AI- and Nano-Enabled Precision Agriculture: Frontier Approaches for Stress Tolerance in Plants

Saima Iqbal; Rida Batool; Zeeshan Ahmad; Abdul Wahab; Mujtaba ul Hassan; Ammarah Riaz; Urooj Farid; Sana Azeem Kiani; Nirma Mubeen; Ayesha Fazal

doi:10.25159/3005-2602/21827

Authors

Saima Iqbal University of Mianwali
Rida Batool Quaid-i-Azam University https://orcid.org/0009-0007-8633-6320
Zeeshan Ahmad COMSATS University Islamabad https://orcid.org/0009-0009-9459-659X
Abdul Wahab COMSATS University Islamabad https://orcid.org/0009-0008-7211-1232
Mujtaba ul Hassan COMSATS University Islamabad https://orcid.org/0009-0004-6702-0118
Ammarah Riaz COMSATS University Islamabad
Urooj Farid COMSATS University Islamabad https://orcid.org/0009-0007-7797-3803
Sana Azeem Kiani Mirpur University of Science and Technology
Nirma Mubeen University of Narowal https://orcid.org/0009-0005-7353-4198
Ayesha Fazal COMSATS University Islamabad https://orcid.org/0009-0007-5328-267X

DOI:

https://doi.org/10.25159/3005-2602/21827

Keywords:

artificial intelligence, nanotechnology, precision agriculture, tolerance to plant stress, smart crop management, nanosensors, sustainable agriculture, climate-resilient farming

Abstract

Increasing climatic variability and the intensifying biotic and abiotic stresses pose major challenges to global crop productivity, emphasising the urgent need for resource-efficient and sustainable agricultural strategies. Artificial intelligence (AI) and nanotechnology have emerged as complementary technologies capable of enhancing precision agriculture through early stress detection, predictive analytics, and targeted agro-input delivery. This review uniquely integrates recent advances in AI-driven predictive analytics and nano-enabled delivery systems within an agricultural framework of closed-loop precision. It highlights their synergistic role in improving plant stress tolerance and climate-resilient crop production. AI approaches to integrating machine learning, deep learning, and multimodal data fusion enable real-time stress monitoring, hotspot detection, and prediction of stress–risk windows for timely site-specific interventions. Concurrently, nano-engineered formulations enhance the efficiency of the use of nutrients, stabilise bioactive compounds, and enable controlled release mechanisms while reducing environmental losses. The convergence of these technologies supports adaptive crop management systems that integrate stress sensing, predictive decision-making, targeted delivery, and response verification. Despite significant progress, challenges remain regarding field-scale validation, nanoparticle environmental fate, AI model generalisation, and regulatory governance. Future priorities include biodegradable nano formulations, uncertainty-aware AI systems, standardised dosimetry, and adaptive multimodal AI platforms for diverse agroecosystems. Overall, the integration of AI and nanotechnology offers a scalable and sustainable pathway towards climate-resilient agriculture by improving yield stability, optimising resource usage, and minimising ecological impacts.

References

[1] H. M. Al-Amin et al., “Current Scenario and Challenges for Agricultural Sustainability,” in Climate Change and Soil-Water-Plant Nexus: Agriculture and Environment, M. M. Rahman, J. C. Biswas and R. S. Meena, Eds., Singapore: Springer, 2024, pp. 433–‍454. doi: 10.1007/978-981-97-6635-2_14. DOI: https://doi.org/10.1007/978-981-97-6635-2_14

[2] C. Bell and A. Egon, “Plant Stress Tolerance Mechanisms,” EasyChair Preprint 14781, Sep. 2024.

[3] D. Rodriguez et al., “Agronomic adaptations to heat stress: Sowing summer crops earlier,” Field Crops Res., vol. 318, p. 109592, Nov. 2024, doi: 10.1016/j.fcr.2024.109592. DOI: https://doi.org/10.1016/j.fcr.2024.109592

[4] S. Roy, P. Mathur, A. P. Chakraborty and S. P. Saha, Plant Stress: Challenges and Management in the New Decade, Springer, 2022. DOI: https://doi.org/10.1007/978-3-030-95365-2

[5] S. Getahun, H. Kefale and Y. Gelaye, “Application of Precision Agriculture Technologies for Sustainable Crop Production and Environmental Sustainability: A Systematic Review,” Sci. World J., vol. 2024, p. 2126734, Oct. 2024, doi: 10.1155/2024/2126734. DOI: https://doi.org/10.1155/2024/2126734

[6] P. Zhang et al., “Nanotechnology and artificial intelligence to enable sustainable and precision agriculture,” Nat. Plants, vol. 7, pp. 864–876, Jun. 2021, doi: 10.1038/s41477-021-00946-6. DOI: https://doi.org/10.1038/s41477-021-00946-6

[7] S. Ogunbunmi et al., “Internet of things weather monitoring system,” World J. Adv. Res. Rev., vol. 22, no. 2, pp. 2099–2110, May 2024, doi: 10.30574/wjarr.2024.22.2.1647. DOI: https://doi.org/10.30574/wjarr.2024.22.2.1647

[8] A. Bano, T. A. Qadri, Manoor and N. Khan, “Bioactive metabolites of plants and microbes and their role in agricultural sustainability and mitigation of plant stress,” S. Afr. J. Bot., vol. 159, pp. 98–109, Aug. 2023, doi: 10.1016/j.sajb.2023.05.049. DOI: https://doi.org/10.1016/j.sajb.2023.05.049

[9] D. Mishra et al., “Chapter 16 – Endophytic fungi as biostimulants: an efficient tool for plant growth promotion under biotic and abiotic stress conditions,” in Biostimulants for Crops from Seed Germination to Plant Development, S. Gupta and J. van Staden, Eds., Academic Press, pp. 365–391, 2021, doi: 10.1016/B978-0-12-823048-0.00019-8. DOI: https://doi.org/10.1016/B978-0-12-823048-0.00019-8

[10] S. Kaur et al., “How do plants defend themselves against pathogens-Biochemical mechanisms and genetic interventions,” Physiol. Mol. Biol. Plants, vol. 28, pp. 485–504, Mar. 2022, doi: 10.1007/s12298-022-01146-y. DOI: https://doi.org/10.1007/s12298-022-01146-y

[11] A. Pfenning-Butterworth et al., “Interconnecting global threats: climate change, biodiversity loss, and infectious diseases,” Lancet Planet. Health, vol. 8, no. 4, pp. E270–‍E283, Apr. 2024, doi: 10.1016/S2542-5196(24)00021-4. DOI: https://doi.org/10.1016/S2542-5196(24)00021-4

[12] T. A. Qadri, A. Khan, M. Anas and M. R. Khan, “Metabolic Adjustments to Abiotic Stress Tolerance,” in Plant Stress Response, Ethics Int. Press, p. 110, 2025.

[13] Q. M. Imran, N. Falak, A. Hussain, B.-G. Mun and B.-W. Yun, “Abiotic Stress in Plants; Stress Perception to Molecular Response and Role of Biotechnological Tools in Stress Resistance,” Agr., vol. 11, no. 8, p. 1579, Aug. 2021, https://doi.org/10.3390/agronomy11081579. DOI: https://doi.org/10.3390/agronomy11081579

[14] R. A. Khan, Z. Noreen, A. Khan and T. A. Qadri, “5. Nutri-Priming as an Efficient Means to Improve Germination and Growth of Mung bean (Vigna radiata L.) Grown Under NaCl Stress,” Pure Appl. Biol., vol. 10, no. 1, pp. 34–45, Mar. 2021, https://doi.org/10.19045/bspab.2021.100005. DOI: https://doi.org/10.19045/bspab.2021.100005

[15] K. Moatter et al., “17. Effects of seed priming with PbSO4 and FeSO4 on germination and growth of seedlings of Beta vulgaris L. under NaCl stress,” Pure Appl. Biol., vol. 9, no. 2, pp. 1405–1423, Jun. 2020, doi: 10.19045/bspab.2020.90147. DOI: https://doi.org/10.19045/bspab.2020.90147

[16] M. Haghpanah, S. Hashemipetroudi, A. Arzani and F. Araniti, “Drought Tolerance in Plants: Physiological and Molecular Responses,” Plants, vol. 13, no. 21, p. 2962, Oct. 2024, doi: 10.3390/plants13212962. DOI: https://doi.org/10.3390/plants13212962

[17] S. Zareen, A. Ali and D.-J. Yun, “Significance of ABA Biosynthesis in Plant Adaptation to Drought Stress,” J. Plant Biol., vol. 67, pp. 175–184, Jun. 2024, doi: 10.1007/s12374-024-09425-9. DOI: https://doi.org/10.1007/s12374-024-09425-9

[18] C. Wu, X. Shen, W. Feng, P. Li and Y. Chen, “Agricultural nanotechnology,” Coord. Chem. Rev., vol. 543, p. 216906, Nov. 2025, doi: 10.1016/j.ccr.2025.216906. DOI: https://doi.org/10.1016/j.ccr.2025.216906

[19] A. Khan et al., “Eco-friendly synthesis of copper oxide-silver bimetallic nanoparticles using Withania coagulans: Characterization and biomedical potential through antioxidant, antibacterial, and catalytic activities,” Inorg. Chem. Commun., vol. 181, p. 115131, Nov. 2025, doi: 10.1016/j.inoche.2025.115131. DOI: https://doi.org/10.1016/j.inoche.2025.115131

[20] A. Khan et al., “Biogenic Nanoparticles and Green Nanocomposites as Sustainable Antimicrobial Strategies Against Bacteria, Fungi and Viruses,” NH, vol. 5, pp. 1–31, Feb. 2026, doi: 10.25159/3005-2602/20247. DOI: https://doi.org/10.25159/3005-2602/20247

[21] H. Wu and Z. Li, “Nano-enabled agriculture: How do nanoparticles cross barriers in plants?” Plant Commun., vol. 3, no. 6, p. 100346, Nov. 2022, doi: 10.1016/j.xplc.2022.100346. DOI: https://doi.org/10.1016/j.xplc.2022.100346

[22] W. Z. Ansari et al., “Nanotechnology Primer: Principles, Types, and Physicochemical Properties of Nanoparticles,” in Managing Plant Viral Diseases With Advanced Nanoparticles, J. Iqbal et al., Eds., IGI Global Sci. Publish., pp. 25–62, 2026, doi: 10.4018/979-8-3373-4913-8.ch002. DOI: https://doi.org/10.4018/979-8-3373-4913-8.ch002

[23] L. Gali, A. Pirozzi and F. Donsì, “Biopolymer- and Lipid-Based Carriers for the Delivery of Plant-Based Ingredients,” Pharm., vol. 15, no. 3, p. 927, Mar. 2023, doi: 10.3390/pharmaceutics15030927. DOI: https://doi.org/10.3390/pharmaceutics15030927

[24] T. A. Qadri et al., “Nanofertilizers for Sustainable Agriculture: Mechanisms, Benefits, and Environmental Challenges,” BioNanoSci., vol. 16, p. 164, Feb. 2026, doi: 10.1007/s12668-025-02370-y. DOI: https://doi.org/10.1007/s12668-025-02370-y

[25] Z. Ahmad et al., “Enhancing plant resilience: Nanotech solutions for sustainable agriculture,” Heliyon, vol. 10, no. 23, p. e40735, Dec. 2024, doi: 10.1016/j.heliyon.2024.e40735. DOI: https://doi.org/10.1016/j.heliyon.2024.e40735

[26] N. Yadav, S. Bora, B. Devi, C. Upadhyay and P. Singh, “Nanoparticle-mediated defense priming: A review of strategies for enhancing plant resilience against biotic and abiotic stresses,” Plant Physiol. Biochem., vol. 213, p. 108796, Aug. 2024, doi: 10.1016/j.plaphy.2024.108796. DOI: https://doi.org/10.1016/j.plaphy.2024.108796

[27] O. Bamisile, C. Acen, D. Cai, Q. Huang and I. Staffell, “The environmental factors affecting solar photovoltaic output,” Renew. Sust. Energy Rev., vol. 208, p. 115073, Feb. 2025, doi: 10.1016/j.rser.2024.115073. DOI: https://doi.org/10.1016/j.rser.2024.115073

[28] H. Shi, Y. Guo and Z. Dong, “Controlled release system of pesticide nanoparticles based on intelligent response: current status and development trend,” Pestic. Biochem. Physiol., vol. 216, p. 106710, Jan. 2026, doi: 10.1016/j.pestbp.2025.106710. DOI: https://doi.org/10.1016/j.pestbp.2025.106710

[29] S. A. Atanda, R. O. Shaibu and F. O. Agunbiade, “Nanoparticles in agriculture: balancing food security and environmental sustainability,” Discov. Agric., vol. 3, p. 26, Feb. 2025, doi: 10.1007/s44279-025-00159-x. DOI: https://doi.org/10.1007/s44279-025-00159-x

[30] M. Ijaz et al., “The Intervention of Nanotechnology in the Management of Plant Biotic Stresses for Sustainable Agricultural System,” in Molecular Dynamics of Plant Stress and its Management, M. Shahid and R. Gaur, Eds., Singapore: Springer, pp. 513–536, 2024, doi: 10.1007/978-981-97-1699-9_23. DOI: https://doi.org/10.1007/978-981-97-1699-9_23

[31] S. Mahra et al., “Harnessing nanotechnology for sustainable agriculture: From seed priming to encapsulation,” Plant Nano Biol., vol. 11, p. 100124, Feb. 2025, doi: 10.1016/j.plana.2024.100124. DOI: https://doi.org/10.1016/j.plana.2024.100124

[32] A. Goyal et al., “Emerging trends and perspectives on nano-fertilizers for sustainable agriculture,” Discover Nano, vol. 20, p. 97, Jun. 2025, doi: 10.1186/s11671-025-04286-8. DOI: https://doi.org/10.1186/s11671-025-04286-8

[33] A. V. Samrot et al., “Production, characterization and application of nanocarriers made of polysaccharides, proteins, bio-polyesters and other biopolymers: A review,” Int. J. Biol. Macromol., vol. 165, p. 3088–3105, Dec. 2020, doi: 10.1016/j.ijbiomac.2020.10.104. DOI: https://doi.org/10.1016/j.ijbiomac.2020.10.104

[34] X. Jin et al., “Biocompatible and Biodegradable Nanocarriers for Targeted Drug Delivery in Precision Medicine,” Biomimetics, vol. 10, no. 7, p. 430, Jun. 2025, doi: 10.3390/biomimetics10070430. DOI: https://doi.org/10.3390/biomimetics10070430

[35] R. Kumar et al., “Lipid based nanocarriers: Production techniques, concepts, and commercialization aspect,” J. Drug Del. Sci. Technol., vol. 74, p. 103526, Aug. 2022, doi: 10.1016/j.jddst.2022.103526. DOI: https://doi.org/10.1016/j.jddst.2022.103526

[36] M. F. Khalid et al., “Nanoparticles: The Plant Saviour under Abiotic Stresses,” Nanomater., vol. 12, no. 21, p. 3915, Nov. 2022, doi: 10.3390/nano12213915. DOI: https://doi.org/10.3390/nano12213915

[37] I. Jośko and G. Brunetti, “Role of Metal and Metal-Oxide Nanoparticles in Crop Mineral Nutrient Deficiency Stress Adaptation and Mitigation, in Nanobiotechnology for Abiotic Stress Adaptation and Mitigation in Agricultural Crops, A. Husen, Ed., Singapore: Springer, pp. 129–164, 2025, doi: 10.1007/978-981-96-9298-9_5. DOI: https://doi.org/10.1007/978-981-96-9298-9_5

[38] K. B. Seljak, P. Kocbek and M. Gašperlin, “Mesoporous silica nanoparticles as delivery carriers: An overview of drug loading techniques,” J. Drug Del. Sci. Technol., vol. 59, p. 101906, Oct. 2020, doi: 10.1016/j.jddst.2020.101906. DOI: https://doi.org/10.1016/j.jddst.2020.101906

[39] K. M. Singh, S. Baksi, S. Rani, A. B. Jha, R. S. Dubey and P. Sharma, “Glycine betaine-loaded nanoparticles: a novel approach for enhancing crop tolerance to salinity and drought stress,” Plant Physiol. Biochem., vol. 228, p. 110193, Nov. 2025, doi: 10.1016/j.plaphy.2025.110193. DOI: https://doi.org/10.1016/j.plaphy.2025.110193

[40] B. Pudhuvai et al., “Nano-Fertilizers (NFs) for Resurgence in Nutrient Use Efficiency (NUE): a Sustainable Agricultural Strategy, Curr. Pollution Rep., vol. 11, p. 1, Oct. 2024, doi: 10.1007/s40726-024-00331-9. DOI: https://doi.org/10.1007/s40726-024-00331-9

[41] R. Hamed, S. Jodeh and R. Alkowni, “Nano bio fertilizer capsules for sustainable agriculture,” Sci. Rep., vol. 14, p. 13646, Jun. 2024, doi: 10.1038/s41598-024-62973-5. DOI: https://doi.org/10.1038/s41598-024-62973-5

[42] J. Victoria et al., “Encapsulated nanopesticides application in plant protection: Quo vadis?” Plant Physiol. Biochem., vol. 206, p. 108225, Jan. 2024, doi: 10.1016/j.plaphy.2023.108225. DOI: https://doi.org/10.1016/j.plaphy.2023.108225

[43] E. Sarikhani, K. Mahato, A. Casanova, K. Rahmani, J. Wang and Z. Jahed, “Nanosensors for real-time intracellular analytics,” Nat. Nanotechnol., vol. 20, pp. 1374–1387, Oct. 2025, doi: 10.1038/s41565-025-02032-w. DOI: https://doi.org/10.1038/s41565-025-02032-w

[44] A. A. Mana, A. Allouhi, A. Hamrani, S. Rehman, I. el Jamaoui and K. Jayachandran, “Sustainable AI-based production agriculture: Exploring AI applications and implications in agricultural practices,” Smart Agric. Technol., vol. 7, p. 100416, Mar. 2024, doi: 10.1016/j.atech.2024.100416. DOI: https://doi.org/10.1016/j.atech.2024.100416

[45] M. Elhaddad and S. Hamam, “AI-Driven Clinical Decision Support Systems: An Ongoing Pursuit of Potential,” Cureus, vol. 16, no. 4, p. e57728, Apr. 2024, doi: 10.7759/cureus.57728. DOI: https://doi.org/10.7759/cureus.57728

[46] E. Bwambale, F. K. Abagale and G. K. Anornu, “Towards a modelling, optimization and predictive control framework for smart irrigation,” Heliyon, vol. 10, no. 18, p. e38095, Sept. 2024, doi: 10.1016/j.heliyon.2024.e38095. DOI: https://doi.org/10.1016/j.heliyon.2024.e38095

[47] N. Karanikas, “Performance and Evaluation,” in Professional Generalism in a Hyper-specialised World, N. Karanikas, Ed., Singapore: Springer, pp. 213–217, 2025, doi: 10.1007/978-981-96-4039-3_18. DOI: https://doi.org/10.1007/978-981-96-4039-3_18

[48] B. Bashir, A. Ali, R. A. Khan, M. Iftikhar and V. Altun, “Nanoparticle-Based Immunoassays and Rapid Field Diagnostic Tools,” in Managing Plant Viral Diseases With Advanced Nanoparticles, J. Iqbal, M. Anas, A. Madan and G. Murtaza, IGI Glob. Sci. Pub., pp. 263–286, 2026, doi: 10.4018/979-8-3373-4913-8.ch009. DOI: https://doi.org/10.4018/979-8-3373-4913-8.ch009

[49] D. B. Olawade, O. Z. Wada, A. O. Ige, B. I. Egbewole, A. Olojo and B. I. Oladapo, “Artificial intelligence in environmental monitoring: Advancements, challenges, and future directions,” Hyg. Environ. Health Adv., vol. 12, p. 100114, Dec. 2024, doi: 10.1016/j.heha.2024.100114. DOI: https://doi.org/10.1016/j.heha.2024.100114

[50] Z. Zhou, Y. Majeed, G. D. Naranjo and E. M. T. Gambacorta, “Assessment for crop water stress with infrared thermal imagery in precision agriculture: A review and future prospects for deep learning applications,” Comp. Electron. Agric., vol. 182, p. 106019, Mar. 2021, doi: 10.1016/j.compag.2021.106019. DOI: https://doi.org/10.1016/j.compag.2021.106019

[51] R. Zia, M. S. Nawaz, M. J. Siddique, S. Hakim and A. Imran, “Plant survival under drought stress: Implications, adaptive responses, and integrated rhizosphere management strategy for stress mitigation,” Microbiol. Res., vol. 242, p. 126626, Jan. 2021, doi: 10.1016/j.micres.2020.126626. DOI: https://doi.org/10.1016/j.micres.2020.126626

[52] M. Vahidi, S. Shafian and W. H. Frame, “Depth-specific soil moisture estimation in vegetated corn fields using a canopy-informed model: A fusion of RGB-thermal drone data and machine learning,” Agric. Water Manag., vol. 307, p. 109213, Feb. 2025, doi: 10.1016/j.agwat.2024.109213. DOI: https://doi.org/10.1016/j.agwat.2024.109213

[53] H. Mishra and D. Mishra, “AI for Data-Driven Decision-Making in Smart Agriculture: From Field to Farm Management,” in Artificial Intelligence Techniques in Smart Agriculture, S. S. Chouhan, A. Saxena, U. P. Singh and S. Jain, Eds., Singapore: Springer, pp. 173–193, 2024, doi: 10.1007/978-981-97-5878-4_11. DOI: https://doi.org/10.1007/978-981-97-5878-4_11

[54] R. Gautron, O.-A. Maillard, P. Preux, M. Corbeels and R. Sabbadin, “Reinforcement learning for crop management support: Review, prospects and challenges,” Comp. Electron. Agric., vol. 200, p. 107182, Sept. 2022, doi: 10.1016/j.compag.2022.107182. DOI: https://doi.org/10.1016/j.compag.2022.107182

[55] C. Wang, C. Wang, W. Li and H. Wang, “A brief survey on RGB-D semantic segmentation using deep learning,” Displays, vol. 70, pp. 102080, Dec. 2021, doi: 10.1016/j.displa.2021.102080. DOI: https://doi.org/10.1016/j.displa.2021.102080

[56] S. S. Mali et al., “Integrating UAV-based multispectral and thermal infrared imageries with machine learning for predicting water stress in winter wheat,” Precision Agric., vol. 26, p. 44, Apr. 2025, doi: 10.1007/s11119-025-10239-z. DOI: https://doi.org/10.1007/s11119-025-10239-z

[57] Y. A. Rajwade, N. S. Chandel, K. Dubey, S. Anakkallan, K. Upender and D. Jat, “Assessment of water stress in rainfed maize using RGB and thermal imagery,” Arab. J. Geosci., vol. 16, p. 119, Jan. 2023, doi: 10.1007/s12517-023-11198-3. DOI: https://doi.org/10.1007/s12517-023-11198-3

[58] I. Ivanova, “Soil moisture forecasting from sensors-based soil moisture, weather and irrigation observations: A systematic review,” Smart Agric. Technol., vol. 10, p. 100692, Mar. 2025, doi: 10.1016/j.atech.2024.100692. DOI: https://doi.org/10.1016/j.atech.2024.100692

[59] Y. Sui et al., “Predicting the spatial distribution of soil salinity based on multi-temporal multispectral images and environmental covariates,” Comp. Electron. Agric., vol. 231, p. 109970, Apr. 2025, doi: 10.1016/j.compag.2025.109970. DOI: https://doi.org/10.1016/j.compag.2025.109970

[60] A. Layton, J. V. Krogmeier, A. Ault and D. R. Buckmaster, “From yield history to productivity zone identification with hidden Markov random fields,” Precision Agric., vol. 21, p. 762–781. doi: 10.1007/s11119-019-09694-2. DOI: https://doi.org/10.1007/s11119-019-09694-2

[61] S. Azimi, “Intelligent nanoparticle design: Unlocking the potential of AI for transformative drug delivery,” Curr. Opin. Biomed. Eng., vol. 36, p. 100625, Dec. 2025, doi: 10.1016/j.cobme.2025.100625. DOI: https://doi.org/10.1016/j.cobme.2025.100625

[62] S. Sinha and Y. M. Lee, “Challenges with developing and deploying AI models and applications in industrial systems,” Discov. Artif. Intell., vol. 4, p. 55, Aug. 2024, doi: 10.1007/s44163-024-00151-2. DOI: https://doi.org/10.1007/s44163-024-00151-2

[63] R. Mash, J. Nyasulu, Z. Malan and L. Hirschhorn, “Understanding implementation research,” Afr. J. Prim. Health Care Fam. Med., vol. 17, no. 2, p. e1–e7, Jun. 2025, https://phcfm.org/index.php/phcfm/article/view/4934/8308.

[64] M. Zaerpour, S. M. Papalexiou and A. Pietroniro, “Increasing tree canopy lowers urban air temperature by up to 1.5 °C in heat-prone areas,” Urban Sustain., vol. 5, p. 92, Nov. 2025, doi: 10.1038/s42949-025-00277-x. DOI: https://doi.org/10.1038/s42949-025-00277-x

[65] J. Sayyad and K. Attarde, “Synergizing Nanotechnology and Artificial Intelligence for Society 5.0 Advancement Through Intelligent Systems,” in NanoMind: Exploring Synergies in Nanotechnology and Machine Learning, S. Mahajan, S. Das, A. Rocha, D. B. Roy and P. Chawla, Eds., Switzerland: Springer, pp. 225–249, 2025, doi: 10.1007/978-3-031-77296-2_10. DOI: https://doi.org/10.1007/978-3-031-77296-2_10

[66] N. Aijaz, H. Lan, T. Raza, M. Yaqub, R. Iqbal and M. S. Pathan, “Artificial intelligence in agriculture: Advancing crop productivity and sustainability,” J. Agric. Food Res., vol. 20, p. 101762, Apr. 2025, doi: 10.1016/j.jafr.2025.101762. DOI: https://doi.org/10.1016/j.jafr.2025.101762

[67] C. D. Selvam and Y. Devarajan, “Investigation of Emerging Technologies in Agriculture: An In-depth Look at Smart Farming, Nano-agriculture, AI, and Big Data,” J. Biosyst. Eng., vol. 50, pp. 170–192, Jun. 2025, doi: 10.1007/s42853-025-00258-z. DOI: https://doi.org/10.1007/s42853-025-00258-z

[68] M. Shirzad, A. Salahvarzi, S. Fathi-karkan, A. Rahdar, M. Guettari and S. Pandey, “Green nanocarriers and Biodegradable Systems for sustainable drug delivery solutions,” J. Drug Deliv. Sci. Technol., vol. 111, p. 107208, Sept. 2025, doi: 10.1016/j.jddst.2025.107208. DOI: https://doi.org/10.1016/j.jddst.2025.107208

[69] A. K. Srivastav and P. Das, “Challenges and Barriers to Adoption,” in Biotechnology and IoT in Agriculture and Food Production, D. A. K. Srivastav and D. P. Das, Eds., Berkeley, CA: Apress, pp. 161–168, 2025, doi: 10.1007/979-8-8688-1469-3_18. DOI: https://doi.org/10.1007/979-8-8688-1469-3_18