Justin], Coram, T.[Tristan], Pinto, F.[Francisco], Reynolds, M.[Matthew], Furbank, R.T.[Robert T.], He, Z.[Zhen],
High-Throughput Plot-Level Quantitative Phenotyping Using Convolutional Neural Networks on Very High-Resolution Satellite Images,
RS(16), No. 2, 2024, pp. 282.
DOI Link 2402

Liu, Y.X.[Yu-Xia], Zhang, X.Y.[Xiao-Yang], Shen, Y.[Yu], Ye, Y.C.[Yong-Chang], Gao, S.[Shuai], Tran, K.H.[Khuong H.],
Evaluation of PlanetScope-detected plant-specific phenology using infrared-enabled PhenoCam observations in semi-arid ecosystems,
PandRS(210), 2024, pp. 242-259.
Elsevier DOI 2404
Plant-specific phenology, Semi-arid ecosystems, PlanetScope, PhenoCam, Infrared-enabled PhenoCam, Reconstruction time series BibRef

Liu, M.[Man], He, W.[Wei], Zhang, H.Y.[Hong-Yan],
WPS: A whole phenology-based spectral feature selection method for mapping winter crop from time-series images,
PandRS(210), 2024, pp. 141-159.
Elsevier DOI 2404
Phenological spectral feature, Feature selection, Winter crop mapping, Spectral separability, Time-weighted dynamic time warping BibRef

Li, D.W.[Da-Wei], Zhou, Z.[Zhaoyi], Wei, Y.C.[Yong-Chang],
Unsupervised shape-aware SOM down-sampling for plant point clouds,
PandRS(211), 2024, pp. 172-207.
Elsevier DOI 2405
Sampling, Self-organizing Map, Plant Phenotyping, Point Cloud Data, Organ Segmentation BibRef

Benito-Verdugo, P.[Pilar], González-Zamora, Á.[Ángel], Martínez-Fernández, J.[José],
Recent Cereal Phenological Variations under Mediterranean Conditions,
RS(16), No. 11, 2024, pp. 1879.
DOI Link 2406

Wagner, N.[Nikolaus], Cielniak, G.[Grzegorz],
Vision-based Monitoring of the Short-term Dynamic Behaviour of Plants for Automated Phenotyping,

Cherepashkin, V.[Vsevolod], Yildiz, E.[Erenus], Fischbach, A.[Andreas], Kobbelt, L.[Leif], Scharr, H.[Hanno],
Deep learning based 3d reconstruction for phenotyping of wheat seeds: a dataset, challenge, and baseline method,

Chen, F.[Feng], Giuffrida, M.V.[Mario Valerio], Tsaftaris, S.A.[Sotirios A.],
Adapting Vision Foundation Models for Plant Phenotyping,

Weyler, J.[Jan], Magistri, F.[Federico], Seitz, P.[Peter], Behley, J.[Jens], Stachniss, C.[Cyrill],
In-Field Phenotyping Based on Crop Leaf and Plant Instance Segmentation,
Image segmentation, Codes, Plants (biology), Crops, Image representation, Convolutional neural networks, Grouping and Shape BibRef

Gomes, D.P.S.[Douglas Pinto Sampaio], Zheng, L.H.[Li-Hong],
Recent Data Augmentation Strategies for Deep Learning in Plant Phenotyping and Their Significance,
Training, Deep learning, Pipelines, Training data, Data models, Task analysis, Optimization, augmentation, leaf counting, synthetic data BibRef

Bhugra, S.[Swati], Garg, K.[Kanish], Chaudhury, S.[Santanu], Lall, B.[Brejesh],
A Hierarchical Framework for Leaf Instance Segmentation: Application to Plant Phenotyping,
Deep learning, Image segmentation, Shape, Annotations, Estimation, Pattern recognition, Biomass BibRef

Azimi, S.[Shiva], Kaur, T.[Taranjit], Gandhi, T.K.[Tapan K],
BAT Optimized CNN Model Identifies Water Stress in Chickpea Plant Shoot Images,
Training, Proteins, Computational modeling, Plants (biology), Tools, Agriculture, Real-time systems, BAT optimization, plant phenotyping BibRef

