19.7.3.7.1 Plant Phenotyping

Chapter Contents (Back)
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.[Changying],
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


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, 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,
ICPR21(8500-8506)
IEEE DOI 2105
Training, Proteins, Computational modeling, Plants (biology), Tools, Agriculture, Real-time systems, computer vision, 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, 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,
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, Computer vision, 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 .


Last update:Oct 16, 2021 at 11:54:21