20.7.3.7.2 Plant Phenotyping

Plant Phenotyping Datasets for Computer Vision,
2016
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
BibRef

Minervini, M., Scharr, H., Tsaftaris, S.,
Image Analysis: The New Bottleneck in Plant Phenotyping,
SPMag(32), No. 4, July 2015, pp. 126-131.
IEEE DOI 1506
[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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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
BibRef

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.Y.[Zhao-Yi], 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
BibRef

Wu, T.[Tong], Xu, X.Q.[Xiao-Qian], Chen, X.S.[Xin-Sen], Lyu, S.X.[Shi-Xuan], Zhang, G.T.[Guo-Tao], Kong, D.D.[Dong-Dong], Zhang, Y.Q.[Yong-Qiang], Tang, Y.[Yijuan], Chen, Y.[Yun], Zhang, J.L.[Jun-Long],
Characterizing Vegetation Phenology Shifts on the Loess Plateau over Past Two Decades,
RS(16), No. 14, 2024, pp. 2583.
DOI Link 2408
BibRef

Jurayj, D.[Duncan], Bowers, R.[Rebecca], Fayne, J.V.[Jessica V.],
Phenology and Plant Functional Type Link Optical Properties of Vegetation Canopies to Patterns of Vertical Vegetation Complexity,
RS(16), No. 14, 2024, pp. 2577.
DOI Link 2408
BibRef

Carril, P.[Pablo], Colzi, I.[Ilaria], Salvini, R.[Riccardo], Beltramone, L.[Luisa], Rindinella, A.[Andrea], Ermini, A.[Andrea], Gonnelli, C.[Cristina], Garzelli, A.[Andrea], Loppi, S.[Stefano],
Multispectral, Thermographic and Spectroradiometric Analyses Unravel Bio-Stimulatory Effects of Wood Distillate in Field-Grown Chickpea (Cicer arietinum L.),
RS(16), No. 14, 2024, pp. 2524.
DOI Link 2408
BibRef

Cao, R.[Ruyin], Li, L.[Luchun], Liu, L.[Licong], Liang, H.Y.[Hong-Yi], Zhu, X.L.[Xiao-Lin], Shen, M.G.[Miao-Gen], Zhou, J.[Ji], Li, Y.[Yuechen], Chen, J.[Jin],
A spatiotemporal shape model fitting method for within-season crop phenology detection,
PandRS(217), 2024, pp. 179-198.
Elsevier DOI 2409
Crop phenology, Crop management, In-season, Near real-time, Phenology prediction BibRef

Gong, Z.[Zheng], Ge, W.[Wenyan], Guo, J.Q.[Jia-Qi], Liu, J.C.[Jin-Cheng],
Satellite remote sensing of vegetation phenology: Progress, challenges, and opportunities,
PandRS(217), 2024, pp. 149-164.
Elsevier DOI 2409
Vegetation Phenology, Remote Sensing, Vegetation Indices, Remote Sensing Data Smoothing, Phenological Parameter Extraction BibRef

Cheng, Y.W.[Ya-Wen], Ren, N.[Ni], Hu, A.[Anqi], Zhou, L.[Lingli], Qi, C.[Chao], Zhang, S.[Shuo], Wu, Q.[Qian],
An Improved 2D Pose Estimation Algorithm for Extracting Phenotypic Parameters of Tomato Plants in Complex Backgrounds,
RS(16), No. 23, 2024, pp. 4385.
DOI Link 2501
BibRef

Rivas, H.[Henry], Delbart, N.[Nicolas], Maignan, F.[Fabienne], Vaudour, E.[Emmanuelle], Ottlé, C.[Catherine],
Country-Scale Crop-Specific Phenology from Disaggregated PROBA-V,
RS(16), No. 23, 2024, pp. 4521.
DOI Link 2501
BibRef

Stausberg, L.[Lina], Jost, B.[Berit], Klingbeil, L.[Lasse], Kuhlmann, H.[Heiner],
A 3D Surface Reconstruction Pipeline for Plant Phenotyping,
RS(16), No. 24, 2024, pp. 4720.
DOI Link 2501
BibRef

