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Plant Science Today (PST)

Review Articles

Vol. 12 No. sp3 (2025): Advances in Plant Health Improvement for Sustainable Agriculture

Artificial intelligence and agriculture: Transforming plant breeding for a sustainable future

DOI
https://doi.org/10.14719/pst.8757
Submitted
8 April 2025
Published
11-11-2025

Abstract

Plant Breeding is a reliable assurance of agriculture productivity and nutritional security, while information technologies offer efficient means of fostering advancements in plant variety development. Processing large amounts of multidimensional breeding data over generations is complex, even though breeding information technologies provide an accessible and scientifically sound approach. Therefore, decision support tools help breeders to extract relevant and valuable information by introducing the golden seed breeding cloud platform. This platform is a cutting-edge AI driven agriculture system designed to maximize seed breeding for increased sustainability, resilience and yield. This brought a paradigm shift in farming by developing AI-powered solutions. To accomplish data integration and feature identification for stress phenotyping, it is necessary to take advantage of machine learning algorithms to extract patterns and features from the massive repository of data. Currently, plant breeding is propelling a revolution driven by state-of-the-art amenities for crop phenotyping and genome sequencing. Advanced phenotyping and genotyping when coupled with machine learning and cognitive sciences, are improving the accuracy of identifying the underlying genetic causes of attributes. Another advancement i.e., Next-generation AI using big data envisages how it can deal with the challenges by interfacing with the multi-omics big data to accelerate plant breeding, particularly for climate-resilient agriculture. Successful implementation of the proposed model based on big data characteristics will facilitate the evolution of breeding from “art” to “science” and eventually to “intelligence” in the era of Artificial Intelligence.

References

  1. 1. Elbasi E, Mostafa N, AlArnaout Z, Zreikat AI, Cina E, Varghese G, Shdefat A, Topcu AE, et al. Artificial intelligence technology in the agricultural sector: a systematic literature review. IEEE Access. 2022;11:171–202. https://doi.org/10.1109/ACCESS.2022.3232485
  2. 2. Lybbert TJ, Sumner DA. Agricultural technologies for climate change in developing countries: policy options for innovation and technology diffusion. Food Policy. 2012;37(1):114–23. https://doi.org/10.1016/j.foodpol.2011.11.001
  3. 3. Shi X, An X, Zhao Q, Liu H, Xia L, Sun X, Guo Y. State of the art Internet of Things in protected agriculture. Sensors (Basel). 2019;19(8):1833. https://doi.org/10.3390/s19081833
  4. 4. Walters JP, Archer DW, Sassenrath GF, Hendrickson JR, Hanson JD, Halloran JM, Vadas P, et al. Exploring agricultural production systems and their fundamental components with system dynamics modelling. Ecol Model. 2016;333:51–65. https://doi.org/10.1016/j.ecolmodel.2016.04.015
  5. 5. Shah G, Shah A, Shah M. Panacea of challenges in real world application of big data analytics in healthcare sector. J Data Inf Manag. 2019;1:107–16. https://doi.org/10.1007/s42488-019-00010-1
  6. 6. Sukhadia A, Upadhyay K, Gundeti M, Shah S, Shah M. Optimization of smart traffic governance system using artificial intelligence. Aug Human Res. 2020;5(1):13. https://doi.org/10.1007/s41133-020-00035-x
  7. 7. Wallace JG, Rodgers Melnick E, Buckler ES. On the road to breeding 4.0: unraveling the good, the bad, and the boring of crop quantitative genomics. Annu Rev Genet. 2018;52(1):421–44. https://doi.org/10.1146/annurev-genet-120116-024846
  8. 8. Harfouche AL, Jacobson DA, Kainer D, Romero JC, Harfouche AH, Mugnozza GS, Moshelion M, et al. Accelerating climate resilient plant breeding by applying next generation artificial intelligence. Trends Biotechnol. 2019;37(11):1217–35. https://doi.org/10.1016/j.tibtech.2019.05.007
  9. 9. Zhao X, Fan W, Chen B, Tang R. Automated machine learning for recommendations: fundamentals and advances. In: Proceedings of the 31st ACM Web Conference (WWW ’22). New York: ACM; 2022. p. 398.
