Acuña-Galindo M. A., Mason R. E., Subramanian N. K., and Hays D. B. (2015). Meta-analysis of wheat QTL regions associated with adaptation to drought and heat stress. Crop Science, 55(2): 477-492.
Aiqing S., Somayanda I., Sebastian S. V., Singh K., Gill K., Prasad P. V. V., and Jagadish S. K. (2018). Heat stress during flowering affects time of day of flowering, seed set, and grain quality in spring wheat. Crop Science, 58(1): 380-392.
Amirbakhtiar N., Zahravi M., Arshad Y., Mahdavimajd J., and Afraz M. H. (2023). Identification of bread wheat accessions tolerant to terminal heat stress. Cereal Research, 13(3): 231-247. DOI: 10.22124/CR.2023.25872.1794.
Aziz A., Mahmood T., Mahmood Z., Shazadi K., Mujeeb-Kazi A., and Rasheed A. (2018). Genotypic variation and genotype×environment interaction for yield-related traits in synthetic hexaploid wheats under a range of optimal and heat-stressed environments. Crop Science, 58(1): 295-303.
Azrai M., Aqil M., Andayani N. N., Efendi R., et al. (2024). Optimizing ensembles machine learning, genetic algorithms, and multivariate modeling for enhanced prediction of maize yield and stress tolerance index. Frontiers in Sustainable Food Systems, 8: 1334421.
Bose S., Banerjee S., Kumar S., Saha A., Nandy D., and Hazra S. (2024). Review of applications of artificial intelligence (AI) methods in crop research. Journal of Applied Genetics, 65(2): 225-240.
Cervantes J., Garcia-Lamont F., Rodríguez-Mazahua L., and Lopez A. (2020). A comprehensive survey on support vector machine classification: Applications, challenges and trends. Neurocomputing, 408: 189-215.
Cheng T., Li M., Quan L., Song Y., Lou Z., Li H., and Du X. (2024). A multimodal and temporal network-based yield assessment method for different heat-tolerant genotypes of wheat. Agronomy, 14(8): 1694.
Elbasyoni I. S. (2018). Performance and stability of commercial wheat cultivars under terminal heat stress. Agronomy, 8(4): 37.
Etminan A., Pour-Aboughadareh A., Mohammadi R., Shooshtari L., Yousefiazarkhanian M., and Moradkhani H. (2019). Determining the best drought tolerance indices using artificial neural network (ANN): Insight into application of intelligent agriculture in agronomy and plant breeding. Cereal Research Communications, 47: 170-181.
Fu Y. B. (2015). Understanding crop genetic diversity under modern plant breeding. Theoretical and Applied Genetics, 128: 2131-2142.
Hasanuzzaman M., Sarwar S., and Islam M. (2020). Identification of yield predictors of wheat (Triticum aestivum L.) under salt stress using random forest, multiple and stepwise regression. International Journal of Experimental Agriculture, 10(1): 20-25.
International Board for Plant Genetic Resources (IBPGR). (1978). Descriptors for wheat and Aegilops. Rome, Italy.
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., P. Zhai, A. Pirani, S.L. Connors, C. Péan, S. Berger, N. Caud, Y. Chen, L. Goldfarb, M.I. Gomis, M. Huang, K. Leitzell, E. Lonnoy, J.B.R. Matthews, T.K. Maycock, T. Waterfield, O. Yelekçi, R. Yu, and B. Zhou (Eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.
Jeong J. H., Resop J. P., Mueller N. D., Fleisher D. H., et al. (2016). Random forests for global and regional crop yield predictions. PloS One, 11(6): e0156571.
Lal M. K., Tiwari R. K., Gahlaut V., Mangal V., et al. (2021). Physiological and molecular insights on wheat responses to heat stress. Plant Cell Reports, 41(3): 501-518.
Langridge P., and Reynolds M. (2021). Breeding for drought and heat tolerance in wheat. Theoretical and Applied Genetics, 134: 1753-1769.
Mishra S. C., Singh S., Patil R., Bhusal N., Malik A., and Sareen S. (2014). Breeding for heat tolerance in wheat. Genetics, 2(2): 1.
