Applied Agriculture Sciences

Agriculture and food sciences | Online ISSN: 3066-3407
23
Citations
96.7k
Views
41
Articles
Your new experience awaits. Try the new design now and help us make it even better
Switch to the new experience
REVIEWS   (Open Access)

Multi-Omics Approaches in Orphan Crops: Bridging the Gap Between Botanical Diversity and Global Food Security

Abstract 1. Introduction 2. The Modern Biotechnological Toolkit for Orphan Crops 3. Methodology 4. Results and Discussion 5. Conclusion References

Zahir Uddin 1*
 

+ Author Affiliations

Applied Agriculture Sciences 4 (1) 1-8 https://doi.org/10.25163/agriculture.4110911

Submitted: 29 June 2026 Revised: 16 August 2026  Accepted: 24 August 2026  Published: 26 August 2026 


Abstract

Modern agriculture leans, perhaps too heavily, on a narrow handful of staple species, and that dependence is beginning to look less like efficiency and more like fragility. As climate volatility intensifies and diets grow monotonous, orphan or underutilized crops - long overlooked by formal breeding programs - are being reconsidered as a practical route toward dietary diversification and climate resilience. This narrative review synthesizes literature published between 2011 and 2026, drawing on genomic, pangenomic, transcriptomic, metabolomic, and phenomic studies of underutilized crop species, alongside research on CRISPR/Cas-based domestication and AI-driven phenotyping frameworks. The evidence indicates that long-read sequencing and graph-based pangenomes have begun resolving structural variation once hidden by single reference genomes, revealing presence-absence variants tied to drought, heat, and nutritional traits in species such as finger millet, tef, and pigeonpea. Precision editing tools, particularly CRISPR/Cas9 and base/prime editors, have already converted several wild or semi-domesticated relatives into agronomically workable crops, while machine-learning-assisted phenomics has improved trait prediction accuracy considerably. Taken together, these findings support an integrated De novo domestication-Speed breeding-AI-empowered Phenomics (DSAP) strategy as a credible, though not yet fully proven, roadmap for repositioning orphan crops within global food systems, provided that open-access genomic infrastructure and participatory breeding are pursued alongside the technology itself.

Keywords: orphan crops; multi-omics integration; pangenomics; CRISPR/Cas gene editing; food security

References

Akcapinar, M. C., & Apaydin, H. (2025). Early yield estimation in wheat using open access global datasets and artificial intelligence. Computers and Electronics in Agriculture, 237, Article 110486. https://doi.org/10.1016/j.compag.2025.110486     

Akdogan, G., Benlioglu, B., Ahmed, H. A., Bilir, M., Ergun, N., Aydogan, S., ... Osturk, M. (2025). Agro-morphological characterization and machine learning-based prediction of genetic diversity in six-row barley genotypes from Türkiye. Euphytica, 221(2), Article 69. https://doi.org/10.1007/s10681-025-03522-7       

Alvarez-Vasquez, A., Lima-Huanca, L., Bardales-Álvarez, R., Valderrama-Valencia, M., & Condori-Pacsi, S. (2025). In silico characterization and determination of gene expression levels under saline stress conditions in the zinc finger family of the C1-2i subclass in Chenopodium quinoa Willd. International Journal of Molecular Sciences, 26(6), Article 2570. https://doi.org/10.3390/ijms26062570      

Amas, J., Natarajan, R. K., Thomas, W. J. W., Edwards, D., Batley, J., & Dolatabadian, A. (2026). Genomic advances in orphan and underutilized Brassicaceae crops and their wild relatives. Frontiers in Plant Science, 17, Article 1878836. https://doi.org/10.3389/fpls.2026.1878836              

Anand, S., Reddy, S. B., Shajini, N. M., Jose, E., Shirsat, M. S., Visakh, R. L., Jha, U. C., Sah, R. P., & Beena, R. (2026). Bridging scales: Integrated multi-omics and deep phenotyping for climate resilience in crop plants. Frontiers in Plant Science, 17, Article 1777294. https://doi.org/10.3389/fpls.2026.1777294            

Ashfaq, M., Khan, I., Shah, D., Ali, S., & Tahir, M. (2025). Predicting wheat yield using deep learning and multi-source environmental data. Scientific Reports, 15(1), Article 26446. https://doi.org/10.1038/s41598-025-11780-7        

Ashraf, U., Mahmood, S., Shahid, N., Imran, M., Siddique, M., & Abrar, M. (2022). Multi-omics approaches for strategic improvements of crops under changing climatic conditions. In C. S. Prakash et al. (Eds.), Principles and practices of OMICS and genome editing for crop improvement (pp. 57-94). Springer Nature Switzerland. https://doi.org/10.1007/978-3-030-96925-7_3               