  10. 10. Kakkad V, Patel M, Shah M. Biometric authentication and image encryption for image security in cloud framework. Multiscale Multidiscip Model Exp Des. 2019;2(4):233–48. https://doi.org/10.1007/s41939-019-00049-y
  11. 11. Patel D, Shah Y, Thakkar N, Shah K, Shah M. Implementation of artificial intelligence techniques for cancer detection. Aug Human Res. 2020;5:1–10. https://doi.org/10.1007/s41133-019-0024-3
  12. 12. Patel H, Prajapati D, Mahida D, Shah M. Transforming petroleum downstream sector through big data: a holistic review. J Pet Explor Prod Technol. 2020;10:2601–11. https://doi.org/10.1007/s13202-020-00889-2
  13. 13. Panchiwala S, Shah M. A comprehensive study on critical security issues and challenges of the IoT world. J Data Inf Manag. 2020;2:257–78. https://doi.org/10.1007/s42488-020-00030-2
  14. 14. Gavhale KR, Gawande U. An overview of the research on plant leaves disease detection using image processing techniques. IOSR J Comput Eng. 2014;16(1):10–6.
  15. 15. Jani K, Chaudhuri M, Patel H, Shah M. Machine learning in films: an approach towards automation in film censoring. J Data Inf Manag. 2020;2:55–64. https://doi.org/10.1007/s42488-019-00016-9
  16. 16. Zakaria M, Mabrouka AS, Sarhan S. Artificial neural network: a brief overview. Int J Comput Sci Eng. 2014;4(2):7–12.
  17. 17. Xiao D, Li B, Mao Y. A multiple hidden layers extreme learning machine method and its application. Math Probl Eng. 2017;2017(1):4670187. https://doi.org/10.1155/2017/4670187
  18. 18. Hesamifard E, Takabi H, Ghasemi M. Cryptodl: deep neural networks over encrypted data. arXiv. 2017;1711.05189. https://doi.org/10.48550/arXiv.1711.05189
  19. 19. Sladojevic S, Arsenovic M, Anderla A, Culibrk D, Stefanovic D. Deep neural networks based recognition of plant diseases by leaf image classification. Comput Intell Neurosci. 2016;2016(1):3289801. https://doi.org/10.1155/2016/3289801
  20. 20. Pandya R, Nadiadwala S, Shah R, Shah M. Buildout of methodology for meticulous diagnosis of K complex in EEG for aiding the detection of Alzheimer’s by artificial intelligence. Aug Human Res. 2020;5:1–8. https://doi.org/10.1007/s41133-019-0021-6
  21. 21. Abiodun OI, Jantan A, Omolara AE, Dada KV, Mohamed NA, Arshad H. State of the art in artificial neural network applications: a survey. Heliyon. 2018;4(11):e00938. https://doi.org/10.1016/j.heliyon.2018.e00938
  22. 22. Nabwire S, Suh HK, Kim MS, Baek I, Cho BK. Application of artificial intelligence in phenomics. Sensors (Basel). 2021;21(13):4363. https://doi.org/10.3390/s21134363
  23. 23. William JR. Some definitions of artificial intelligence. 2022. https://cse.buffalo.edu/~Rapaport/definitions.of.ai.html
  24. 24. van Dijk AD, Kootstra G, Kruijer W, de Ridder D. Machine learning in plant science and plant breeding. iScience. 2021;24(1):101890. https://doi.org/10.1016/j.isci.2020.101890
  25. 25. Patel UK, Anwar A, Saleem S, Malik P, Rasul B, Patel K, Yao R, et al. Artificial intelligence as an emerging technology in the current care of neurological disorders. J Neurol. 2021;268:1623–42. https://doi.org/10.1007/s00415-019-09518-3
  26. 26. Yanting SH, Clarke R, Tingting ZH, Clifton D; inventors; Oxford University Innovation Ltd, assignee. Generating neural network models, classifying physiological data, and classifying patients into clinical classifications. US Patent Application US 18/280,751. 2024 Sep 12.