Mojtabaee Zamani M., Mabipour M., and Mesgarbashi M. (2015). Evaluating reaction of bread wheat genotypes to heat stress during grain filling stage in Ahwaz. Journal of Plant Productions, 37: 119-130.
Niazian M., and Niedbała G. (2020). Machine learning for plant breeding and biotechnology. Agriculture, 10(10): 436.
Paliwal R., Röder M. S., Kumar U., Srivastava J. P., and Joshi A. K. (2012). QTL mapping of terminal heat tolerance in hexaploid wheat (T. aestivum L.). Theoretical and Applied Genetics, 125(3): 561-575.
Posch B. C., Kariyawasam B. C., Bramley H., Coast O., et al. (2019). Exploring high temperature responses of photosynthesis and respiration to improve heat tolerance in wheat. Journal of Experimental Botany, 70(19): 5051-5069.
Rahimi Y., Bihamta M. R., Taleei A., Alipour H., and Ingvarsson P. K. (2019). Applying an artificial neural network approach for drought tolerance screening among Iranian wheat landraces and cultivars grown under well-watered and rain-fed conditions. Acta Physiologiae Plantarum, 41: 1-17.
Rehman H. U., Tariq A., Ashraf I., Ahmed M., Muscolo A., Basra S. M., and Reynolds M. (2021). Evaluation of physiological and morphological traits for improving spring wheat adaptation to terminal heat stress. Plants, 10(3): 455.
Rezaei E. E., Siebert S., Manderscheid R., Müller J., et al. (2018). Quantifying the response of wheat yields to heat stress: The role of the experimental setup. Field Crops Research, 217: 93-103.
Rezaeizadeh A., Mohamadi V., Siahpoush M. R., and Ahmadi A. (2020). The response of Iranian spring wheat cultivars to heat stress at anthesis and grain filling stages. Journal of Crop Breeding, 12(33): 107-109.
Satpathi A., Setiya P., Das B., Nain A. S., Jha P. K., Singh S., and Singh S. (2023). Comparative analysis of statistical and machine learning techniques for rice yield forecasting for Chhattisgarh, India. Sustainability, 15(3): 2786.
Sharma N., Kumar M., Daetwyler H. D., Trethowan R. M., Hayden M., and Kant S. (2024). Phenotyping for heat stress tolerance in wheat population using physiological traits, multispectral imagery, and machine learning approaches. Plant Stress, 14: 100593.
Shiferaw B., Smale M., Braun H. J., Duveiller E., Reynolds M., and Muricho G. (2013). Crops that feed the world 10. Past successes and future challenges to the role played by wheat in global food security. Food Security, 5: 291-317.
Singh A., Ganapathysubramanian B., Singh A. K., and Sarkar S. (2016). Machine learning for high-throughput stress phenotyping in plants. Trends in Plant Science, 21(2): 110-124.
Thistlethwaite R., Tan D., Bokshi A., Ullah S., and Trethowan R. (2020). A phenotyping strategy for evaluating the high-temperature tolerance of wheat. Field Crops Research, 255: 107905.
Uddin S., Haque I., Lu H., Moni M. A., and Gide E. (2022). Comparative performance analysis of K-nearest neighbour (KNN) algorithm and its different variants for disease prediction. Scientific Reports, 12(1): 6256.
Ullah A., Nadeem F., Nawaz A., Siddique K. H., and Farooq M. (2022). Heat stress effects on the reproductive physiology and yield of wheat. Journal of Agronomy and Crop Science, 208(1): 1-17.
Yadav M. R., Choudhary M., Singh J., Lal M. K., et al. (2022). Impacts, tolerance, adaptation, and mitigation of heat stress on wheat under changing climates. International Journal of Molecular Sciences, 23(5): 2838.
Zahravi M., Amirbakhtiar N., Arshad Y., Mosharraf Ghahfarrokhi G., and Ahmadi M. (2021). Identification of heat tolerant genetic sources in bread wheat germplasm. Journal of Crop Breeding, 13(39): 228-238. DOI: 10.52547/jcb.13.39.228.
Zhao Y., Xiao D., Bai H., Tang J., Liu D. L., Qi Y., and Shen Y. (2022). The prediction of wheat yield in the North China Plain by coupling crop model with machine learning algorithms. Agriculture, 13(1): 99.
)