Triticale (X Triticosecale Wittmack), a wheat-rye small grain crop hybrid, combines wheat and rye attributes in one hexaploid genome. It is characterized by high adaptability to adverse environmental conditions: drought, soil acidity, salinity and heavy metal ions, poorer soil quality, and waterlogging. So that its cultivation is prospective in a changing climate. Here, we describe RGB on-ground phenotyping of field-grown eighteen triticale market-available cultivars, made in naturally changing light conditions, in two consecutive winter cereals growing seasons: 2018-2019 and 2019-2020. The number of ears was counted on top-down images with an accuracy of 95% and mean average precision (mAP) of 0.71 using advanced object detection algorithm YOLOv4, with ensemble modeling of field imaging captured in two different illumination conditions. A correlation between the number of ears and yield was achieved at the statistical importance of 0.16 for data from 2019. Results are discussed from the perspective of modern breeding and phenotyping bottleneck.

Faculty of Science National Centre for Biomolecular Research Masaryk University Brno Czech Republic

Plant Breeding and Acclimatization Institute National Research Institute IHAR PIB Biochemistry and Biotechnology Department 05 870 Radzików Poland

Plant Breeding Strzelce Ltd Co IHAR Group 99 307 Strzelce Poland

PSI Spol S R O 66424 Drasov Czech Republic

Zobrazit více v PubMed

Alharbi N, Zhou J, Wang W (2018) Automatic counting of wheat spikes from wheat growth images. In: Proceedings of the 7th International Conference on Pattern Recognition Applications and Methods ICPRAM, vol 1. Funchal, Madeira, pp 346–355. https://doi.org/10.5220/0006580403460355

Arseniuk E, Krzymuski J, Martyniak J, Oleksiak T (2003) Breeding and seed production in groups and species of crops. Cereals. In: Krzymuski J (ed) History of crop breeding and seed production in Poland in the XX century (In Polish). Plant Breeding and Acclimatization Institute, Radzików, pp 57–87.

Austin RB (1993) Augmenting yield-based selection. In: Hayward MD, Bosemark NO, Romagosa I, Cerezo M (eds) Plant breeding: principles and prospects, Springer GmbH: Dordrecht, pp 391–405. https://doi.org/10.1007/978-94-011-1524-7_24

Blum A (2005) Drought resistance, water-use efficiency, and yield potential- are they compatible, dissonant, or mutually exclusive? Aust J Agric Res 56:1159–1168. https://doi.org/10.1071/AR05069 DOI

Blum A (2011) Drought resistance– is it really a complex trait? Funct Plant Biol 38:753–757. https://doi.org/10.1071/FP11101 PubMed DOI

Blum A (2014) The abiotic stress response and adaptation of triticale- a review. Cereal Res Com 42:359–375. https://doi.org/10.1556/crc.42.2014.3.1 DOI

Bochkovskiy A, Wang C, Liao HM (2020) YOLOv4: optimal speed and accuracy of object detection. arXiv preprint 2020.  https://doi.org/10.48550/arXiv.2004.10934

Correia PMP, Cairo Westergaard J, Bernardes da Silva A, Roitsch T, Carmo-Silva E, Marques da Silva J (2022) High-throughput phenotyping of physiological traits for wheat resilience to high temperature and drought stress. J Exp Bot 73:5235–5251. https://doi.org/10.1093/jxb/erac160 PubMed DOI PMC

Doroszewski A, Jadczyszyn J, Kozyra J, Pudełko R, Stuczyński T, Mizak K, Łopatka A, Koza P, Górski T, Wróblewska E (2012) Fundamentals of the agricultural drought monitoring system. Woda-Środowisko-Obszary Wiejskie 12: 77–91 (in Polish with English Summary).  https://www.itp.edu.pl/old/wydawnictwo/woda/zeszyt_38_2012/artykuly/Doroszewski%20i%20in.pdf