  27. 27. Frey LJ. Artificial intelligence and integrated genotype–phenotype identification. Genes (Basel). 2018;10(1):18. https://doi.org/10.3390/genes10010018
  28. 28. Zheng H, Li W, Jiang J, Liu Y, Cheng T, Tian Y, Zhu Y, Cao W, Zhang Y, Yao X. A comparative assessment of different modeling algorithms for estimating leaf nitrogen content in winter wheat using multispectral images from an unmanned aerial vehicle. Remote Sens (Basel). 2018;10(12):2026. https://doi.org/10.3390/rs10122026
  29. 29. Salehi M, Farhadi S, Moieni A, Safaie N, Ahmadi H. Mathematical modeling of growth and paclitaxel biosynthesis in Corylus avellana cell culture responding to fungal elicitors using multilayer perceptron genetic algorithm. Front Plant Sci. 2020;11:1148. https://doi.org/10.3389/fpls.2020.01148
  30. 30. Asefpour Vakilian K. Machine learning improves our knowledge about miRNA functions towards plant abiotic stresses. Sci Rep. 2020;10(1):3041. https://doi.org/10.1038/s41598-020-59981-6
  31. 31. Wang MW, Goodman JM, Allen TE. Machine learning in predictive toxicology: recent applications and future directions for classification models. Chem Res Toxicol. 2020;34(2):217–39. https://doi.org/10.1021/acs.chemrestox.0c00316
  32. 32. Hu H, Scheben A, Edwards D. Advances in integrating genomics and bioinformatics in the plant breeding pipeline. Agriculture (Basel). 2018;8(6):75. https://doi.org/10.3390/agriculture8060075
  33. 33. Orozco Arias S, Isaza G, Guyot R. Retrotransposons in plant genomes: structure, identification, and classification through bioinformatics and machine learning. Int J Mol Sci. 2019;20(15):3837. https://doi.org/10.3390/ijms20153837
  34. 34. Azevedo AM, Andrade VC, Pedrosa CE, Oliveira CM, Dornas MF, Cruz CD, Valadares NR. Application of artificial neural networks in indirect selection: a case study on the breeding of lettuce. Bragantia. 2015;74(4):387–93. https://doi.org/10.1590/1678-4499.0088
  35. 35. Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, et al. Recent advances in convolutional neural networks. Pattern Recogn. 2018;77:354–77. https://doi.org/10.1016/j.patcog.2017.10.013
  36. 36. Ali AM, Darvishzadeh R, Skidmore A, Gara TW, Heurich M. Machine learning methods’ performance in radiative transfer model inversion to retrieve plant traits from Sentinel 2 data of a mixed mountain forest. Int J Digit Earth. 2021;14(1):106–20. https://doi.org/10.1080/17538947.2020.1794064
  37. 37. Gold KM, Townsend PA, Herrmann I, Gevens AJ. Investigating potato late blight physiological differences across potato cultivars with spectroscopy and machine learning. Plant Sci. 2020;295:110316. https://doi.org/10.1016/j.plantsci.2019.110316
  38. 38. Barth R, IJsselmuiden J, Hemming J, Van Henten EJ. Synthetic bootstrapping of convolutional neural networks for semantic plant part segmentation. Comput Electron Agric. 2019;161:291–304. https://doi.org/10.1016/j.compag.2017.11.040
  39. 39. An J, Li W, Li M, Cui S, Yue H. Identification and classification of maize drought stress using deep convolutional neural network. Symmetry (Basel). 2019;11(2):256. https://doi.org/10.3390/sym11020256
  40. 40. Kattenborn T, Leitloff J, Schiefer F, Hinz S. Review on convolutional neural networks (CNN) in vegetation remote sensing. ISPRS J Photogramm Remote Sens. 2021;173:24–49. https://doi.org/10.1016/j.isprsjprs.2020.12.010
