Developing crop varieties that maintain productivity under drought is essential for future food security. Here, we investigated the potential of time-resolved high-throughput phenotyping to predict harvest-related traits and identify drought-stressed plants. Six barley lines (Hordeum vulgare) were grown in a greenhouse environment with well-watered and drought treatments, and dynamically phenotyped using RGB, thermal infrared, chlorophyll fluorescence, and hyperspectral imaging sensors. A temporal phenomic classification model accurately distinguished between drought-treated and control plants, achieving high accuracy (classification accuracy ≥0.97) even when relying solely on predictors from the early drought response phase. Canopy temperature depression at the early stage and RGB-derived plant size estimates at the late stage emerged as key classification features. A temporal phenomic prediction model of harvest-related traits achieved particularly high mean R2 values for total biomass dry weight (0.97) and total spike weight (0.93), with RGB plant size estimators emerging as important predictors. Importantly, prediction accuracy for these traits remained high (R2 ≥ 0.84) even when restricted to early developmental phase data, including the stem elongation stage. Models trained on pooled drought and control data outperformed single-treatment models and maintained high predictive power across treatments. Together, these findings highlight the value of integrating high-throughput phenotyping with temporal modeling to enable earlier, more cost-effective selection of drought-resilient genotypes and demonstrate the broader potential of phenomics-driven strategies for accelerating crop improvement under stress-prone environments.

Bioinformatics Department Institute of Biochemistry and Biology University of Potsdam Potsdam Germany

Institute of Plant Sciences Agricultural Research Organization Bet Dagan Israel

PSI spol s r o Drásov Czechia

Systems Biology and Mathematical Modeling Max Planck Institute of Molecular Plant Physiology Potsdam Germany

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Abdelhakim L. O. A., Pleskačová B., Rodriguez-Granados N. Y., Sasidharan R., Perez-Borroto L. S., Sonnewald S., et al. (2024). High throughput image-based phenotyping for determining morphological and physiological responses to single and combined stresses in potato. JoVE 208, e66255. doi:  10.3791/66255, PMID: PubMed DOI

Adak A., Murray S. C., Anderson S. L. (2023). Temporal phenomic predictions from unoccupied aerial systems can outperform genomic predictions. G3: Genes Genomes Genet. 13 (1), jkac294. doi:  10.1093/g3journal/jkac294, PMID: PubMed DOI PMC

Al-Tamimi N., Langan P., Bernád V., Walsh J., Mangina E., Negrão S. (2022). Capturing crop adaptation to abiotic stress using image-based technologies. Open Biol. 12, 210353. doi:  10.1098/rsob.210353, PMID: PubMed DOI PMC

Anderson M. J. (2017). Permutational multivariate analysis of variance (PERMANOVA). In: Wiley StatsRef: Statistics Reference Online, Balakrishnan N., Colton T., Everitt B., Piegorsch W., Ruggeri F., Teugels J. L., John Wiley & Sons, Ltd., Hoboken, NJ, 1–15. doi:  10.1002/9781118445112.stat07841 DOI

Biju S., Fuentes S., Gupta D. (2018). The use of infrared thermal imaging as a non-destructive screening tool for identifying drought-tolerant lentil genotypes. Plant Physiol. Biochem. 127, 11–24. doi:  10.1016/j.plaphy.2018.03.005, PMID: PubMed DOI

Cai K., Chen X., Han Z., Wu X., Zhang S., Li Q., et al. (2020). Screening of worldwide barley collection for drought tolerance: the assessment of various physiological measures as the selection criteria. Front. Plant Sci. 11. doi:  10.3389/fpls.2020.01159, PMID: PubMed DOI PMC

Chen D., Neumann K., Friedel S., Kilian B., Chen M., Altmann T., et al. (2014). Dissecting the phenotypic components of crop plant growthand drought responses based on high-throughput image analysis w open. Plant Cell 26, 4636–4655. doi:  10.1105/tpc.114.129601, PMID: PubMed DOI PMC

Cooper M., Messina C. D. (2023). Breeding crops for drought-Affected environments and improved climate resilience. Plant Cell 35 (1), 162–186. doi:  10.1093/plcell/koac321, PMID: PubMed DOI PMC

