Abad P, Gouzy J, Aury JM, Castagnone-Sereno P, Danchin EGJ, Deleury E, et al. Genome sequence of the metazoan plant-parasitic nematode Meloidogyne incognita. Nat Biotechnol. 2008;26:909–15.
Article
CAS
Google Scholar
Sasser JN. A world perspective on nematology: the role of the society. Vistas Nematol. 1987;7–14.
Chitwood DJ. Research on plant-parasitic nematode biology conducted by the United States Department of Agriculture-Agricultural Research Service. Pest Manag Sci. 2003;59:748–53. https://doi.org/10.1002/ps.684.
Article
CAS
Google Scholar
Savary S, Willocquet L, Pethybridge SJ, Esker P, McRoberts N, Nelson A. The global burden of pathogens and pests on major food crops. Nat Ecol Evol. 2019;3:430–9.
Article
Google Scholar
Atkinson HJ, Urwin PE, Hansen E, McPherson MJ. Designs for engineered resistance to root-parasitic nematodes. Trends Biotechnol. 1995;13:369–74.
Article
CAS
Google Scholar
Bleve-Zacheo T, Zacheo G. Cytological studies of the susceptible reaction of sugarbeet roots to Heterodera schachtii. Physiol Mol Plant Pathol. 1987;30:13–25.
Article
Google Scholar
Wyss U. Observations on the feeding behaviour of Heterodera schachtii throughout development, including events during moulting. Fundam Appl Nematol. 1992;15(1):75–89.
Google Scholar
Wyss U, Zunke U. Observations on the behaviour of second stage juveniles of Hetero inside host roots. Rev Nematol. 1986;9:153–65.
Google Scholar
Bleve-Zacheo T, Rubino L, Melillo MT, Russo AM. The 33K protein encoded by cymbidium ringspot tombusvirus localizes to modified peroxisomes of infected cells and of uninfected transgenic plants. J Plant Pathol. 1997;197–202.
Gheysen G, Fenoll C. Gene expression in nematode feeding sites. Annu Rev Phytopathol. 2002;40:191–219.
Article
CAS
Google Scholar
Gray JE, Picton S, Giovannoni JJ, Grierson D. The use of transgenic and naturally occurring mutants to understand and manipulate tomato fruit ripening. Plant Cell Environ. 1994;17:557–71.
Article
CAS
Google Scholar
Anjam MS, et al. Host factors influence the sex of nematodes parasitizing roots of Arabidopsis thaliana. Plant Cell Environ. 2020;43(5):1160–74.
Article
CAS
Google Scholar
Hu W, Strom N, Haarith D, Chen S, Bushley KE. Mycobiome of cysts of the soybean cyst nematode under long term crop rotation. Front Microbiol. 2018;9:386.
Article
Google Scholar
Radakovic ZS, Anjam MS, Escobar E, Chopra D, Cabrera J, Silva AC, et al. Arabidopsis HIPP27 is a host susceptibility gene for the beet cyst nematode Heterodera schachtii. Mol Plant Pathol. 2018. https://doi.org/10.1111/mpp.12668.
Article
Google Scholar
Sohrabi S, Mor DE, Kaletsky R, Keyes W, Murphy CT. High-throughput behavioral screen in C. elegans reveals Parkinson’s disease drug candidates. Commun Biol 2021; 4:1–9. Available from: https://www.nature.com/articles/s42003-021-01731-z
Bates K, Le K, Lu H. Deep learning for robust and flexible tracking in behavioral studies for C. elegans. PLOS Comput Biol. 2022;18:e1009942. https://doi.org/10.1371/journal.pcbi.1009942.
Article
CAS
Google Scholar
Hebert L, Ahamed T, Costa AC, O’Shaughnessy L, Stephens GJ. WormPose: Image synthesis and convolutional networks for pose estimation in C. elegans. PLOS Comput Biol. 2021;17:e1008914. https://doi.org/10.1371/journal.pcbi.1008914.
Article
CAS
Google Scholar
Czedik-Eysenberg A, Seitner S, Güldener U, Koemeda S, Jez J, Colombini M, et al. The ‘PhenoBox’, a flexible, automated, open-source plant phenotyping solution. New Phytol. 2018;219:808–23. https://doi.org/10.1111/nph.15129.
Article
Google Scholar
Colmer J, O’Neill CM, Wells R, Bostrom A, Reynolds D, Websdale D, et al. SeedGerm: a cost-effective phenotyping platform for automated seed imaging and machine-learning based phenotypic analysis of crop seed germination. New Phytol. 2020;228:778–93. https://doi.org/10.1111/nph.16736.
Article
CAS
Google Scholar
Reynolds D, Ball J, Bauer A, Davey R, Griffiths S, Zhou J. CropSight: a scalable and open-source information management system for distributed plant phenotyping and IoT-based crop management. Gigascience 2019; 8:1–11. https://academic.oup.com/gigascience/article/8/3/giz009/5304887
Panjvani K, Dinh AV, Wahid KA. LiDARPheno—a low-cost LiDAR-based 3D scanning system for leaf morphological trait extraction. Front Plant Sci. 2019;10:147.
Article
Google Scholar
Lien MR, Barker RJ, Ye Z, Westphall MH, Gao R, Singh A, et al. A low-cost and open-source platform for automated imaging. Plant Methods. 2019;15:1–14. https://doi.org/10.1186/s13007-019-0392-1.
