Geodesic
High throughput crop phenotyping systems
News of the Kabardin-Balkar scientific center of RAS, no. 5 (2022), pp. 19-24.

Voir la notice de l'article provenant de la source Math-Net.Ru

In this paper, the analysis of systems of high-performance phenotyping of agricultural crops is carried out. Systems based on mobile robots, unmanned aerial vehicles and software and hardware systems were considered. It is shown that despite the fact that there are ready-made solutions on the market, they do not cover the entire range of tasks.
Keywords: phenotyping, selection, robotics. 
@article{IZKAB_2022_5_a1,
     author = {M. I. Anchekov and K. Ch. Bzhikhatlov and A. M. Leshkenov},
     title = {High throughput crop phenotyping systems},
     journal = {News of the Kabardin-Balkar scientific center of RAS},
     pages = {19--24},
     publisher = {mathdoc},
     number = {5},
     year = {2022},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/IZKAB_2022_5_a1/}
}
TY  - JOUR
AU  - M. I. Anchekov
AU  - K. Ch. Bzhikhatlov
AU  - A. M. Leshkenov
TI  - High throughput crop phenotyping systems
JO  - News of the Kabardin-Balkar scientific center of RAS
PY  - 2022
SP  - 19
EP  - 24
IS  - 5
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/IZKAB_2022_5_a1/
LA  - ru
ID  - IZKAB_2022_5_a1
ER  - 
%0 Journal Article
%A M. I. Anchekov
%A K. Ch. Bzhikhatlov
%A A. M. Leshkenov
%T High throughput crop phenotyping systems
%J News of the Kabardin-Balkar scientific center of RAS
%D 2022
%P 19-24
%N 5
%I mathdoc
%U http://geodesic.mathdoc.fr/item/IZKAB_2022_5_a1/
%G ru
%F IZKAB_2022_5_a1
M. I. Anchekov; K. Ch. Bzhikhatlov; A. M. Leshkenov. High throughput crop phenotyping systems. News of the Kabardin-Balkar scientific center of RAS, no. 5 (2022), pp. 19-24. http://geodesic.mathdoc.fr/item/IZKAB_2022_5_a1/

[1] (data obrascheniya 31.10.2022 g.) https://www.un.org/ru/global-issues/population

[2] T. Mueller-Sim, “The Robotanist: A ground-based agricultural robot for high-throughput crop phenotyping”, IEEE International Conference on Robotics and Automation (ICRA), 2017 | DOI

[3] M. A. Fischler, R. C. Bolles, “Random sample consensus”, Commun. ACM, 24:6 (1981), 381–395 | DOI | MR

[4] R. Coulter, Implementation of the pure pursuit path tracking algorithm, Tech. Rep., Carnegie Mellon University Robotics Institute, 1992 (January)

[5] F. Baret, B. de Solan, S. Thomas [et al.], Phenomobile: A fully automatic robot for highthroughput field phenotyping of a large range of crops with active measurements, 2022 (April) https://www.robopec.com/wp-content/uploads/2020/08/IAMPS_Phenomobile.pdf

[6] S. Madec, F. Baret, B. de Solan [et al.], “High-throughput phenotyping of plant height: comparing unmanned aerial vehicles and ground lidar estimates”, Frontiers in Plant Science, 2017, no. 8 | DOI

[7] L. Volpato [et al.], “High Throughput Field Phenotyping for Plant Height Using UAV-Based RGB Imagery in Wheat Breeding Lines: Feasibility and Validation”, Front. Plant Sci, 12 (2021) | DOI | MR

[8] W. Chivasa, O. Mutanga, J. Burguen'o, “UAV-based high-throughput phenotyping to increase prediction and selection accuracy in maize varieties under artificial MSV inoculation”, Computers and Electronics in Agriculture, 184 (2021) | DOI | MR

[9] W. Su [et al.], “Phenotyping of Corn Plants Using Unmanned Aerial Vehicle (UAV) Images”, Remote Sensing, 11:17 (2019) | DOI

[10] Ma. L. Buchaillot [et al.], “Evaluating Maize Genotype Performance under Low Nitrogen Conditions Using RGB UAV Phenotyping Techniques”, Sensors, 19:8 (2019) | DOI

[11] A. Arunachalam, H. Andreasson, “Real-time plant phenomics under robotic farming setup: A vision-based platform for complex plant phenotyping tasks”, Computers Engineering, 92 (2021) | DOI

[12] M. A. Gehan [et al.], “PlantCV v2: Image analysis software for high-throughput plant phenotyping”, PeerJ, 5 (2017) | DOI

[13] C. T. Rueden [et al.], “ImageJ2: ImageJ for the next generation of scientific image data”, BMC Bioinformatics, 18:1 (2017) | DOI

[14] C. Klukas, D. Chen, J. M. Pape, “Integrated Analysis Platform: An Open-Source Information System for High-Throughput Plant Phenotyping”, Plant Physiology, 165:2 (2014), 506–518 | DOI | MR

[15] J. De Vylder [et al.], “Rosette Tracker: An Open Source Image Analysis Tool for Automatic Quantification of Genotype Effects”, Plant Physiology, 160:3 (2012), 1149–1159 https://www.jstor.org/stable/41693984 | DOI

[16] J. R. Ubbens, I. Stavness, “Deep Plant Phenomics: A Deep Learning Platform for Complex Plant Phenotyping Tasks”, Front. Plant Sci, 8 (2017) | DOI

[17] F. Apelt [et al.], “Phytotyping 4D: a light-field imaging system for non-invasive and accurate monitoring of spatio-temporal plant growth”, Plant J, 82:4 (2015), 693–706 | DOI

[18] C. Zhang [et al.], “3D Robotic System Development for High-throughput Crop Phenotyping”, IFAC-PapersOnLine, 49:16 (2016), 242–247 | DOI | MR

[19] A. Mazis [et al.], “Application of high-throughput plant phenotyping for assessing biophysical traits and drought response in two oak species under controlled environment”, Forest Ecology and Management, 465 (2020), 118101 | DOI | MR

[20] (data obrascheniya 31.10.2022 g.) https://phenospex.com/

[21] E. N. Rakutko, “Determination of the crown structure of plants during their automated phenotyping”, Technologies and technical means of mechanized production of crop and livestock products, 2020, no. 2 (103), 44–57 (In Russian) | DOI

[22] M. Ya. Braginsky, D. V. Tarakanov, “Plant phenotyping by an adaptive image processing system based on convolutional neural networks”, Publishing Center of SurSU, 2021, no. 2 (42), 6–16 (In Russian) | DOI | MR

[23] F. Rockel [et al.], “PhenoApp: A mobile tool for plant phenotyping to record field and greenhouse observations”, F1000Res, 11 (2022), 12 | DOI