  41. 41. Srivastava A, Gupta S, Shanker K, Gupta N, Gupta AK, Lal RK. Genetic diversity in Indian poppy (Papaver somniferum L.) germplasm using multivariate and SCoT marker analyses. Ind Crops Prod. 2020;144:112050. https://doi.org/10.1016/j.indcrop.2019.112050
  42. 42. Boonsrangsom T. Genetic diversity of ‘Wan Chak Motluk’ (Curcuma comosa Roxb.) in Thailand using morphological characteristics and RAPD markers. S Afr J Bot. 2020;130:224–30. https://doi.org/10.1016/j.sajb.2020.01.005
  43. 43. Raza A, Saher MS, Farwa A, Ahmad KR. Genetic diversity analysis of Brassica species using PCR based SSR markers. Gesunde Pflanzen. 2019;71(1):1–7. https://doi.org/10.1007/s10343-018-0435-y
  44. 44. Xu S, Shen J, Wei Y, Li Y, He Y, Hu H, Feng X. Automatic plant phenotyping analysis of Melon (Cucumis melo L.) germplasm resources using deep learning methods and computer vision. Plant Methods. 2024;20:166. https://doi.org/10.1186/s13007-024-01293-1
  45. 45. Pandolfi C, Mugnai S, Azzarello E, Bergamasco S, Masi E, Mancuso S. Artificial neural networks as a tool for plant identification: a case study on Vietnamese tea accessions. Euphytica. 2009;166:411–21. https://doi.org/10.1007/s10681-008-9828-9
  46. 46. Saini G, Khamparia A, Luhach AK. Classification of plants using convolutional neural network. In: Proceedings of the First International Conference on Sustainable Technologies for Computational Intelligence: ICTSCI 2019. Singapore: Springer; 2020. p 551–61. https://doi.org/10.1007/978-981-15-0029-9_44
  47. 47. Sakoda K, Watanabe T, Sukemura S, Kobayashi S, Nagasaki Y, Tanaka Y, Shiraiwa T. Genetic diversity in stomatal density among soybeans elucidated using high throughput technique based on an algorithm for object detection. Sci Rep. 2019;9(1):7610. https://doi.org/10.1038/s41598-019-44127-0
  48. 48. Yang HW, Hsu HC, Yang CK, Tsai MJ, Kuo YF. Differentiating between morphologically similar species in genus Cinnamomum (Lauraceae) using deep convolutional neural networks. Comput Electron Agric. 2019;162:739–48. https://doi.org/10.1016/j.compag.2019.05.003
  49. 49. Sant’Anna IC, Tomaz RS, Silva GN, Nascimento M, Bhering LL, Cruz CD. Superiority of artificial neural networks for a genetic classification procedure. Genet Mol Res. 2015;14(4):9898–9906. https://doi.org/10.4238/2015.august.19.24
  50. 50. Niedbała G, Wróbel B, Piekutowska M, Zielewicz W, Paszkiewicz Jasińska A, Wojciechowski T, Niazian M. Application of artificial neural networks sensitivity analysis for the pre identification of highly significant factors influencing the yield and digestibility of grassland sward in the climatic conditions of central Poland. Agronomy. 2022;12(5):1133. https://doi.org/10.3390/agronomy12051133
  51. 51. Li N, Lin H, Wang T, Li Y, Liu Y, Chen X, et al. Impact of climate change on cotton growth and yields in Xinjiang, China. Field Crops Res. 2020;247:107590. https://doi.org/10.1016/j.fcr.2019.107590
  52. 52. Niazian M, Sadat-Noori SA, Abdipour M, Tohidfar M, Mortazavian SM. Image processing and artificial neural network-based models to measure and predict physical properties of embryogenic callus and number of somatic embryos in ajowan (Trachyspermum ammi (L.) Sprague). In Vitro Cell Dev Biol Plant. 2018;54:54–68. https://doi.org/10.1007/s11627-017-9877-7
