Application of non-destructive imaging techniques to evaluate cold tolerance in French marigold (Tagetes patula) genotypes
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Keywords:
Cold tolerance, French marigold, High throughput phenotyping, Infrared imaging, Near-infrared, Red-Green-Blue spectraAbstract
Climate change-induced erratic weather patterns necessitate the development of cold-tolerant marigold cultivars for sustainable floricultural production. The present study was carried out during winter (rabi) season 2021–22 and 2022–23 at ICAR-Indian Agricultural Research Institute, New Delhi to evaluate the efficacy of high-throughput, non-destructive image-based phenotyping techniques, including Red-Green-Blue (RGB), Near-Infrared (NIR), and Infrared (IR) imaging, for quantitative assessment of essential plant traits such as plant area, greenness, water content, and temperature. Ten French marigold (Tagetes patula L.) genotypes (Pusa Deep, Pusa Arpita, Dainty Marietta, Valencia Yellow, Orange Winner, Hisar Beauty, Hisar Jafri, Gulzafri Orange, Fr./W-20, Fr./W-21) were evaluated. The experiment was laid out in a complete randomized design (CRD) with two factors (genotype and environment) and three replications, with 18 plants/environment and 6 plants/replication. Technologies were applied to assess cold tolerance during the early reproductive phase of French marigold genotypes, grown under contrasting environments: Controlled conditions (polyhouse, 30.1°-33.7°C/3.4°-3.7°C) and cold stress (open field, 26.4°-28°C/0.8°-1.2°C) during winter season. Comparative analysis revealed that cold stress significantly impacted morpho-physiological parameters: Plant area decreased by 1.38-fold, caliper length by 1.07-fold, and compactness by 2.10-fold compared to the polyhouse environment. Convex hull area and circumference were reduced by 1.22-fold and 1.05-fold, respectively. Additionally, greenness and plant temperature decreased by approximately 1.03-fold, roundness by 2.07-fold, and plant water content by 1.44-fold. Statistical analysis revealed that open field conditions significantly decreased all measured morpho-physiological parameters, with plant compactness showing the greatest reduction compared to controlled conditions. Notably, genotypes including ‘Hisar Beauty’ and ‘Hisar Jafri ’ exhibited superior cold tolerance, demonstrating the least reductions in measured parameters under cold stress, while maintaining higher water content (NIR reflectance, 140.98%) and lower plant surface temperatures (19.06°C) compared to other genotypes. These findings underscore the potential of non-destructive image-based phenotyping as an efficient tool in screening for cold tolerance in marigold breeding programmes, offering a viable and precise alternative to traditional screening methods for accelerated cultivar development.
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References
Abro A A, Qasim M, Abbas M, Muhammad N, Ali I, Khalid S and Liu F. 2025. Integrating physiological and molecular insights in cotton under cold stress conditions. Genetic Resources and Crop Evolution 72(3): 2561–91. DOI: https://doi.org/10.1007/s10722-024-02143-8
Acosta-Gamboa L M, Liu S, Langley E, Campbell Z, Castro-Guerrero N, Mendoza-Cozatl D and Lorence A. 2016. Moderate to severe water limitation differentially affects the phenome and ionome of Arabidopsis. Functional Plant Biology 44(1): 94–106. DOI: https://doi.org/10.1071/FP16172
Ali B, Kumar S, Sui X, Niu J, Yang J, Zheng M and Li H. 2025. Exogenous acetylsalicylic acid mitigates cold stress in common bean seedlings by enhancing antioxidant defense and photosynthetic efficiency. Frontiers in Plant Science 16: 1589706. DOI: https://doi.org/10.3389/fpls.2025.1589706
Anonymous. 2024. Area and production of horticulture crops for 2023–24 (First Advance Estimates), Ministry of Agriculture and Farmers’ Welfare, Government of India, New Delhi. 2024. Arvidsson S, Perez-Rodríguez P and Mueller-Roeber B. 2011. A growth phenotyping pipeline for Arabidopsis thaliana integrating image analysis and rosette area modelling for robust quantification of genotype effects. New Phytologist 191(3): 895–907. DOI: https://doi.org/10.1111/j.1469-8137.2011.03756.x
Awlia M, Nigro A, Fajkus J, Schmoeckel S M, Negrao S, Santelia D and Panzarova K. 2016. High-throughput non-destructive phenotyping of traits that contribute to salinity tolerance in Arabidopsis thaliana. Frontiers in Plant Science 7: 207736. DOI: https://doi.org/10.3389/fpls.2016.01414
Ballester C, Jimenez-Bello M A, Castel J R and Intrigliolo D S. 2013. Usefulness of thermography for plant water stress detection in citrus and persimmon trees. Agricultural and Forest Meteorology 168: 120–29. DOI: https://doi.org/10.1016/j.agrformet.2012.08.005
Chen D, Neumann K, Friedel S, Kilian B, Chen M, Altmann T and Klukas C. 2014. Dissecting the phenotypic components of crop plant growth and drought responses based on high-throughput image analysis. The Plant Cell 26(12): 4636–55. DOI: https://doi.org/10.1105/tpc.114.129601
Chen D, Shi R, Pape J M, Neumann K, Arend D, Graner A and Klukas C. 2018. Predicting plant biomass accumulation from image-derived parameters. GigaScience 7(2): 1–13. DOI: https://doi.org/10.1093/gigascience/giy001
Choudhary M, Beniwal B S and Kumari A. 2014. Evaluation of marigold genotypes under semi-arid conditions of Haryana. Annals of Horticulture 7(1): 30-5.
