Plants in Computer Vision

In this topical collection (20 articles)

1. Software

   Leaf-GP: an open and automated software application for measuring growth phenotypes for arabidopsis and wheat

   Ji Zhou, Christopher Applegate, Albor Dobon Alonso, Daniel Reynolds… Article:117

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2. Methodology

   Fine-grained recognition of plants from images

   Milan Šulc, Jiří Matas Article:115

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3. Software

   Segmentation of 3D images of plant tissues at multiple scales using the level set method

   Annamária Kiss, Typhaine Moreau, Vincent Mirabet, Cerasela Iliana Calugaru… Article:114

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4. Methodology

   PYM: a new, affordable, image-based method using a Raspberry Pi to phenotype plant leaf area in a wide diversity of environments

   Benoît Valle, Thierry Simonneau, Romain Boulord, Francis Sourd… Article:98

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5. Research

   Acquiring and preprocessing leaf images for automated plant identification: understanding the tradeoff between effort and information gain

   Michael Rzanny, Marco Seeland, Jana Wäldchen, Patrick Mäder Article:97

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6. Methodology

   A robot-assisted imaging pipeline for tracking the growths of maize ear and silks in a high-throughput phenotyping platform

   Nicolas Brichet, Christian Fournier, Olivier Turc, Olivier Strauss… Article:96

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7. Methodology

   A “Do-It-Yourself” phenotyping system: measuring growth and morphology throughout the diel cycle in rosette shaped plants

   Andrei Dobrescu, Livia C. T. Scorza, Sotirios A. Tsaftaris… Article:95

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8. Research

   Microscope image based fully automated stomata detection and pore measurement method for grapevines

   Hiranya Jayakody, Scarlett Liu, Mark Whitty, Paul Petrie Article:94

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9. Methodology article

   A novel 3D imaging system for strawberry phenotyping

   Joe Q. He, Richard J. Harrison, Bo Li Article:93

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10. Methodology

    Histological quantification of maize stem sections from FASGA-stained images

    David Legland, Fadi El-Hage, Valérie Méchin, Matthieu Reymond Article:84

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11. Research

    TasselNet: counting maize tassels in the wild via local counts regression network

    Hao Lu, Zhiguo Cao, Yang Xiao, Bohan Zhuang, Chunhua Shen Article:79

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12. Research

    Non-destructive, high-content analysis of wheat grain traits using X-ray micro computed tomography

    Aoife Hughes, Karen Askew, Callum P. Scotson, Kevin Williams, Colin Sauze… Article:76

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13. Methodology

    A method for automatic segmentation and splitting of hyperspectral images of raspberry plants collected in field conditions

    Dominic Williams, Avril Britten, Susan McCallum, Hamlyn Jones… Article:74

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14. Research

    Remote estimation of rapeseed yield with unmanned aerial vehicle (UAV) imaging and spectral mixture analysis

    Yan Gong, Bo Duan, Shenghui Fang, Renshan Zhu, Xianting Wu, Yi Ma, Yi Peng Article:70

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15. Methodology

    Deep phenotyping: deep learning for temporal phenotype/genotype classification

    Sarah Taghavi Namin, Mohammad Esmaeilzadeh, Mohammad Najafi, Tim B. Brown… Article:66

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16. Methodology

    Image-based methods for phenotyping growth dynamics and fitness components in Arabidopsis thaliana

    François Vasseur, Justine Bresson, George Wang, Rebecca Schwab… Article:63

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17. Methodology

    Classification of CITES-listed and other neotropical Meliaceae wood images using convolutional neural networks

    Prabu Ravindran, Adriana Costa, Richard Soares, Alex C. Wiedenhoeft Article:25

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18. Research

    Citizen crowds and experts: observer variability in image-based plant phenotyping

    M. Valerio Giuffrida, Feng Chen, Hanno Scharr, Sotirios A. Tsaftaris Article:12

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19. Methodology

    The use of plant models in deep learning: an application to leaf counting in rosette plants

    Jordan Ubbens, Mikolaj Cieslak, Przemyslaw Prusinkiewicz, Ian Stavness Article:6

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20. Methodology

    Using k-NN to analyse images of diverse germination phenotypes and detect single seed germination in Miscanthus sinensis

    Danny Awty-Carroll, John Clifton-Brown, Paul Robson Article:5

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