  53. 53. Singh A, Ganapathysubramanian B, Singh AK, Sarkar S. Machine learning for high throughput stress phenotyping in plants. Trends Plant Sci. 2016;21(2):110–24. https://doi.org/10.1016/j.tplants.2015.10.015
  54. 54. Picon A, Alvarez Gila A, Seitz M, Ortiz Barredo A, Echazarra J, Johannes A. Deep convolutional neural networks for mobile capture device based crop disease classification in the wild. Comput Electron Agric. 2019;161:280–90. https://doi.org/10.1016/j.compag.2018.04.002
  55. 55. Pacal I, Kunduracioglu I, Alma MH, Deveci M, Kadry S, Nedoma J, et al. A systematic review of deep learning techniques for plant diseases. Artif Intell Rev. 2024;57(11):304. https://doi.org/10.1007/s10462-024-10944-7
  56. 56. Sibiya M, Sumbwanyambe M. A computational procedure for the recognition and classification of maize leaf diseases out of healthy leaves using convolutional neural networks. Agriengineering. 2019;1(1):119–31. https://doi.org/10.3390/agriengineering1010009
  57. 57. Selvaraj MG, Vergara A, Ruiz H, Safari N, Elayabalan S, Ocimati W, et al. AI powered banana diseases and pest detection. Plant Methods. 2019;15:1–1. https://doi.org/10.1186/s13007-019-0475-z
  58. 58. Coulibaly S, Kamsu Foguem B, Kamissoko D, Traore D. Deep neural networks with transfer learning in millet crop images. Comput Ind. 2019;108:115–20. https://doi.org/10.1016/j.compind.2019.02.003
  59. 59. Niazian M. Application of genetics and biotechnology for improving medicinal plants. Planta. 2019;249:953–73. https://doi.org/10.1007/s00425-019-03099-1
  60. 60. Altuntaş Y, Cömert Z, Kocamaz AF. Identification of haploid and diploid maize seeds using convolutional neural networks and a transfer learning approach. Comput Electron Agric. 2019;163:104874. https://doi.org/10.1016/j.compag.2019.104874
  61. 61. Blonder B, Graae BJ, Greer B, Haagsma M, Helsen K, Kapás RE, et al. Remote sensing of ploidy level in quaking aspen (Populus tremuloides Michx.). J Ecol. 2020;108(1):175–88. https://doi.org/10.1111/1365-2745.13296
  62. 62. Santeramo D, Howell J, Ji Y, Yu W, Liu W, Kelliher T. DNA content equivalence in haploid and diploid maize leaves. Planta. 2020;251(1):30. https://doi.org/10.1007/s00425-019-03320-1
  63. 63. Crossa J, Pérez Rodríguez P, Cuevas J, Montesinos López O, Jarquín D, De Los Campos G, et al. Genomic selection in plant breeding: methods, models, and perspectives. Trends Plant Sci. 2017;22(11):961–75. https://doi.org/10.1016/j.tplants.2017.08.011
  64. 64. Libbrecht MW, Noble WS. Machine learning applications in genetics and genomics. Nat Rev Genet. 2015;16(6):321–32. https://doi.org/10.1038/nrg3920
  65. 65. Perez Sanz F, Navarro PJ, Egea Cortines M. Plant phenomics: An overview of image acquisition technologies and image data analysis algorithms. GigaScience. 2017;6(11):gix092. https://doi.org/10.1093/gigascience/gix092
  66. 66. Ma C, Zhang HH, Wang X. Machine learning for big data analytics in plants. Trends Plant Sci. 2014;19(12):798–808. https://doi.org/10.1016/j.tplants.2014.08.004
  67. 67. van Dijk AD, Kootstra G, Kruijer W, de Ridder D. Machine learning in plant science and plant breeding. iScience. 2021;24(1):101890. https://doi.org/10.1016/j.isci.2020.101890
  68. 68. Sartor RC, Noshay J, Springer NM, Briggs SP. Identification of the expressome by machine learning on omics data. Proc Natl Acad Sci U S A. 2019;116(36):18119–25. https://doi.org/10.1073/pnas.1813645116