Nethala, P.[Prasad], Um, D.[Dugan], Vemula, N.[Neha], Montero, O.F.[Oscar Fernandez], Lee, K.[Kiju], Bhandari, M.[Mahendra],
Techniques for Canopy to Organ Level Plant Feature Extraction via Remote and Proximal Sensing: A Survey and Experiments,
RS(16), No. 23, 2024, pp. 4370.
DOI Link 2501
BibRef

Anku, K.[Kenneth], Percival, D.[David], Vankoughnett, M.[Mathew], Lada, R.[Rajasekaran], Heung, B.[Brandon],
Monitoring and Prediction of Wild Blueberry Phenology Using a Multispectral Sensor,
RS(17), No. 2, 2025, pp. 334.
DOI Link 2502
BibRef

Pal, D.[Debasmita], Ross, A.[Arun],
Synthesizing forestry images conditioned on plant phenotype using a generative adversarial network,
PR(162), 2025, pp. 111385.
Elsevier DOI 2503
Generative modeling, Synthetic forestry imagery, Plant phenotype prediction, Canopy greenness (GCC), Redness of plants (RCC) BibRef

Niu, Z.Z.[Zhong-Zhong], Li, X.[Xuan], Zhao, T.Z.[Tian-Zhang], Chen, Z.Y.[Zhi-Yuan], Jin, J.[Jian],
Advanced Plant Phenotyping Technologies for Enhanced Detection and Mode of Action Analysis of Herbicide Damage Management,
RS(17), No. 7, 2025, pp. 1166.
DOI Link 2504
BibRef

Wagner, N.[Nikolaus], Cielniak, G.[Grzegorz],
Vision-based Monitoring of the Short-term Dynamic Behaviour of Plants for Automated Phenotyping,
CVPPA23(624-633)
IEEE DOI 2401
BibRef

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,
CVPPA23(561-571)
IEEE DOI 2401
BibRef

Chen, F.[Feng], Giuffrida, M.V.[Mario Valerio], Tsaftaris, S.A.[Sotirios A.],
Adapting Vision Foundation Models for Plant Phenotyping,
CVPPA23(604-613)
IEEE DOI 2401
BibRef

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,
WACV22(2968-2977)
IEEE DOI 2202
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,
DICTA20(1-8)
IEEE DOI 2201
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,
ICPR21(10173-10179)
IEEE DOI 2105
Deep learning, Image segmentation, Shape, Annotations, Estimation, 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,
ICPR21(8500-8506)
IEEE DOI 2105
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,
ISPRS20(B1:451-456).
DOI Link 2012
BibRef

Lyu, B., Smith, S.D., Cherkauer, K.A.,
Fine-Grained Recognition in High-throughput Phenotyping,
AgriVision20(320-329)
IEEE DOI 2008
Feature extraction, Histograms, Image recognition, Task analysis, Biological system modeling, Visualization BibRef

Grenzdörffer, G.J.,
Automatic Generation of Geometric Parameters of Individual Cauliflower Plants for Rapid Phenotyping Using Drone Images,
UAV-g19(329-335).
DOI Link 1912
BibRef

Chen, Y., Ribera, J., Delp, E.J.,
Estimating Plant Centers Using A Deep Binary Classifier,
Southwest18(105-108)
IEEE DOI 1809
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,
CVPPP17(2022-2029)
IEEE DOI 1802
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,
CVPPP17(2038-2045)
IEEE DOI 1802
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,
CVPPP17(2055-2063)
IEEE DOI 1802
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,
MVA17(428-431)
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,
DICTA16(1-8)
IEEE DOI 1701
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,
CAIP15(II:134-145).
Springer DOI 1511
BibRef

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,
PlantType14(247-263).
Springer DOI 1504
BibRef

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,
SCIA11(467-478).
Springer DOI 1105
BibRef

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,
MultiTemp11(261-264).
IEEE DOI 1109
BibRef

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