Debbagh M., Sun S., Lefsrud M. (2025). Predictive modeling, pattern recognition, and spatiotemporal representations of plant growth in simulated and controlled environments: A comprehensive review. Plant Phenomics 7 (3), 1–18. doi:  10.1016/j.plaphe.2025.100089 DOI

FAO (2023). Agricultural production statistics 2000–2022. FAOSTAT Analytical Briefs, No. 79. Rome: FAO. doi:  10.4060/cc9205en DOI

Farooq M., Wahid A., Zahra N., Hafeez M. B., Siddique K. H. M. (2024). Recent advances in plant drought tolerance. J. Plant Growth Regul. 43 (10), 3337–3369. doi:  10.1007/s00344-024-11351-6 DOI

Findurová H., Veselá B., Panzarová K., Pytela J., Trtílek M., Klem K. (2023). Phenotyping drought tolerance and yield performance of barley using a combination of imaging methods. Environ. Exp. Bot. 209, 105314. doi:  10.1016/J.ENVEXPBOT.2023.105314 DOI

Ghojogh B., Crowley M. (2023). The theory behind overfitting, cross validation, regularization, bagging, and boosting: tutorial. ArXiv 1905, 12787. doi:  10.48550/arXiv.1905.12787 DOI

Gill T., Gill S. K., Saini D. K., Chopra Y., de Koff J. P., Sandhu K. S. (2022). A comprehensive review of high throughput phenotyping and machine learning for plant stress phenotyping. Phenomics 2, 156–183. doi:  10.1007/s43657-022-00048-z, PMID: PubMed DOI PMC

Grömping U. (2009). Variable importance assessment in regression: linear regression versus random forest. Am. Statistician 63, 308–319. doi:  10.1198/tast.2009.08199 DOI

Heuermann M. C., Knoch D., Junker A., Altmann T. (2023). Natural plant growth and development achieved in the IPK PhenoSphere by dynamic environment simulation. Nat. Commun. 14 (1), 5783. doi:  10.1038/s41467-023-41332-4, PMID: PubMed DOI PMC

Hobby D., Tong H., Heuermann M., Mbebi A. J., Laitinen R. A. E., Dell’Acqua M., et al. (2025). Predicting plant trait dynamics from genetic markers. Nature. 11, 1018–1027. doi:  10.1038/s41477-025-01986-y, PMID: PubMed DOI PMC

HÜbner S., HÖffken M., Oren E., Haseneyer G., Stein N., Graner A., et al. (2009). Strong correlation of wild barley (Hordeum spontaneum) population structure with temperature and precipitation variation. Mol. Ecol. 18, 1523–1536. doi:  10.1111/j.1365-294X.2009.04106.x, PMID: PubMed DOI

Humplík J. F., Lazár D., Husičková A., Spíchal L. (2015). Automated phenotyping of plant shoots using imaging methods for analysis of plant stress responses - a review. Plant Methods 11, 1–10. doi:  10.1186/s13007-015-0072-8, PMID: PubMed DOI PMC

IPCC (2021). Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Masson-Delmotte V., Zhai P., Pirani A., Connors S. L., Péan C., Berger S., et al. (eds.). Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 2391 pp. doi:  10.1017/9781009157896 DOI

Jarquin D., de Leon N., Romay C., Bohn M., Buckler E. S., Ciampitti I., et al. (2021). Utility of climatic information via combining ability models to improve genomic prediction for yield within the genomes to fields maize project. Front. Genet. 11. doi:  10.3389/fgene.2020.592769, PMID: PubMed DOI PMC

Khadka K., Earl H. J., Raizada M. N., Navabi A. (2020). A physio-morphological trait-based approach for breeding drought tolerant wheat. Front. Plant Sci. 11). doi:  10.3389/fpls.2020.00715, PMID: PubMed DOI PMC

Langstroff A., Heuermann M. C., Stahl A., Junker A. (2022). Opportunities and limits of controlled-environment plant phenotyping for climate response traits. In Theor. Appl. Genet. 135, 1–16. doi:  10.1007/s00122-021-03892-1, PMID: PubMed DOI PMC