Drzazga T, Studnicki M (2015) Analiza adaptacji środowiskowej rodów hodowlanych w wielokrotnych, jednorocznych doświadczeniach, na przykładzie plonowania rodów pszenicy ozimej. [Eng.: Evaluation of adaptability to environments of varieties in multiple annual trials: a case study of winter wheat] (in Polish with English Abstract and Tables and Figures description). Biul IHAR. 275:29–38 DOI

Drzazga T, Krajewski P, Śmiałek E (2013) Wykorzystanie różnych poziomów intensywności agrotechniki w hodowli pszenicy ozimej [Eng.: Usefulness of different input level environments in selection of winter wheat] (in Polish with English Abstract and Tables and Figures description). Biuletyn IHAR 270:3–16. https://doi.org/10.37317/biul-2013-0001 DOI

Edwards DC, McKee TB (1997) Characteristics of 20th century drought in the United States at multiple times scales. Atmospheric Science Paper 634, 1–30 May 1997, Colorado State University. Available online: https://mountainscholar.org/handle/10217/170176 . Accessed 12 Jul 2023

EEA (European Environment Agency) (2008) Impacts of Europe’s changing climate. EEA Report No 3/2008. https://www.eea.europa.eu/publications/briefing_2008_3/file . Accessed on 12 Jul 2023

Everingham M, Van Gool L, Williams CKI, Winn J, Zisserman A (2010) The PASCAL visual object classes (VOC) challenge. Int J Comput vis 88:303–338. https://doi.org/10.1007/s11263-009-0275-4 DOI

Feller U, Kingston-Smith A, Centritto M (2017) Abiotic stresses in agroecology: a challenge for whole plant physiology. Frontiers Environ Sci 5:1–4. https://doi.org/10.3389/fenvs.2017.00013 DOI

Gołębiowska G, Dyda M (2023) The genome regions associated with abiotic and biotic stress tolerance, as well as other important breeding traits in triticale. Plants 12:619. https://doi.org/10.3390/plants12030619 DOI

Groisman P, Knight R, Easterling D, Karl TR, Hegerl G, Razuvaev V (2005) Trends in intense precipitation in the climate record. J Climate 18:1326–1350. https://doi.org/10.1175/jcli3339.1 DOI

GUS (2019) Agriculture, Forestry in the 2019 year. Central Statistics Office Thematic areas (in Polish: Obszary tematyczne. Rolnictwo. Leśnictwo w roku 2019) https://bdl.stat.gov.pl/BDL/start . Accessed on 12 Jul 2023

Han L, Yang G, Dai H et al (2019) Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data. Plant Methods 15:10. https://doi.org/10.1186/s13007-019-0394-z PubMed DOI PMC

Hasan MM, Chopin JP, Laga H, Miklavcic SJ (2018) Detection and analysis of wheat spikes using convolutional neural networks. Plant Meth 14:1–13. https://doi.org/10.1186/s13007-018-0366-8 DOI

He K, Gkioxari G, Dollár P, Girshick R (2017) Mask R-CNN. In: 2017 IEEE International Conference on Computer Vision (ICCV), Venice, pp 2961–2969. https://doi.org/10.1109/ICCV.2017.322

Hussain M (2023) yolo-v1 to yolo-v8, the rise of yolo and its complementary nature toward digital manufacturing and industrial defect detection. Machines 11:677. https://doi.org/10.3390/machines11070677 DOI

ITA, International Triticale Association. Triticale History, created at Ghent University, Faculty of Bioscience Engi-neering, Available online: https://triticale.org/triticale-history/ Accessed 12 Jul 2023

Lancashire PD, Bleihilder H, van den Boom T, Langeluddeke P, Stauss R, Weber E, Witzenberger AA (1991) Uniform decimal code for growth stages of crops and weeds. Ann Appl Biol 119:561–601. https://doi.org/10.1111/j.1744-7348.1991.tb04895.x DOI

Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324. https://doi.org/10.1109/5.726791 DOI

Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: hierarchical vision trans-former using shifted windows. In Proceedings of the IEEE/CVF international conference on computer vision, 10012–10022. https://doi.org/10.1109/ICCV48922.2021.00986