  69. 69. Demirci S, Peters SA, de Ridder D, van Dijk AD. DNA sequence and shape are predictive for meiotic crossovers throughout the plant kingdom. Plant J. 2018;95(4):686–99. https://doi.org/10.1111/tpj.13979
  70. 70. Denyer T, Ma X, Klesen S, Scacchi E, Nieselt K, Timmermans MC. Spatiotemporal developmental trajectories in the Arabidopsis root revealed using high throughput single cell RNA sequencing. Dev Cell. 2019;48(6):840–52. https://doi.org/10.1016/j.devcel.2019.02.022
  71. 71. Jean-Baptiste K, McFaline-Figueroa JL, Alexandre CM, Dorrity MW, Saunders L, Bubb KL, et al. Dynamics of gene expression in single root cells of Arabidopsis thaliana. Plant Cell. 2019;31(5):993–1011. https://doi.org/10.1105/tpc.18.00785
  72. 72. Lin F, Fan J, Rhee SY. QTG-Finder: a machine-learning based algorithm to prioritize causal genes of quantitative trait loci in Arabidopsis and rice. G3 (Bethesda). 2019;9(10):3129–38. https://doi.org/10.1534/g3.119.400319
  73. 73. Fahlgren N, Gehan MA, Baxter I. Lights, camera, action: high-throughput plant phenotyping is ready for a close-up. Curr Opin Plant Biol. 2015;24:93–9. https://doi.org/10.1016/j.pbi.2015.02.006
  74. 74. Dhondt S, Wuyts N, Inzé D. Cell to whole-plant phenotyping: the best is yet to come. Trends Plant Sci. 2013;18(8):428–39. https://doi.org/10.1016/j.tplants.2013.04.008
  75. 75. van Es SW, van der Auweraert EB, Silveira SR, Angenent GC, van Dijk AD, Immink RG. Comprehensive phenotyping reveals interactions and functions of Arabidopsis thaliana TCP genes in yield determination. Plant J. 2019;99(2):316–28. https://doi.org/10.1111/tpj.14326
  76. 76. Virlet N, Sabermanesh K, Sadeghi-Tehran P, Hawkesford MJ. Field Scanalyzer: An automated robotic field phenotyping platform for detailed crop monitoring. Funct Plant Biol. 2016;44(1):143–53. https://doi.org/10.1071/FP16163
  77. 77. Araus JL, Kefauver SC, Zaman-Allah M, Olsen MS, Cairns JE. Translating high-throughput phenotyping into genetic gain. Trends Plant Sci. 2018;23(5):451–66. https://doi.org/10.1016/j.tplants.2018.02.001
  78. 78. Li L, Zhang Q, Huang D. A review of imaging techniques for plant phenotyping. Sensors (Basel). 2014;14(11):20078–111. https://doi.org/10.3390/s141120078
  79. 79. Pound MP, Atkinson JA, Wells DM, Pridmore TP, French AP. Deep learning for multi-task plant phenotyping. Proc IEEE Int Conf Comput Vis Workshops. 2017:2055–63. https://doi.org/10.1101/204552
  80. 80. Ward D, Moghadam P, Hudson N. Deep leaf segmentation using synthetic data. arXiv. 2018. https://doi.org/10.48550/arXiv.1807.10931
  81. 81. Taghavi Namin S, Esmaeilzadeh M, Najafi M, Brown TB, Borevitz JO. Deep phenotyping: deep learning for temporal phenotype/genotype classification. Plant Methods. 2018;14:1–4. https://doi.org/10.1186/s13007-018-0333-4
  82. 82. Chandrika G, Hepziba J, Pillai MA, Pushpam KA, Vijayalakshmi R. Vision-based rice genotype identification using machine learning techniques. Biol Forum Int J. 2023;15(8 A):285–91. Available from: https://www.researchtrend.net/bfij/pdf/Vision-based-Rice-Genotype-Identification-using-Machine-Learning-Techniques-CHANDRIKA-G-47.pdf
  83. 83. Kuska M, Wahabzada M, Leucker M, Dehne HW, Kersting K, Oerke EC, et al. Hyperspectral phenotyping on the microscopic scale: towards automated characterization of plant-pathogen interactions. Plant Methods. 2015;11:1–5. https://doi.org/10.1186/s13007-015-0073-7