Lawson T., Blatt M. R. (2014). Stomatal size, speed, and responsiveness impact on photosynthesis and water use efficiency. Plant Physiol. 164, 1556–1570. doi:  10.1104/pp.114.237107, PMID: PubMed DOI PMC

Leonelli S., Davey R. P., Arnaud E., Parry G., Bastow R. (2017). Data management and best practice for plant science. Nat. Plants 3 (6), 1–4. doi:  10.1038/nplants.2017.86, PMID: PubMed DOI

Li Z., Guo R., Li M., Chen Y., Li G. (2020). A review of computer vision technologies for plant phenotyping. Comput. Electron. Agric. 176, 1–21. doi:  10.1016/j.compag.2020.105672 DOI

Mbebi A. J., Mercado F., Hobby D., Tong H., Nikoloski Z. (2025). Advances in multi-trait genomic prediction approaches: classification, comparative analysis, and perspectives. Briefings in Bioinformatics, 26 (3), 1–16. doi:  10.1093/bib/bbaf211, PMID: PubMed DOI PMC

Neumann K., Klukas C., Friedel S., Rischbeck P., Chen D., Entzian A., et al. (2015). Dissecting spatiotemporal biomass accumulation in barley under different water regimes using high-throughput image analysis. Plant Cell Environ. 38, 1980–1996. doi:  10.1111/pce.12516, PMID: PubMed DOI

Newton A. C., Flavell A. J., George T. S., Leat P., Mullholland B., Ramsay L., et al. (2011). Crops that feed the world 4. Barley: a resilient crop? Strengths and weaknesses in the context of food security. Food Secur. 3, 141–178. doi:  10.1007/s12571-011-0126-3 DOI

Pham A. T., Maurer A., Pillen K., Brien C., Dowling K., Berger B., et al. (2019). Genome-wide association of barley plant growth under drought stress using a nested association mapping population. BMC Plant Biol. 19 (1), 134. doi:  10.1186/s12870-019-1723-0, PMID: PubMed DOI PMC

Qiao M., Hong C., Jiao Y., Hou S., Gao H. (2024). Impacts of drought on photosynthesis in major food crops and the related mechanisms of plant responses to drought. Plants 13 (13), 1808. doi:  10.3390/plants13131808, PMID: PubMed DOI PMC

Rahaman M. M., Chen D., Gillani Z., Klukas C., Chen M. (2015). Advanced phenotyping and phenotype data analysis for the study of plant growth and development. Front. Plant Sci. 6, 619. doi:  10.3389/fpls.2015.00619, PMID: PubMed DOI PMC

Rayaprolu L., Jayasankar K., Aarts M. G. M., Harbinson J. (2025). Photosynthetic variation for climate-resilient crops: photosynthetic responses to fluctuating light and chilling in tomato. Physiol. Plantarum 177 (3), e70241. doi:  10.1111/ppl.70241, PMID: PubMed DOI PMC

Rosati A., Benincasa P. (2023). Revisiting source versus sink limitations of wheat yield during grain filling. Agron. J. 115, 3197–3205. doi:  10.1002/agj2.21454 DOI

Shi R., López-Malvar A., Knoch D., Tschiersch H., Heuermann M. C., Shaaf S., et al. (2025). Integrating high-throughput phenotyping and genome-wide association analyses to unravel Mediterranean maize resilience to combined drought and high temperatures. Plant Stress 17, 1–14. doi:  10.1016/j.stress.2025.100954 DOI

Shin Y. K., Bhandari S. R., Jo J. S., Song J. W., Lee J. G. (2021). Effect of drought stress on chlorophyll fluorescence parameters, phytochemical contents, and antioxidant activities in lettuce seedlings. Horticulturae 7 (8), 238. doi:  10.3390/horticulturae7080238 DOI

Singh A., Ganapathysubramanian B., Singh A. K., Sarkar S. (2016). Machine learning for high-throughput stress phenotyping in plants. Trends Plant Sci. 21, 110–124. doi:  10.1016/j.tplants.2015.10.015, PMID: PubMed DOI