Article
Google Scholar
Zhou J, Applegate C, Alonso AD, Reynolds D, Orford S, Mackiewicz M, et al. Leaf-GP: An open and automated software application for measuring growth phenotypes for arabidopsis and wheat. Plant Methods. 2017;13:1–17. https://doi.org/10.1186/s13007-017-0266-3.
Article
CAS
Google Scholar
Halcro K, McNabb K, Lockinger A, Socquet-Juglard D, Bett KE, Noble SD. The BELT and phenoSEED platforms: Shape and colour phenotyping of seed samples. Plant Methods. 2020;16:1–13. https://doi.org/10.1186/s13007-020-00591-8.
Article
Google Scholar
Pound MP, Fozard S, Torres Torres M, Forde BG, French AP. AutoRoot: open-source software employing a novel image analysis approach to support fully-automated plant phenotyping. Plant Methods. 2017;13:1–10. https://doi.org/10.1186/s13007-017-0161-y.
Article
Google Scholar
Valle B, Simonneau T, Boulord R, Sourd F, Frisson T, Ryckewaert M, et al. PYM: A new, affordable, image-based method using a Raspberry Pi to phenotype plant leaf area in a wide diversity of environments. Plant Methods. 2017;13:1–17. https://doi.org/10.1186/s13007-017-0248-5.
Article
Google Scholar
Roberts EH. Temperature and seed germination. In: Symposium of the Society for Experimental Biology. 1988. p. 109–32.
Hoagiand DR. Nutrition of strawberry plant under controlled conditions.(a) Effects of deficiencies of boron and certain other elements,(b) susceptibility to injury from sodium salts. In: Proceedings of the American Society for Horticultural Science. 1933. p. 288–94.
W.S Rasband, ImageJ, U. S. National Institutes of Health, Bethesda. ImageJ [Internet]. Maryland, USA. https://imagej.nih.gov/ij/
Legland D, Arganda-Carreras I, Andrey P. MorphoLibJ: Integrated library and plugins for mathematical morphology with ImageJ. Bioinformatics. 2016;32:3532–4.
CAS
Google Scholar
Siddique S, Radakovic ZS, Hiltl C, Pellegrin C, Baum TJ, Beasley H, et al. The genome and lifestage-specific transcriptomes of a plant-parasitic nematode and its host reveal susceptibility genes involved in trans-kingdom synthesis of vitamin B5. Nat Commun 2021 [cited 2022 May 19]; in press.
Reinhard E, Ashikhmin M, Gooch B, Shirley P. Color transfer between images. IEEE Comput Graph Appl. 2001;21:34–41.
Article
Google Scholar
Sacco MA, Koropacka K, Grenier E, Jaubert MJ, Blanchard A, Goverse A, et al. The cyst nematode SPRYSEC protein RBP-1 elicits Gpa2- and RanGAP2-dependent plant cell death. Opperman C, editor. PLoS Pathog 2009;5:e1000564. https://doi.org/10.1371/journal.ppat.1000564
Panella L, Lewellen RT. Broadening the genetic base of sugar beet: introgression from wild relatives. Euphytica. 2006;154:383–400. https://doi.org/10.1007/s10681-006-9209-1.
Article
CAS
Google Scholar
Cai D, Kleine M, Kifle S, Harloff HJ, Sandal NN, Marcker KA, et al. Positional cloning of a gene for nematode resistance in sugar beet. Science (80-). 1997;275:832–4. https://doi.org/10.1126/science.275.5301.832.
Article
CAS
Google Scholar
(Germany) AD-P-NB, 1992 undefined. The effects of imidacloprid on aphids and virus yellows in sugar beet. agris.fao.org [Internet]. [cited 2022 May 22]; https://agris.fao.org/agris-search/search.do?recordID=DE93U0269
Märländer B, Hoffmann C, Koch HJ, Ladewig E, Merkes R, Petersen J, et al. Environmental situation and yield performance of the sugar beet crop in Germany: Heading for sustainable development. J Agron Crop Sci [Internet]. 2003 [cited 2022 May 22];189:201–26. www.blackwell.de/synergy
Radakovic ZS. Identification and characterisation of Heterodera schachtii susceptibility genes AtPANB1 and HIPP27 in Arabidopsis thaliana [Internet]. PHD thesis. Rheinische Friedrich-Wilhelms-Universität Bonn; 2018 [cited 2022 May 23]. https://bonndoc.ulb.uni-bonn.de/xmlui/handle/20.500.11811/7377
Rogers H, Baricz N, Pawar KS. 3D printing services: classification, supply chain implications and research agenda. Int J Phys Distrib Logist Manag. 2016;46:886–907.
Article
Google Scholar
3D People UK | 3D Printing Service | Order Online [Internet]. [cited 2022 May 31]. https://www.3dpeople.uk/
Zhang Q, Van Wijk R, Zarza X, Shahbaz M, Van Hooren M, Guardia A, et al. Knock-down of arabidopsis PLC5 reduces primary root growth and secondary root formation while overexpression improves drought tolerance and causes stunted root hair growth. Plant Cell Physiol. 2018;59:2004–19.
Article
CAS
Google Scholar
Mahony NO, Campbell S, Carvalho A, Harapanahalli S, Velasco-Hernandez G, Krpalkova L, et al. Deep learning vs. traditional computer vision. Arai K, Kapoor S, editors. Cham: Springer International Publishing; 2019 [cited 2022 May 17]. P. 943. http://arxiv.org/abs/1910.13796
Ferentinos KP. Deep learning models for plant disease detection and diagnosis. Comput Electron Agric. 2018;145:311–8.
Article
Google Scholar