Babele, P. K., Kudapa, H., Singh, Y., Varshney, R. K., & Kumar, A. (2022). Mainstreaming orphan millets for advancing climate smart agriculture to secure nutrition and health. Frontiers in Plant Science, 13, Article 902536. https://doi.org/10.3389/fpls.2022.902536

Babitha, K. C., Vemanna, R., Nataraja, K., et al. (2015). Overexpression of EcbHLH57 transcription factor from Eleusine coracana L. in tobacco confers tolerance to salt, oxidative and drought stress. PLoS ONE, 10(7), Article e0129060. https://doi.org/10.1371/journal.pone.0137098        

Beyene, G., Plaza-Wüthrich, S., Erny, D., Assefa, K., & Tadele, Z. (2022). CRISPR/Cas9-mediated tetra-allelic mutation of the 'Green Revolution' SEMIDWARF-1 (SD-1) gene confers lodging resistance in tef (Eragrostis tef). Plant Biotechnology Journal, 20(9), 1716-1729. https://doi.org/10.1111/pbi.13840        

Boatwright, L., Thudi, M., Sangireddy, M. K. R., Coffin, A. W., Tadesse, H. K., Vutla, S., et al. (2024). GWAS analysis for plant height and stem diameter in sorghum using multiple phenotyping approaches. The Plant Phenome Journal, 7(1), Article e70008. https://doi.org/10.1002/ppj2.70008            

Chen, F., et al. (2025). Computational approaches bridging omics and physiology. Plant Communications, 6, Article 101199. https://doi.org/10.1016/j.xplc.2025.101199              

Chen, J., Wang, L., Su, T., Zhang, C., & Dong, L. (2023). Pangenome analysis reveals genomic variations associated with domestication traits in broomcorn millet. Nature Genetics, 55(12), 2243-2254. https://doi.org/10.1038/s41588-023-01580-w      

Cortés, A. J., Castillejo, M. Á., & Yockteng, R. (2023). 'Omics' approaches for crop improvement. Agronomy, 13(5), Article 1401. https://doi.org/10.3390/agronomy13051401           

Dadu, R. H. R., Bar, I., Ford, R., Sambasivam, P., Croser, J., Ribalta, F., Kaur, S., Sudheesh, S., & Gupta, D. (2021). Lens orientalis contributes quantitative trait loci and candidate genes associated with ascochyta blight resistance in lentil. Frontiers in Plant Science, 12, Article 703283. https://doi.org/10.3389/fpls.2021.703283   

Danilevicz, M. F., Upadhyaya, S. R., Batley, J., Bennamoun, M., Bayer, P. E., & Edwards, D. (2025). Understanding plant phenotypes in crop breeding through explainable AI. Plant Biotechnology Journal, 23(11), 4200-4213. https://doi.org/10.1111/pbi.70208               

Diakite, S., Norman, P. E., Kamara, L., Pehlivan, N., & Zargar, M. (2026). Genetic enhancement of root, tuber and cereal crops via pangenomics, multi-omics integration and AI-driven prediction. Frontiers in Plant Science, 17, Article 1793924. https://doi.org/10.3389/fpls.2026.1793924              

Feldman, M. J., Park, J., Miller, N., Wakholi, C., Greene, K., Abbasi, A., et al. (2024). A scalable, low-cost phenotyping strategy to assess tuber size, shape, and the colorimetric features of tuber skin and flesh in potato breeding populations. The Plant Phenome Journal, 7(1), Article e20099. https://doi.org/10.1002/ppj2.20099       

Gelaye, Y., Li, J., Yang, P., & Luo, H. (2025). Accelerating orphan crop innovation through genomic breakthroughs for climate-resilient and sustainable food systems. Discover Food, 5, Article 426. https://doi.org/10.1007/s44187-025-00697-9             

Ghamkhar, K. (2022). Plant genetic resources and their values. In K. Ghamkhar & C. M. Richards (Eds.), Plant genetic resources for the 21st century: The OMICS era (pp. 3-8). CRC Press.

Ghamkhar, K., & Richards, C. M. (2022). Omics technologies for genetic resources: Review and prospects. In K. Ghamkhar & C. M. Richards (Eds.), Plant genetic resources for the 21st century: The OMICS era (pp. 25-45). CRC Press.