Singh A., Jones S., Ganapathysubramanian B., Sarkar S., Mueller D., Sandhu K., et al. (2021). Challenges and opportunities in machine-augmented plant stress phenotyping. Trends Plant Sci. 26, 53–69. doi:  10.1016/j.tplants.2020.07.010, PMID: PubMed DOI

Song P., Wang J., Guo X., Yang W., Zhao C. (2021). High-throughput phenotyping: Breaking through the bottleneck in future crop breeding. Crop J. 9, 633–645. doi:  10.1016/j.cj.2021.03.015 DOI

Stekhoven D. J., Bühlmann P. (2011). MissForest—non-parametric missing value imputation for mixed-type data. Bioinformatics 28, 112–118. doi:  10.1093/bioinformatics/btr597, PMID: PubMed DOI

Tardieu F., Cabrera-Bosquet L., Pridmore T., Bennett M. (2017). Plant phenomics, from sensors to knowledge. Curr. Biol. 27, R770–R783. doi:  10.1016/j.cub.2017.05.055, PMID: PubMed DOI

Varshney R. K., Barmukh R., Roorkiwal M., Qi Y., Kholova J., Tuberosa R., et al. (2021). Breeding custom-designed crops for improved drought adaptation. Adva. Genet. 2 (3), e202100017. doi:  10.1002/ggn2.202100017, PMID: PubMed DOI PMC

Vico G., Tang F. H. M., Brunsell N. A., Crews T. E., Katul G. G. (2023). Photosynthetic capacity, canopy size and rooting depth mediate response to heat and water stress of annual and perennial grain crops. Agric. For. Meteorol. 341, 109666. doi:  10.1016/J.AGRFORMET.2023.109666 DOI

Way D. A., Yamori W. (2014). Thermal acclimation of photosynthesis: On the importance of adjusting our definitions and accounting for thermal acclimation of respiration. Photosynthesis Res. 119, 89–100. doi:  10.1007/s11120-013-9873-7, PMID: PubMed DOI

Wen T., Li J. H., Wang Q., Gao Y. Y., Hao G. F., Song B. A. (2023). Thermal imaging: The digital eye facilitates high-throughput phenotyping traits of plant growth and stress responses. Sci. Total Environ. 899, 165626. doi:  10.1016/J.SCITOTENV.2023.165626, PMID: PubMed DOI

Xu R., Ferguson J. N., Kromdijk J., Nikoloski Z. (2025). Generalizability of machine learning models for plant traits using hyperspectral reflectance data: The case of maize. bioRxiv. 2025.07.03.661070, 1–49. doi:  10.1017/9781009157896 DOI

Xu Y., Zhang X., Li H., Zheng H., Zhang J., Olsen M. S., et al. (2022). Smart breeding driven by big data, artificial intelligence, and integrated genomic-enviromic prediction. Mol. Plant 15, 1664–1695. doi:  10.1016/j.molp.2022.09.001, PMID: PubMed DOI

Yang W., Feng H., Zhang X., Zhang J., Doonan J. H., Batchelor W. D., et al. (2020). Crop phenomics and high-throughput phenotyping: past decades, current challenges, and future perspectives. Mol. Plant 13, 187–214. doi:  10.1016/j.molp.2020.01.008, PMID: PubMed DOI

Ying X. (2019). An overview of overfitting and its solutions. J. Physics: Conf. Ser. 1168, 22022. doi:  10.1088/1742-6596/1168/2/022022 DOI

Zhang Z., Qu Y., Ma F., Lv Q., Zhu X., Guo G., et al. (2024). Integrating high-throughput phenotyping and genome-wide association studies for enhanced drought resistance and yield prediction in wheat. New Phytol. 243, 1758–1775. doi:  10.1111/nph.19942, PMID: PubMed DOI

Zhou R., Kan X., Chen J., Hua H., Li Y., Ren J., et al. (2019). Drought-induced changes in photosynthetic electron transport in maize probed by prompt fluorescence, delayed fluorescence, P700 and cyclic electron flow signals. Environ. Exp. Bot. 158, 51–62. doi:  10.1016/j.envexpbot.2018.11.005 DOI