Special issue on computer vision and image analysis in plant phenotyping

Augustin, M., Haxhimusa, Y., Busch, W., Kropatsch, W.G.: A framework for the extraction of quantitative traits from 2d images of mature Arabidopsis thaliana. Mach. Vis. Appl. 27(5), 647–661 (2016). doi:10.1007/s00138-015-0720-z Augustin, M., Haxhimusa, Y., Busch, W., Kropatsch, W.G.: Image-based phenotyping of the mature Arabidopsis shoot system. In: Computer Vision—ECCV 2014 Workshops, vol. 8928, pp. 231–246. Springer (2015) Behmann, J., Mahlein, A.K., Paulus, S., Dupuis, J., Kuhlmann, H., Oerke, E.C., Plümer, L.: Generation and application of hyperspectral 3d plant models: methods and challenges. Mach. Vis. Appl. 27(5), 611–624 (2016). doi:10.1007/s00138-015-0716-8 Behmann, J., Mahlein, A.K., Paulus, S., Kuhlmann, H., Oerke, E.C., Plümer, L.: Generation and application of hyperspectral 3d plant models. In: L. Agapito, M.M. Bronstein, C. Rother (eds.) Computer Vision—ECCV 2014 Workshops, vol. 8928, pp. 117–130. Springer (2016). doi:10.1007/978-3-319-16220-1_9 Benoit, L., Benoit, R., Belin, É., Vadaine, R., Demilly, D., Chapeau-Blondeau, F., Rousseau, D.: On the value of the Kullback-Leibler divergence for cost-effective spectral imaging of plants by optimal selection of wavebands. Mach. Vis. Appl. 27(5), 625–635 (2016). doi:10.1007/s00138-015-0717-7 Boyle, R.D., Corke, F.M.K., Doonan, J.H.: Automated estimation of tiller number in wheat by ribbon detection. Mach. Vis. Appl. 27(5), 637–646 (2016). doi:10.1007/s00138-015-0719-5 Cruz, J.A., Yin, X., Liu, X., Imran, S.M., Morris, D.D., Kramer, D.M., Chen, J.: Multi-modality imagery database for plant phenotyping. Mach. Vis. Appl. 27(5), 735–749 (2016). doi:10.1007/s00138-015-0734-6 Golbach, F., Kootstra, G., Damjanovic, S., Otten, G., Zedde, R.: Validation of plant part measurements using a 3d reconstruction method suitable for high-throughput seedling phenotyping. Mach. Vis. Appl. 27(5), 663–680 (2016). doi:10.1007/s00138-015-0727-5 Kelly, D., Vatsa, A., Mayham, W., Kazic, T.: Extracting complex lesion phenotypes in Zea mays. Mach. Vis. Appl. 27(1), 145–156 (2016). doi:10.1007/s00138-015-0718-6 Kelly, D., Vatsa, A., Mayham, W., Ngô, L., Thompson, A., Kazic, T.: An opinion on imaging challenges in phenotyping field crops. Mach. Vis. Appl. 27(5), 681–694 (2016). doi:10.1007/s00138-015-0728-4 Larese, M.G., Granitto, P.M.: Finding local leaf vein patterns for legume characterization and classification. Mach. Vis. Appl. 27(5), 709–720 (2016). doi:10.1007/s00138-015-0732-8 Larese, M.G., Granitto, P.M.: Hybrid consensus learning for legume species and cultivars classification. In: L. Agapito, M.M. Bronstein, C. Rother (eds.) Computer Vision—ECCV 2014 Workshops, vol. 8928, pp. 201–214. Springer (2015). doi:10.1007/978-3-319-16220-1_15 Mairhofer, S., Johnson, J., Sturrock, C.J., Bennett, M.J., Mooney, S.J., Pridmore, T.P.: Visual tracking for the recovery of multiple interacting plant root systems from X-ray \(\mu \)CT images. Mach. Vis. Appl. 27(5), 721–734 (2016). doi:10.1007/s00138-015-0733-7 Mairhofer, S., Sturrock, C.J., Bennett, M.J., Mooney, S.J., Pridmore, T.P.: Visual object tracking for the extraction of multiple interacting plant root systems. In: L. Agapito, M.M. Bronstein, C. Rother (eds.) Computer Vision—ECCV 2014 Workshops, vol. 8928, pp. 89–104. Springer (2015). doi:10.1007/978-3-319-16220-1_7 Minervini, M., Fischbach, A., Scharr, H., Tsaftaris, S.A.: Finely-grained annotated datasets for image-based plant phenotyping. Pattern Recognit. Lett. (2015). doi:10.1016/j.patrec.2015.10.013 Minervini, M., Scharr, H., Tsaftaris, S.A.: Image analysis: the new bottleneck in plant phenotyping [Applications Corner]. IEEE Signal Processing Magazine 32(4), 126–131 (2015). doi:10.1109/MSP.2015.2405111 Othmani, A.A., Jiang, C., Lomenie, N., Favreau, J.M., Piboule, A., Voon, L.F.C.L.Y.: A novel computer-aided tree species identification method based on burst wind segmentation of 3d bark textures. Mach. Vis. Appl. 27(5), 751–766 (2016). doi:10.1007/s00138-015-0738-2 Pound, M.P., French, A.P., Fozard, J.A., Murchie, E.H., Pridmore, T.P.: A patch-based approach to 3d plant shoot phenotyping. Mach. Vis. Appl. 27(5), 767–779 (2016). doi:10.1007/s00138-016-0756-8 Pound, M.P., French, A.P., Murchie, E.H., Pridmore, T.P.: Surface reconstruction of plant shoots from multiple views. In: L. Agapito, M.M. Bronstein, C. Rother (eds.) Computer Vision—ECCV 2014 Workshops, vol. 8928, pp. 158–173. Springer (2015). doi:10.1007/978-3-319-16220-1_12 Santos, T.T., Koenigkan, L.V., Barbedo, J.G.A., Rodrigues, G.C.: 3d plant modeling: localization, mapping and segmentation for plant phenotyping using a single hand-held camera. In: L. Agapito, M.M. Bronstein, C. Rother (eds.) Computer Vision—ECCV 2014 Workshops, vol. 8928, pp. 247–263. Springer (2015). doi:10.1007/978-3-319-16220-1_18 Santos, T.T., Rodrigues, G.C.: Flexible three-dimensional modeling of plants using low- resolution cameras and visual odometry. Mach. Vis. Appl. 27(5), 695–707 (2016). doi:10.1007/s00138-015-0729-3 Scharr, H., Minervini, M., French, A.P., Klukas, C., Kramer, D.M., Liu, X., Luengo, I., Pape, J.M., Polder, G., Vukadinovic, D., Yin, X., Tsaftaris, S.A.: Leaf segmentation in plant phenotyping: a collation study. Mach. Vis. Appl. 27(4), 585–606 (2016). doi:10.1007/s00138-015-0737-3