Das B, Nair B, Reddy V K and Venkatesh P. 2018. Evaluation of multiple linear, neural network and penalised regression models for prediction of rice yield based on weather parameters for west coast of India. International Journal of Biometeorology 62(10): 1809–22. DOI: https://doi.org/10.1007/s00484-018-1583-6
Godoy-Hernandez G and Miranda-Ham M L. 2007. Marigold biotechnology: Tissue culture and genetic transformation. Transgenic Plant Journal 1(1): 169–74.
Goharrizi K J, Meru G, Kermani S G, Heidarinezhad A and Salehi F. 2021. Short-term cold stress affects physiological and biochemical traits of pistachio rootstocks. South African Journal of Botany 141: 90–98. DOI: https://doi.org/10.1016/j.sajb.2021.04.029
Golzarian M R, Frick R A, Rajendran K, Berger B, Roy S, Tester M and Lun D S. 2011. Accurate inference of shoot biomass from high-throughput images of cereal plants. Plant Methods 7(2): 1–11. DOI: https://doi.org/10.1186/1746-4811-7-2
Granier C, Aguirrezabal L, Chenu K, Cookson S J, Dauzat M, Hamard P and Tardieu F. 2006. PHENOPSIS, an automated platform for reproducible phenotyping of plant responses to soil water deficit in Arabidopsis thaliana permitted the identification of an accession with low sensitivity to soil water deficit. New Phytologist 169(3): 623–35. DOI: https://doi.org/10.1111/j.1469-8137.2005.01609.x
Hairmansis A, Berger B, Tester M and Roy S J. 2014. Image-based phenotyping for non-destructive screening of different salinity tolerance traits in rice. Rice 7(16): 1–10. DOI: https://doi.org/10.1186/s12284-014-0016-3
James R A and Sirault X R. 2012. Infrared thermography in plant phenotyping for salinity tolerance. Plant Salt Tolerance: Methods and Protocols 913: 173–89. DOI: https://doi.org/10.1007/978-1-61779-986-0_11
Kim S L, Kim N, Lee H, Lee E, Cheon K S, Kim M and Kim K H. 2020. High-throughput phenotyping platform for analysing drought tolerance in rice. Planta 252(3): 1–18. DOI: https://doi.org/10.1007/s00425-020-03436-9
Kumar, M N, Jain R, Singh M C, Tiwari A K, Singh B, Sethi S and Madhavi K. 2024. Evaluation of packaging materials for enhancing the storage life of marigold flowers (Tagetes erecta). The Indian Journal of Agricultural Sciences 94(2): 192–97. DOI: https://doi.org/10.56093/ijas.v94i2.142634
Mazis A, Choudhury S D, Morgan P B, Stoerger V, Hiller J, Ge Y and Awada T. 2020. 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: 1–12. DOI: https://doi.org/10.1016/j.foreco.2020.118101
Panda Babyrani, Dash S K, Mondal S, Senapaty J, Dash M, Samal K C and Chakraborty K. 2023. Exploring the physiological efficiencies of promising rice (Oryza sativa) accessions for increasing grain yield. The Indian Journal of Agricultural Sciences 93(11): 1180–85. DOI: https://doi.org/10.56093/ijas.v93i11.140727
Pappula-Reddy S P, Kumar S, Pang J, Chellapilla B, Pal M, Millar A H and Siddique K H. 2024. High-throughput phenotyping for terminal drought stress in chickpea (Cicer arietinum L.). Plant Stress 11: 1–12. DOI: https://doi.org/10.1016/j.stress.2024.100386
Singh B, Kumar S, Elangovan A, Vasht D, Arya S, Duc N T and Chinnusamy V. 2023. Phenomics based prediction of plant biomass and leaf area in wheat using machine learning approaches. Frontiers in Plant Science 14: 1–16. DOI: https://doi.org/10.3389/fpls.2023.1214801
Wang M, Dong D, Zheng W, Jiao L, Zhao X and Zhao C. 2013. Using infrared sensor for large area canopy total temperature measurements of rice plants. Applied Engineering in Agriculture 29(1): 115–22. DOI: https://doi.org/10.13031/2013.42524
Yang W, Guo Z, Huang C, Wang K, Jiang N, Feng H and Xiong L. 2015. Genome-wide association study of rice (Oryza sativa L.) leaf traits with a high-throughput leaf scorer. Journal of Experimental Botany 66(18): 5605–15. DOI: https://doi.org/10.1093/jxb/erv100
Zheng X W, Cao X Y, Jiang W H, Xu G Z, Liang Q Z and Yang Z Y. 2024. Cryoprotectant-mediated cold stress mitigation in litchi flower development: transcriptomic and metabolomic perspectives. Metabolites 14(4): 223. DOI: https://doi.org/10.3390/metabo14040223
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