Authors : Dr. Sonny Gad Attipoe

Volume/Issue : Volume 11 - 2026, Issue 8 - August

Google Scholar : https://tinyurl.com/nhcv3zmx

DOI : https://doi.org/10.38124/ijisrt/26aug707

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : Climate change is increasingly disrupting agricultural systems worldwide through more frequent extreme weather events, shifting rainfall patterns, and rising temperatures. These changes contribute to reduced crop yields, unstable food supplies, and heightened production risks, especially in vulnerable developing regions. In response, ClimateSmart Agriculture (CSA) has been developed as a strategic framework to improve agricultural productivity while enhancing resilience and promoting environmentally sustainable farming practices. Within this framework, Artificial Intelligence (AI) is increasingly recognized as a transformative tool capable of supporting data-driven agricultural decision-making. This review systematically synthesizes recent literature on AI applications in CSA, focusing on four key thematic areas: crop yield prediction, weather and climate forecasting, irrigation and water management, and pest and disease detection under climate stress. Peer-reviewed articles published between 2020 and 2026 were retrieved from Google Scholar, Semantic Scholar, and Crossref. A total of 170 studies were initially identified, of which 58 highly relevant articles were selected following screening based on relevance, quality, and thematic alignment. Findings indicate that AIbased systems significantly improve predictive accuracy, optimize resource use, and enhance early warning capabilities across agricultural systems. Machine learning, deep learning, remote sensing, IoT, and big data analytics are widely applied to support precision agriculture and climate adaptation strategies. However, key challenges such as data scarcity in developing regions, high implementation costs, limited farmer adoption, and climate uncertainty continue to constrain widespread adoption. The review concludes that AI holds strong potential to transform CSA by improving productivity, resilience, and sustainability. Nonetheless, its effectiveness depends on the development of accessible, affordable, and context-specific solutions supported by strong policy frameworks and improved digital infrastructure.

Keywords : Machine Learning; Remote Sensing; Climate Resilience; Digital Infrastructure.

References :

1. AbdelRahman, M. A. E. (2026). Smart water for sustainable agriculture through climate resilient assessment and integrated soil water crop management. Discover Water, 6(1), 33. https://doi.org/10.1007/s43832-026-00354-x.
2. Ahmad, A., Saraswat, D., & El Gamal, A. (2023). A survey on using deep learning techniques for plant disease diagnosis and recommendations for development of appropriate tools.  Smart Agricultural Technology, 3, 100083.     https://doi.org/10.1016/j.atech.2022.100083 .
3. Ahmed, Z., Gui, D., Murtaza, G., Yunfei, L., & Ali, S. (2023). An overview of smart irrigation management for improving water productivity under climate change in drylands. Agronomy, 13(8), 2113. https://doi.org/10.3390/agronomy13082113.
4. Akanbi, M. B., Adedotun, K. J., Raji, A. K., & Banjoko, I. K. (2025). Optimizing water resource management in agriculture using AI-powered solar irrigation systems. Journal of Science Innovation and Technology Research, 5(9), 51-66.  https://doi.org/10.70382/ajsitr.v7i9.011.
5. Al khatib, A. M. G., & Alshaib, B. M. (2025). Smart irrigation systems: Optimizing water use with AI. In Lal, P., Mishra, P. (Eds), Transforming agriculture through artificial intelligence for sustainable food systems, (pp. 73-93). Springer Nature, Singapore. https://doi.org/10.1007/978-981-96-4795-8_5.
6. Ali, A., Hussain, T., & Zahid, A. (2025). Smart irrigation technologies and prosspects for enhacing water use efficiecy for sustainable agriculture. AgriEngeering, 7(4), 106. https://doi.org/10.3390/agriengineering7040106.
7. Ali, F., Rehman, A., Hameed, A., Sarfraz, S., Rajput, N. A., & Atiq, M. (2024). Climate change impact on plant pathogen emergence: Artificial intelligence (AI) approach. Plant quarantine challenges under climate change anxiety, (pp. 281-303). Springer Nature, Switzerland. https://doi.org/10.1007/978-3-031-56011-8_9.
8. Ali, Z., Muhammad, A., Lee, N., & Waqar, M. (2025). Artificial intelligence for sustainable agriculture: A comprehensive review of AI-driven technologies in crop production. Sustainability, 17(5), 2281. https://doi.org/10.3390/su17052281.
9. Alvim, S. J., Guimarães, C. M., Sousa, E. F. D., Garcia, R. F., & Marciano, C. R. (2022). Application of artificial intelligence for irrigation management: A systematic review. Engenharia Agrícola, 42(spe). https://doi.org/10.1590/1809-4430-eng.agric.v42nepe20210159/2022.
10. Amulothu, D. V. R. T., Rodge, R. R., Hasan, W., & Gupta, S. (2024). Machine learning for pest and disease detection in crops. Agriculture 4.0, (pp. 111-132). CRC Press. https://doi.org/10.1201/9781003570219-6.
11. Anand, S., & Sandhu, S. K. (2024). Biotic stress management in field crops using artificial intelligence technologies.  In Pandey, K., Kushwaha, N.L., Pande, C.B., Singh, K.G. (Eds), Artificial Intelligence and Smart Agriculture. Advances in Geographical and Environmental Sciences, (pp. 315-335). Springer Nature, Singapore. https://doi.org/10.1007/978-981-97-0341-8_16.
12. Attipoe, S. G. (2026). Artificial Intelligence Applications in Climate-Smart Agriculture: A Critical Review of Evidence, Implementation and Responsible Innovation. Asian Journal of Advances in Agricultural Research, 26(8), 77-99. https://doi.org/ 10.9734/ajaar/2026/v26i8746.
13. Bara, Z. J., Riasat, S., & Ahsan Khan, M. (2025). Integration of Artificial Intelligence (AI) and Internet of Things (IoT) in smart agriculture. Bincang Sains Dan Teknologi, 4(02), 71–77. https://doi.org/10.56741/bst.v4i02.941.
14. Bayar, J., Ali, N., Cao, Z., Ren, Y., & Dong, Y. (2025). Artificial intelligence of things (AIoT) for precision agriculture: Applications in smart irrigation, nutrient and disease management. Smart Agricultural Technology, 12, 101629. https://doi.org/10.1016/j.atech.2025.101629.
15. Benhmad, T., Rhaimi, C. B., Alomari, S., & Aljuhani, L. (2024). Design and implementation of an integrated IoT and artificial antelligence system for smart irrigation Management. International Journal of Advances in Soft Computing & Its Applications, 16(1), 197-218. https://doi.org/10.15849/ijasca.240330.12.
16. Benos, L., Bechar, A., & Bochtis, D. (2021). Machine learning in agriculture: A comprehensive updated review. Sensors, 21(11), 3758. https://doi.org/10.3390/s21113758.
17. Biswal, K. A., Das, S., & Jana, S. (2025). Emerging plant disease prediction through forewarning model and Artificial Intelligence (AI) under climate change scenario. In Climate Resilient and Sustainable Agriculture: Volume 2: Social and Transformative Strategies, (pp. 155 -196). Springer Nature, Switzerland. https://doi.org/10.1007/978-3-032-04141-8_6.
18. Boursianis, A. D., Papadopoulou, M. S., Diamantoulakis, P., Liopa-Tsakalidi, A., Barouchas, P., Salahas, G., Karagiannidis, G., Wan, S., & Goudos, S. K. (2022). Internet of Things (IoT) and Agricultural Unmanned Aerial Vehicles (UAVs) in smart farming: A comprehensive review. Internet of Things, 18, 100187. https://doi.org/10.1016/j.iot.2020.100187.
19. Bukhari, S. A. S., Mushtaq, A. R., Butt, M. H., Siddique, M. S., & Abbas, M. U. U. H. (2025). AI-driven strategies for predicting and managing insect pest dynamics under climate change. Trends in Animal and Plant Sciences, 5, 46-53. https://doi.org/10.62324/taps/2025.063.
20. Bwambale, E., Abagale, F. K., & Anornu, G. (2022). Smart irrigation monitoring and control strategies for improving water use efficiency in precision agriculture: A review. Agricultural Water Management, 260, 107324. https://doi.org/10.1016/j.agwat.2021.107324.
21. Chavula, P., Kayusi, F., Lungu, G., Mambwe, H., & Uwimbabazi, A. (2024). AI application in climate-smart agricultural technologies: A synthesis study. LatIA, 2, 330-330. https://doi.org/10.62486/latia2025330.
22. Delfani, P., Thuraga, V., Banerjee, B., & Chawade, A. (2024). Integrative approaches in modern agriculture: IoT, ML and AI for disease forecasting amidst climate change. Precision Agriculture, 25, 2589–2613. https://doi.org/10.1007/s11119-024-10164-7.
23. Devi, T., Deepa, N., Gayathri, N., & Rakesh Kumar, S. (2024). AI‐based weather forecasting system for smart agriculture system using a recurrent neural networks (RNN) algorithm. In A. Kumar, P. S. Rathore, A. K. Dubey, A. L. Srivastav, T. Ananth Kumar, & V. Dutt (Eds.), Sustainable management of electronic waste, (pp. 97–112). Wiley. https://doi.org/10.1002/9781394166923.ch5.
24. Dewedar, O. M., El-Shafie, A. F., Marwa, M. A., & Abdelraouf, R. E. (2023). Improvement of irrigation water management using simulation models and artificial intelligence under dry environment conditions In Egypt: A review. Egyptian Journal of Chemistry, 66(11), 93-106. https://doi.org/10.21608/ejchem.2023.151289.6552.
25. Dewitte, S., Cornelis, J. P., Müller, R., & Munteanu, A. (2021). Artificial intelligence revolutionises weather forecast, climate monitoring and decadal prediction. Remote Sensing, 13(16), 3209. https://doi.org/10.3390/rs13163209.
26. Dhanaraj, R. K., Maragatharajan, M., Sureshkumar, A., & Balakannan, S. P., (2025). On-device AI for climate-resilient farming with intelligent crop yield prediction using lightweight models on smart agricultural devices. Scientific Reports, 15(1), 31195. https://doi.org/10.1038/s41598-025-16014-4.
27. Dhanke, J. A., Srivastava, D., Menaga, D., Raj, R., Kumar, K. V., Jangir, P., & Mani, P. (2025). Climate-Based AI-Powered precision irrigation: Sustainably smart agriculture frameworks for maximum crop yields. Remote Sensing in Earth Systems Sciences, 8(1), 161-172. https://doi.org/10.1007/s41976-024-00174-4.
28. Gadhiya, R. (2025). Agricultural productivity: An AI-based crop yield prediction system using climate and soil data. SSRN Electronic Journal. Available at  SSRN: https://ssrn.com/abstract=5237252.
29. Goel, M., & Pandey, M. (2024). Crop yield prediction using AI: A review. 2024 IEEE 2nd international conference on disruptive technologies (ICDT), (pp. 1547-1553). Greater Noida, India. 15-16 March, 2024.  https://doi.org/10.1109/icdt61202.2024.10489432.
30. Gupta, S., Mohanty, S., & Behera, D. K. (2025). AI-based yield prediction: A thorough review. Indian Journal of Science and Technology, 18(10), 822-838. https://doi.org/10.17485/ijst/v18i10.175.
31. Hachimi, C. E., Belaqziz, S., Khabba, S., Sebbar, B., Dhiba, D., & Chehbouni, A. (2022). Smart weather data management based on artificial intelligence and big data analytics for precision agriculture. Agriculture, 13(1), 95. https://doi.org/10.3390/agriculture13010095.
32. Hamdaoui, H., Hsana, Y., Hamdi, I., Al kaddouri, H., & Kouddane, N.-E. (2024). revolutionizing agriculture: A comprehensive review of AI-enabled precision irrigation and water quality forecasting. Basrah Journal of Agricultural Sciences, 37(2), 354–380. https://doi.org/10.37077/25200860.2024.37.2.26.
33. Hernández Hernández, G. C., Gómez Gómez, J., & Jiménez-Cabas, J. (2025). Predictive models based on artificial intelligence to estimate crop yield: A literature review. Agriculture, 15(23), 2438. https://doi.org/10.3390/agriculture15232438.
34. Hu, T., Zhang, X., Bohrer, G., Liu, Y., Zhou, Y., Martin, J., LI. Y., & Zhao, K. (2023). Crop yield prediction via explainable AI and interpretable machine learning: Dangers of black box models for evaluating climate change impacts on crop yield. Agricultural and Forest Meteorology, 336, 109458. https://doi.org/10.1016/j.agrformet.2023.109458.
35. Islam, M., Bijjahalli, S., Fahey, T., Gardi, A., Sabatini, R., & Lamb, D. W. (2024). Destructive and non-destructive measurement approaches and the application of AI models in precision agriculture: A review. Precision Agriculture, 25, 1127–1180. https://doi.org/10.1007/s11119-024-10112-5.
36. Jabed, M. A., & Murad, M. A. A. (2024). Crop yield prediction in agriculture: A comprehensive review of machine learning and deep learning approaches, with insights for future research and sustainability. Heliyon, 10(24), e40836. https://doi.org/10.1016/j.heliyon.2024.e40836.
37. Javaid, M., Haleem, A., Singh, R. P., Suman, R., & Gonzalez, E. S. (2022). Understanding the adoption of Industry 4.0 technologies in improving environmental sustainability. Sustainable Operations and Computers, 3, 203–217. https://doi.org/10.1016/j.susoc.2022.01.008.
38. Kavipriya, J., & Vadivu, G. (2024). Exploring crop yield prediction with remote sensing imagery and AI. In 2024 International conference on advances in computing, communication and applied informatics (ACCAI), (pp.1-5). IEEE. https://doi.org/10.1109/accai61061.2024.10601854.
39. Kumari, K., Mirzakhani Nafchi, A., Mirzaee, S., & Abdalla, A. (2025). AI-driven future farming: achieving climate-smart and sustainable agriculture. AgriEngineering, 7(3), 89.  https://doi.org/10.3390/agriengineering7030089.
40. Li, C., & Wang, M. (2024). Pest and disease management in agricultural production with artificial intelligence: Innovative applications and development trends. Advances in Resources Research, 4(3), 381-401. https://doi.org/10.50908/arr.4.3_381.
41. Mandour, H., Helmi, R. Y., Safhi, F. A., Alshaya, D. S., Jala, A. S., Alharbi, K., & Hassanin, A. A. (2025). Artificial intelligence for climate-smart agriculture: Enhancing food security and plant adaptation. Notulae Botanicae Horti Agrobotanici Cluj-Napoca, 53(4), 14796-14796. https://doi.org/10.15835/nbha53414796.
42. Masasi, J., Ng’ombe, J. N., & Masasi, B. (2024). Artificial Intelligence in agriculture: Current trends and innovations. Big Data in Agriculture, 6(2), 113–116. https://doi.org/10.26480/bda.02.2024.113.116.
43. Mat Pauzi, N. A., Mustaza, S. M., Zainal, N., Mohamed Moubark, A., & Mohd Zaman, M. H. (2025). Artificial intelligence in precision agriculture: A review. Jurnal Kejuruteraan, 37(3), 1515–1538. https://doi.org/10.17576/jkukm-2025-37 (3)-34.
44. Mishra, H., Kayusi, F., Adamopoulos, I., & Olayinka, O. T. (2026). AI-driven irrigation and water management systems. In Robotics and Intelligent Machines in Smart Agriculture: Emerging Systems and Applications, 1st ed. (pp. 270-309). CRC Press. https://doi.org/10.1201/9781003562627.
45. Naqvi, S. M. Z. A., Hussain, S., Awais, M., Tahir, M. N., Saleem, S. R., Al-Yarimi, F. A. M., Ashurov, M., Saidani, O., Khan, M. I., Wu, J., Wei, Z., & Hu, J. (2025). Climate-resilient water management: Leveraging IoT and AI for sustainable agriculture. Egyptian Informatics Journal, 30, 100691. https://doi.org/10.1016/j.eij.2025.100691.
46. Patidar, A., Chaudhary, R., Mishra, N., Rajpoot, S. K., Prasad, S. K., & Bhushan, C. (2025). Smart water systems: AI and IoT in precision irrigation. In Singh, A.K., Kumar, P., Singh, S.S. (eds) Sustainable Agriculture Management in Semi-Arid Climates, 2 (pp. 175-193). Springer Nature, Switzerland. https://doi.org/10.1007/978-3-031-94066-8_8.
47. Rana, A., & Lone, A. F. (2025). Integrated artificial intelligence in weather forecasting for agriculture: Opportunities, challenges, and the road ahead. International Journal of Environment and Climate Change, 15(11), 594-609. https://doi.org/10.9734/ijecc/2025/v15i115137.
48. Sharada, K., Choudhary, S. L., Harikrishna, T., Dixit, R. S., Suman, S. K., Ayyappa Chakravarthi, M., & Bhagyalakshmi, L. (2025). GeoAgriGuard: AI-driven pest and disease management with remote sensing for global food security. Remote Sensing in Earth Systems Sciences, 8(2), 409-422. https://doi.org/10.1007/s41976-025-00192-w.
49. Shawon, S. M., Ema, F. B., Mahi, A. K., Niha, F. L., & Zubair, H.T. (2024). Crop yield prediction using machine learning: An extensive and systematic literature review. Smart Agricultural Technology, 10(7), 100718. https://doi.org/10.1016/j.atech.2024.100718.
50. Sivaranjani, T., & Vimal, S. P. (2023). AI Method for Improving Crop Yield Prediction Accuracy Using ANN. Computer Systems Science & Engineering, 47(1), 154-170. https://doi.org/10.32604/csse.2023.036724.
51. Soy, A., & Salwadkar, M. (2024). Smart irrigation systems sing IoT and AI for efficient water resource management. National Journal of Smart Agriculture and Rural Innovation, 2(1), 26-32. https://aasrresearch.com/index.php/NJSARI/article/view/126.
52. Talaviya, T., Shah, D., Patel, N., Yagnik, H., Shah, M. (2020). Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture, 4, 58–73. https://doi.org/10.1016/j.aiia.2020.04.002.
53. Tace, Y., Elfilali, S., Tabaa, M., & Leghris, C. (2023). Implementation of smart irrigation using IoT and artificial intelligence. Mathematical modeling and computing, 10(2), 575-582. https://doi.org/10.23939/mmc2023.02.575.
54. Valleggi, L., & Stefanini, F. M. (2024). A mini-review on data science approaches in crop yield and disease detection. Frontiers in Agronomy, 6, 1352219.  https://doi.org/10.3389/fagro.2024.1352219.
55. Wei, H., Xu, W., Kang, B., Eisner, R., Muleke, A., Rodriguez, D., deVoil, P., Sadras, V., & Monjardino, M. (2024). Irrigation with artificial intelligence: Problems, premises, promises. Human-Centric Intelligent Systems, 4, 187–205. https://doi.org/10.1007/s44230-024-00072-4.
56. Wu, Y., Chen, L., Yang, N., & Zongbao, S. (2025). Research progress of deep learning-based artificial intelligence technology in pest and disease detection and control. Agriculture, 15(19), 2077. https://doi.org/10.3390/agriculture15192077.
57. Xiao, F., Wang, H., Xu, Y., & Zhang, R. (2023). Fruit detection and recognition based on deep learning for automatic harvesting: An overview and review. Agronomy, 13(6), 1625. https://doi.org/10.3390/agronomy13061625.
58. Yu, L., Du, Z., Li, X., Zheng, J., Zhao, Q., Wu, H., Weise, D., Yang, Y., Zhang, Q., Li, X., Ma, X., & Huang, X. (2025). Enhancing global agricultural monitoring system for climate-smart agriculture. Climate Smart Agriculture, 2(1), 100037. https://doi.org/10.1016/j.csag.2024.100037.
59. Zheng, H., Ma, W., & He, Q. (2024). Climate-smart agricultural practices for enhanced farm productivity, income, resilience, and greenhouse gas mitigation: A comprehensive review. Mitigation and Adaptation Strategies for Global Change, 29 (4), 28.  https://doi.org/10.1007/s11027-024-10124-6.