Lv F, Lu F, Wu J, Lim C (2018) MBLLEN: low-light image/video enhancement using CNNs. In Proceedings of the BMVC 220(1):4. http://bmvc2018.org/contents/papers/0700.pdf

Matysik P, Nita Z, Matysik E (2007) Skuteczność kryteriów selekcji pszenicy ozimej w pokoleniu F4 na podstawie komponentów plonu [in Polish with English Abstract, Table headers and Figure legends. Eng.: Effectiveness of selection criteria applied in F4 progeny of winter wheat on the basis of yield components]. Biuletyn IHAR 244:99–110 DOI

Mergoum M, Singh PK, Peña R, Lozano A, Cooper KV, Salmon DF, Macpherson H (2009) Triticale: a “new” crop with old challenges. In Carena MJ (ed) Cereals, Springer GmbH pp 267–287.  https://doi.org/10.1007/978-0-387-72297-9_9

Mir RR, Reynolds M, Pinto F, Khan MA, Bhat MA (2019) High-throughput phenotyping for crop improvement in the genomics era. Plant Sci 282:60–72. https://doi.org/10.1016/j.plantsci.2019.1001.1007 PubMed DOI

Misra T, Arora A, Marwaha S, Chinnusamy V, Rao A, Jain R, Sahoo R, Ray M, Kumar S, Raju D, Jha R, Nigam A, Goel S (2020) SpikeSegNet- a deep learning approach utilizing encoder-decoder network with hourglass for spike segmentation and counting in wheat plant from visual imaging. Plant Meth 16(1):40. https://doi.org/10.1186/s13007-020-00582-9 DOI

Moskal K, Kowalik S, Podyma W, Łapiński B, Boczkowska M (2021) The pros and cons of rye chromatin introgression into wheat genome. Agronomy 11:456. https://doi.org/10.3390/agronomy11030456 DOI

Oleksiak T, Spyroglou I, Pacoń D, Matysik P, Pernisova M, Rybka K (2022) Effect of drought on wheat production in Poland between 1961 and 2019. Crop Sci 62:728–743. https://doi.org/10.1002/csc2.20690 DOI

Ren S, He K, Girshick R, Sun J (2015) Faster R-CNN: towards real-time object detection with region proposal networks.  ArXiv./abs/1506.01497

Reynolds M, Foulkes J, Furbank R, Griffiths S, King J, Murchie E, Parry M, Slafer G (2012) Achieving yield gains in wheat. Plant Cell Environ 35:1799–1823. https://doi.org/10.1111/j.1365-3040.2012.02588.x PubMed DOI

Rosenzweig C, Elliott J, Deryng D, Ruane AC, Müller C, Arneth A, Boote KJ, Folberth C, Glotter M, Khabarov N et al (2014) Assessing agricultural risks of climate change in the 21st century in a global gridded crop model intercomparison. Proc Natl Acad Sci USA 111:3268–3273. https://doi.org/10.1073/pnas.1222463110 PubMed DOI

Rozbicki J, Gozdowski D, Studnicki M, Mądry W, Golba J, Sobczyński G, Wijata M (2019) Management intensity effects on grain yield and its quality traits of winter wheat cultivars in different environments in Poland. Electron J Polish Agric Univ 22(1):1. https://doi.org/10.30825/5.ejpau.168.2019.22.1 DOI

Singh G, Chaudhary H (2006) Selection parameters and yield enhancement of wheat (Triticum aestivum L.) under different moisture stress conditions. Asian J Plant Sci 5:894–898. https://doi.org/10.3923/ajps.2006.894.898 DOI

Tang H, Zhu H, Fei L, Wang T, Cao Y, Xie C (2023) Low-illumination image enhancement based on deep learning techniques: a brief review. Photonics 10:198. https://doi.org/10.3390/photonics10020198 DOI

Ullah S, Bramley H, Mahmood T, Trethowan R (2020) A strategy of ideotype development for heat-tolerant wheat. J Agr Crop Sci 206:229–241. https://doi.org/10.1111/jac.12378 DOI