Hutton, J.J., Lipa, G., Baustian, D., Sulik, J., Bruce, R.W.,
High Accuracy Direct Georeferencing of the Altum Multi-spectral UAV Camera and Its Application to High Throughput Plant Phenotyping,
DOI Link 2012

Lyu, B., Smith, S.D., Cherkauer, K.A.,
Fine-Grained Recognition in High-throughput Phenotyping,
Feature extraction, Histograms, Image recognition, Task analysis, Pattern recognition, Biological system modeling, Visualization BibRef

Grenzdörffer, G.J.,
Automatic Generation of Geometric Parameters of Individual Cauliflower Plants for Rapid Phenotyping Using Drone Images,
DOI Link 1912

Chen, Y., Ribera, J., Delp, E.J.,
Estimating Plant Centers Using A Deep Binary Classifier,
Unmanned aerial vehicles, Agriculture, Image segmentation, Shape, Chemicals, Image analysis, Genetics, Plant Phenotyping, CNN BibRef

Choudhury, S.D., Goswami, S., Bashyam, S., Awada, T., Samal, A.,
Automated Stem Angle Determination for Temporal Plant Phenotyping Analysis,
Cameras, Colored noise, Image color analysis, Image segmentation, Image sequences, Junctions, Skeleton BibRef

Uchiyama, H., Sakurai, S., Mishima, M., Arita, D., Okayasu, T., Shimada, A., Taniguchi, R.I.,
An Easy-to-Setup 3D Phenotyping Platform for KOMATSUNA Dataset,
Cameras, Image color analysis, Indoor environments, Lighting, Soil, Tools BibRef

Pound, M.P., Atkinson, J.A., Wells, D.M., Pridmore, T.P., French, A.P.,
Deep Learning for Multi-task Plant Phenotyping,
Agriculture, Ear, Image resolution, Image segmentation, Machine learning, Training BibRef

Bhugra, S., Anupama, A., Chaudhury, S., Lall, B., Chugh, A.,
Phenotyping of xylem vessels for drought stress analysis in rice,
DOI Link 1708
Feature extraction, Image segmentation, Microscopy, Morphology, Principal component analysis, Shape, Stress BibRef

Nguyen, C.V.[Chuong V.], Fripp, J.[Jurgen], Lovell, D.R.[David R.], Furbank, R.[Robert], Kuffner, P.[Peter], Daily, H.[Helen], Sirault, X.[Xavier],
3D Scanning System for Automatic High-Resolution Plant Phenotyping,
Australia BibRef

Han, S.[Simeng], Cointault, F.[Frédéric], Salon, C.[Christophe], Simon, J.C.[Jean-Claude],
Automatic Detection of Nodules in Legumes by Imagery in a Phenotyping Context,
Springer DOI 1511

Santos, T.T.[Thiago Teixeira], Koenigkan, L.V.[Luciano Vieira], Barbedo, J.G.A.[Jayme Garcia Arnal], Rodrigues, G.C.[Gustavo Costa],
3D Plant Modeling: Localization, Mapping and Segmentation for Plant Phenotyping Using a Single Hand-held Camera,
Springer DOI 1504

Song, Y.[Yu], Glasbey, C.A.[Chris A.], van der Heijden, G.W.A.M.[Gerie W.A.M.], Polder, G.[Gerrit], Dieleman, J.A.[J. Anja],
Combining Stereo and Time-of-Flight Images with Application to Automatic Plant Phenotyping,
Springer DOI 1105

Roerink, G.J., Danes, M.H.G.I., Gomez Prieto, O., de Wit, A.J.W., van Vliet, A.J.H.,
Deriving plant phenology from remote sensing,

Chapter on Implementations and Applications, Databases, QBIC, Video Analysis, Hardware and Software, Inspection continues in
Precision Agriculture Tools .

Last update:Jul 13, 2024 at 15:27:21