Gunnula, W., Kanawapee, N., Chokthaweepanich, H., & Phansak, P. (2025). Exploring drought response: Machine-learning-based classification of rice tolerance using root and physiological traits. Agronomy, 15(8), Article 1840. https://doi.org/10.3390/agronomy15081840           

Hatakeyama, M., Aluri, S., Balachadran, M. T., Sivarajan, S. R., Patrignani, A., Grüter, S., Poveda, L., Shimizu-Inatsugi, R., Baeten, J., Françoijs, K., Nataraja, K., Reddy, Y., Phadnis, S., Ravikumar, R., Schlapbach, R., Sreeman, S., & Shimizu, K. (2018). Multiple hybrid de novo genome assembly of finger millet, an orphan allotetraploid crop. DNA Research, 25(1), 39-47. https://doi.org/10.1093/dnares/dsx036     

He, Q., Tang, S., Zhi, H., Chen, J., Zhang, J., & Diao, X. (2024). A complete reference genome assembly for foxtail millet and Setaria-db, a comprehensive database for Setaria. Molecular Plant, 17(2), 219-222. https://doi.org/10.1016/j.molp.2023.12.012               

Hu, H., Zhao, J., Thomas, W. J. W., Batley, J., & Edwards, D. (2025). The role of pangenomics in orphan crop improvement. Nature Communications, 16(1), Article 118. https://doi.org/10.1038/s41467-024-55260-4       

Huang, X., Su, D., & Xu, C. (2025). Revitalizing orphan crops to combat food insecurity. Nature Communications, 16, Article 66020. https://doi.org/10.1038/s41467-025-66020-3          

Jamie, H., Jha, U. C., & Nayyar, H. (2026). The multi-omics opportunity to improve heat adaptation in chickpea. Food and Energy Security, 15, Article e70292. https://doi.org/10.1002/fes3.70292         

Jarvis, D. E., Ho, Y. S., Lightfoot, D. J., Schmöckel, S. M., Li, B., & Schmutz, J. (2017). The genome of Chenopodium quinoa. Nature, 542, 307-312. https://doi.org/10.1038/nature21370        

Kim, Y.-O., Patel, D. H., Lee, D.-S., Song, Y., & Bae, H.-J. (2011). High cadmium-binding ability of a novel Colocasia esculenta metallothionein increases cadmium tolerance in Escherichia coli and tobacco. Bioscience, Biotechnology, and Biochemistry, 75(10), 1912-1920. https://doi.org/10.1271/bbb.110292      

Kumar, A., Anju, T., Kumar, S., Chhapekar, S. S., Sreedharan, S., Singh, S., Choi, S. R., Ramchiary, N., & Lim, Y. P. (2021b). Linking omics and gene editing tools for rapid improvement of traditional food plants for diversified foods and sustainable food security. Preprints, Article 2021060363. https://doi.org/10.20944/preprints202106.0363.v1      

Kumar, A., Sreedharan, S., Singh, P., Achigan-Dako, E. G., & Ramchiary, N. (2021a). Improvement of a traditional orphan food crop, Portulaca oleracea L. (Purslane) using genomics for sustainable food security and climate-resilient agriculture. Frontiers in Sustainable Food Systems, 5, Article 711820. https://doi.org/10.3389/fsufs.2021.711820             

Kundu, B. K., & Tanti, B. (2025). Decoding plant physiology through systems biology: Integrative multi-omics and computational perspectives for next-generation crop design. Plant Communications, 7, Article 100706. https://doi.org/10.1016/j.xplc.2025.100706              

Lemmon, Z. H., Reem, N. T., Dalrymple, J., Soyk, S., Swartwood, K. E., & Lippman, Z. B. (2018). Rapid improvement of domestication traits in an orphan crop by genome editing. Nature Plants, 4(10), 766-770. https://doi.org/10.1038/s41477-018-0259-x               

Li, H. W., Zang, B. S., Deng, X. W., & Wang, X. P. (2011). Overexpression of the trehalose-6-phosphate synthase gene OsTPS1 enhances abiotic stress tolerance in rice. Planta, 234(5), 1007-1017. https://doi.org/10.1007/s00425-011-1458-0   

Mahmood, U., Li, X., Fan, Y., Chang, W., Niu, Y., Li, J., Qu, C., & Lu, K. (2022). Multi-omics revolution to promote plant breeding efficiency. Frontiers in Plant Science, 13, Article 1062952. https://doi.org/10.3389/fpls.2022.1062952         

Manoj Kumar, S., Kalyani, S., Kahodariya, J. H., Sinha, K. P., Thakur, A., & Perveen, N. (2026). Integrating synthetic biology and multi-omics for precision crop improvement: Technological advances and ethical perspectives. International Journal of Advanced Biochemistry Research, 10(3), 620-631. https://doi.org/10.33545/26174693.2026.v10.i3h.7936

National Bureau of Plant Genetic Resources. (2023). Plant genetic resources collections in India: NBPGR curation database. https://nbpgr.org.in/nbpgr2023    

Ramashia, S. E., Anyango, J. O., & Ndakidemi, P. A. (2018). Nutritional, calcium, and mineral profiling of finger millet. Food Chemistry, 245, 111-118. https://doi.org/10.1016/j.foodchem.2017.10.086          

