Skip to main navigation menu Skip to main content Skip to site footer
Plant Science Today (PST)

Review Articles

Early Access

Application of artificial intelligence in agriculture and allied sectors: A comprehensive review towards sustainable solutions

DOI
https://doi.org/10.14719/pst.8630
Submitted
2 April 2025
Published
17-06-2025
Versions

Abstract

Sustainability is a holistic goal that can be effectively achieved through the combined efforts of agriculture and its allied sectors. Artificial intelligence (AI) plays a transformative role in this endeavour by bridging sector-specific solutions and integrating them to promote environmental protection and food security. AI is revolutionizing sustainable agriculture, ensuring both food security and environmental protection. The main objective of this article is to comprehensively review the various applications of AI in agriculture and its interlinked sectors like fishery, animal husbandry, forestry, agricultural engineering, horticulture and food science by compiling several previous studies to highlight their role in achieving sustainability and identify research gaps. The literature review was done through databases like Scopus and Google Scholar. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) framework was used to identify, screen and select articles. This article explores the various applications of AI in pest and disease management, weed management, weather forecasting, soil management, greenhouse farming, precision agriculture and yield management. AI has numerous advantages, such as data-driven decision-making, resource management and reduced environmental impacts. This review highlights the implementation of inclusive strategies to achieve sustainability by pointing out the gaps in research, policy and implementation of technologies. The review concludes that integrating AI into agriculture and its allied sectors offer significant benefits that outweigh potential drawbacks, thereby fostering sustainable practices and environmentally friendly innovations.

References

  1. 1. Gustafson JP, Raven PH. World food supply: problems and prospects. In: Sivasankar S, Ellis N, Jankuloski L, Ingelbrecht I, editors. Mutation breeding, genetic diversity and crop adaptation to climate change. 1st ed. UK: CABI; 2021. p. 3-9. https://doi.org/10.1079/9781789249095.0001
  2. 2. Talaviya T, Shah D, Patel N, Yagnik H, Shah M. Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artif Intell Agric. 2020;4:58-73. https://doi.org/10.1016/j.aiia.2020.04.002
  3. 3. Merrill BF, Lu N, Yamaguchi T, Takagaki M, Maruo T, Kozai T, et al. Next evolution of agriculture : A review of innovations in plant factories. In: Pessarakli M, editor. Handbook of photosynthesis. CRC Press; 2018. https://doi.org/10.1201/9781315372136-40
  4. 4. Chandel N, Kumar A, Kumar R. Towards sustainable agriculture: Integrating agronomic practices, environmental physiology and plant nutrition. Int J Plant Soil Sci. 2024;36(6):492-503. https://doi.org/10.9734/ijpss/2024/v36i64651
  5. 5. Sonnino A. Towards Sustainable Food and Agriculture Systems. Rendiconti/Accademia Nazionale Del Xl XLII(Tomo I). 2018:103-14.
  6. 6. Naresh RK, Chandra MS, Vivek S, Charankumar GR, Chaitanya J, et al. The prospect of artificial intelligence (AI) in precision agriculture for farming systems productivity in sub-tropical India: A review. Curr J Appl Sci Technol. 2020;39(48):96-110. https://doi.org/10.9734/cjast/2020/v39i4831205
  7. 7. Nagendraswamy C, Salis A. A review article on artificial intelligence. Ann Biomed Sci Eng. 2021;5:013-4. https://doi.org/10.29328/journal.abse.1001012
  8. 8. Fadziso T. How artificial intelligence improves agricultural productivity and sustainability: A global thematic analysis. Asia Pac J Energy Environ. 2019;6:91-100. https://doi.org/10.18034/apjee.v6i2.542
  9. 9. Erh-Chun, Shan L, Chan P. How artificial intelligence is transforming agriculture. Rev Bus Res. 2023;23(1):59-70. https://doi.org/10.18374/RBR-23-1.6
  10. 10. Javaid M, Haleem A, Khan IH, Suman R. Understanding the potential applications of artificial intelligence in agriculture sector. Adv Agrochem. 2023;2(1):15-30. https://doi.org/10.1016/j.aac.2022.10.001
  11. 11. Kolikipogu R, Darak V, Yennapu R, Reddy S, Sureddi RMK, Kuchipudi R. Agriculture recommender system for precision farming using machine learning (ARS). In: 2023 3rd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA). IEEE; 2023 Dec 21. p. 921-7. https://doi.org/10.1109/ICIMIA60377.2023.10426510
  12. 12. Begum M. Impact of climate change on agriculture and its allied sectors: An overview. Emerg Trends Clim Change. 2022;1(1):19-28. https://doi.org/10.18782/2583-4770.103
  13. 13. Jose Mekha, Parthasarathy V. An automated pest identification and classification in crops using artificial intelligence—A state-of-art-review. Autom Control Comput Sci. 2022;56(3):283-90. https://doi.org/10.3103/S0146411622030038
  14. 14. Prabha R, Kennedy JS, Vanitha G, Sathiah N, Priya MB. Artificial intelligence-powered expert system model for identifying fall armyworm infestation in maize (Zea mays L.). J Appl Nat Sci. 2021;13(4):1339-49. https://doi.org/10.31018/jans.v13i4.3040
  15. 15. Susheel KS, Rajkumar R. A comprehensive review on intelligent techniques in crop pests and diseases. Int J Recent Innov Trends Comput Commun. 2023;11(9):137-49. https://doi.org/10.17762/ijritcc.v11i9.8328
  16. 16. Patil R, Sinkar Y, Ruke A, Kulkarni H, Kadam O. Smart agri-advisor: Integrating chatbot technology with CNN-based crop disease classification for enhanced agricultural decision-making. Int J Eng Trends Technol. 2024;72(7):375-80. https://doi.org/10.14445/22315381/IJETT-V72I7P141
  17. 17. Shoaib M, Shah B, EI-Sappagh S, Ali A, Ullah A, Alenezi F, et al. An advanced deep learning models-based plant disease detection: A review of recent research. Front Plant Sci. 2023;14:1158933. https://doi.org/10.3389/fpls.2023.1282443
  18. 18. Storey G, Meng Q, Li B. Leaf disease segmentation and detection in apple orchards for precise smart spraying in sustainable agriculture. Sustain. 2022;14(3):1458. https://doi.org/10.3390/su14031458
  19. 19. Karar ME, Alsunaydi F, Albusaymi S, Alotaibi S. A new mobile application of agricultural pests recognition using deep learning in cloud computing system. Alex Eng J. 2021;60(5):4423-32. https://doi.org/10.1016/j.aej.2021.03.009
  20. 20. Tannous M, Stefanini C, Romano D. A deep-learning-based detection approach for the identification of insect species of economic importance. Insects. 2023;14(2):148. https://doi.org/10.3390/insects14020148
  21. 21. Adikari KE, Shrestha S, Ratnayake DT, Budhathoki A, Mohanasundaram S, Dailey MN. Evaluation of artificial intelligence models for flood and drought forecasting in arid and tropical regions. Environ Model Softw. 2021;144:105136. https://doi.org/10.1016/j.envsoft.2021.105136
  22. 22. Awais M, Naqvi SMZA, Zhang H, Li L, Zhang W, Awwad FA, et al. AI and machine learning for soil analysis: an assessment of sustainable agricultural practices. Bioresour Bioprocess. 2023;10(1):90. https://doi.org/10.1186/s40643-023-00710-y
  23. 23. Bilal M, