Non-destructive image-based phenotyping for screening cold tolerance in French marigold (Tagetes patula L.) genotypes

Kanwar Pal Singh

doi:10.56093/ijas.v95i9.156579

Authors

Kanwar Pal Singh Indian Agricultural Research Instiute, New Delhi 110012

https://doi.org/10.56093/ijas.v95i9.156579

Keywords:

Cold tolerance, French marigold, High throughput phenotyping, Infrared imaging, Near-Infrared, Red-Green-Blue spectra

Abstract

Erratic weather patterns are becoming more prevalent due to climate change, hence breeding for cold-tolerant marigold genotypes has become essential for its successful cultivation. This study emphasizes the use of high-throughput, non-destructive image-based phenotyping techniques, including Red-Green-Blue (RGB), Near-Infrared (NIR), and Infrared (IR) imaging, to capture essential plant traits such as plant area, greenness, water content, and temperature. These technologies were applied to assess cold tolerance during the early reproductive phase of ten French marigold genotypes, grown under both controlled (polyhouse, 30.1°-33.7°/3.4°-3.7°C) and cold stress (open field, 26.4°-28°C/0.8°-1.2°C) winter conditions. Under cold stress, plant area decreased by 1.38-fold, calliper length by 1.07-fold, and compactness by 2.10-fold compared to polyhouse-controlled environment. Convex hull area and circumference reduced by 1.22-fold and 1.05-fold, respectively. Additionally, greenness and plant temperature got decreased by approximately 1.03-fold, roundness by 2.07-fold, and plant water content by 1.44-fold. These results highlight that open conditions significantly reduced the key morpho-physiological traits, with compactness experiencing the highest decline. However, genotypes such as Hisar Beauty and Hisar Jafri exhibited the least reductions in different parameters under cold stress, maintaining higher water content (NIR reflectance, 140.98%) and lower plant surface temperatures (19.06℃) which are indicative of better cold tolerance. This study underscores the potential of non-destructive image-based phenotyping in screening for cold tolerance in marigold breeding programs, offering a promising alternative to traditional screening methods.

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Author Biography

Kanwar Pal Singh, Indian Agricultural Research Instiute, New Delhi 110012

Principal Scientist, Division of Floriculture and Landscaping, IARI, Pusa Campus, New Delhi-110012.

References

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.

Ahmed I, Eramian M, Ovsyannikov I, van der Kamp W, Nielsen K, Duddu H S and Bett K. 2019. Automatic detection and segmentation of lentil crop breeding plots from multi-spectral images captured by UAV-mounted camera. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV) pp 1673–1681.

Anonymous. 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, Pérez‐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.

Awlia M, Nigro A, Fajkus J, Schmoeckel S M, Negrão S, Santelia D and Panzarová K. 2016. High-throughput non-destructive phenotyping of traits that contribute to salinity tolerance in Arabidopsis thaliana. Frontiers in Plant Science 7: 207736.

Ballester C, Jiménez-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.

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.

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.

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): 1809–1822.

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.

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.

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.

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.

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.

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.

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). Indian Journal of Agricultural Sciences, 94(2): 192–97.

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.

Panda Babyrani, Dash S K, Mondal Subhankar, Senapaty Jeetendra, Dash M, Samal K C and Chakraborty K. 2023. Exploring the physiological efficiencies of promising rice (Oryza sativa) accessions for increasing grain yield. Indian Journal of Agricultural Sciences 93 (11): 1180–85.

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.

Percival G C and Henderson A. 2003. An assessment of the freezing tolerance of urban trees using chlorophyll fluorescence. Journal of Horticultural Science and Biotechnology 78(2): 254–60.

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.

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.

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.