Rasool, G., & Qadir, F. (2026). Precision genome editing for smarter, inclusive, and nutritious crops: Emerging tools, omics integration, and global perspectives. Trends in Animal and Plant Sciences, 7, 38-51. https://doi.org/10.62324/TAPS/2026.006 

Sanfeliu Meliá, A., Cárdenas, P. D., & Bak, S. (2026). From wild to tamed: Reimagining novel crops through omics and local plant diversity. Plants, People, Planet. Advance online publication. https://doi.org/10.1002/ppp3.70209             

Shen, L., Ding, G., Jackson, R., Ali, M., Liu, S., Mitchell, A., & Edwards, D. (2024). GSP-AI, an AI-powered platform for identifying key growth stages and the vegetative-to-reproductive transition in wheat using trilateral drone imagery and meteorological data. Plant Phenomics, 6, Article 255. https://doi.org/10.34133/plantphenomics.0255   

Singh, N., Rai, V., & Singh, N. K. (2020). Multi-omics strategies and prospects to enhance seed quality and nutritional traits in pigeonpea. The Nucleus, 63, 249-256. https://doi.org/10.1007/s13237-020-00341-0      

Tan, S., Liu, J., Lu, H., Lan, M., Yu, J., et al. (2022). Machine learning approaches for rice seedling growth stages detection. Frontiers in Plant Science, 13, Article 914771. https://doi.org/10.3389/fpls.2022.914771

Tao, M., Zhang, T., Xie, X., & Liang, X. (2023). Water footprint modeling and forecasting of cassava based on different artificial intelligence algorithms in Guangxi, China. Journal of Cleaner Production, 382, Article 135238. https://doi.org/10.1016/j.jclepro.2022.135238         

VanBuren, R., Man Wai, C., Wang, X., Pardo, J., Yocca, A. E., et al. (2020). Exceptional subgenome stability and functional divergence in the allotetraploid Ethiopian cereal teff. Nature Communications, 11(1), Article 884. https://doi.org/10.1038/s41467-020-14682-1       

Varshney, R. K., Chen, W., Li, Y., Bharti, A. K., Saxena, R. K., Schlueter, J. A., et al. (2011). Draft genome sequence of pigeonpea (Cajanus cajan), an orphan legume crop of resource-poor farmers. Nature Biotechnology, 30(1), 83-89. https://doi.org/10.1038/nbt.2022

Venezia, M., & Krainer, K. (2021). Current advancements and limitations of gene editing in orphan crops. Frontiers in Plant Science, 12, Article 742932. https://doi.org/10.3389/fpls.2021.742932             

Wang, S., Wang, G., Wang, Y., Wang, Y., Wang, H., Li, W., & Xu, Y. (2025). Integrating meteorological and breeding data to predict maize yields using machine learning algorithms. Frontiers in Plant Science, 16, Article 1722068. https://doi.org/10.3389/fpls.2025.1722068              

Wang, Z., & Xiang, G. (2025). Genomic research and genetic improvement of orphan crops: Novel strategies for addressing global food security challenges. Agrobiodiversity, 2(2), 19-32. https://doi.org/10.48130/abd-0025-0004

Williams, W. M. (2023). Germplasm enhancement and genebanks. In K. Ghamkhar, W. M. Williams, & A. H. D. Brown (Eds.), Plant genetic resources for the 21st century: The OMICS era (1st ed., pp. 229-262). Apple Academic Press.

Wu, H., Han, R., Zhao, L., Liu, M., Chen, H., Li, W., et al. (2025). AutoGP: An intelligent breeding platform for enhancing maize genomic selection. Plant Communications, 6(2), Article 101240. https://doi.org/10.1016/j.xplc.2025.101240          

Yan, H., Sun, M., Zhang, Z., & Varshney, R. K. (2023). Pangenomic analysis identifies structural variation associated with heat tolerance in pearl millet. Nature Genetics, 55(3), 507-518. https://doi.org/10.1038/s41588-023-01302-4   

You, J., Li, X., Low, M., Lobell, D., & Ermon, S. (2017). Deep Gaussian process for crop yield prediction based on remote sensing data. Proceedings of the AAAI Conference on Artificial Intelligence, 31(1), 4559-4565. https://doi.org/10.1609/aaai.v31i1.11172               

Zenda, T., Liu, S., Dong, A., Li, J., Wang, Y., Liu, X., Wang, N., & Duan, H. (2021). Omics-facilitated crop improvement for climate resilience and superior nutritive value. Frontiers in Plant Science, 12, Article 774994. https://doi.org/10.3389/fpls.2021.774994


Article metrics
View details
0
Downloads
0
Citations
9
Views

View Dimensions


View Plumx


View Altmetric



0
Save
0
Citation
9
View
0
Share