  84. 84. Zhang Z, Zhu L. A review on unmanned aerial vehicle remote sensing: Platforms, sensors, data processing methods, and applications. Drones. 2023;7(6):398. https://doi.org/10.3390/drones7060398
  85. 85. Sharma V, Honkavaara E, Hayden M, Kant S. UAV remote sensing phenotyping of wheat collection for response to water stress and yield prediction using machine learning. Plant Stress. 2024;12:100464. https://doi.org/10.1016/j.stress.2024.100464
  86. 86. Das Choudhury S, Guadagno CR, Bashyam S, Mazis A, Ewers BE, Samal A, et al. Stress phenotyping analysis leveraging autofluorescence image sequences with machine learning. Front Plant Sci. 2024;15:1353110. https://doi.org/10.3389/fpls.2024.1353110
  87. 87. Arya S, Sahoo RN, Sehgal VK, Bandyopadhyay K, Rejith RG, Chinnusamy V, et al. High-throughput chlorophyll fluorescence image-based phenotyping for water deficit stress tolerance in wheat. Plant Physiol Rep. 2024;1–6. https://doi.org/10.1007/s40502-024-00783-7
  88. 88. Bock CH, Poole GH, Parker PE, Gottwald TR. Plant disease severity estimated visually, by digital photography and image analysis, and by hyperspectral imaging. Crit Rev Plant Sci. 2010;29(2):59–107. https://doi.org/10.1080/07352681003617285
  89. 89. Sterkenburg TF, Grünwald PD. The no-free-lunch theorems of supervised learning. Synthese. 2021;199(3):9979–10015. https://doi.org/10.1007/s11229-021-03233-1
  90. 90. Sychrovský D, Šustr M, Davoodi E, Bowling M, Lanctot M, Schmid M. Learning not to regret. Proc AAAI Conf Artif Intell. 2024;38(14):15202–10. https://doi.org/10.1609/aaai.v38i14.29443
  91. 91. Niehues A, de Visser C, Hagenbeek FA, Kulkarni P, Pool R, Karu N, et al. A multi-omics data analysis workflow packaged as a FAIR Digital Object. GigaScience. 2024;13:giad115. https://doi.org/10.1093/gigascience/giad115
  92. 92. Doniparthi G, Mühlhaus T, Deßloch S. Integrating FAIR Experimental Metadata for Multi-omics Data Analysis. Datenbank-Spektrum. 2024;1–9. https://doi.org/10.1007/s13222-024-00473-6
  93. 93. Selby P, Abbeloos R, Backlund JE, Basterrechea Salido M, Bauchet G, Benites-Alfaro OE, et al. BrAPI—an application programming interface for plant breeding applications. Bioinformatics. 2019;35(20):4147–55. https://doi.org/10.1093/bioinformatics/btz190
  94. 94. Sheikh M, Iqra F, Ambreen H, Pravin KA, Ikra M, Chung YS. Integrating artificial intelligence and high-throughput phenotyping for crop improvement. J Integr Agric. 2024;23(6):1787–802. https://doi.org/10.1016/j.jia.2023.10.019
  95. 95. Oliveira RC, Silva RD. Artificial intelligence in agriculture: benefits, challenges, and trends. Appl Sci. 2023;13(13):7405. https://doi.org/10.3390/app13137405
  96. 96. Sparrow R, Howard M, Degeling C. Managing the risks of artificial intelligence in agriculture. NJAS Impact Agr Life Sci. 2021;93(1):172–96. https://doi.org/10.1080/27685241.2021.2008777
  97. 97. Ryan M. The social and ethical impacts of artificial intelligence in agriculture: mapping the agricultural AI literature. AI Soc. 2023;38(6):2473–85. https://doi.org/10.1007/s00146-021-01377-9
  98. 98. Uddin M, Chowdhury A, Kabir MA. Legal and ethical aspects of deploying artificial intelligence in climate-smart agriculture. AI Soc. 2024;39(1):221–34. https://doi.org/10.1007/s00146-022-01421-2

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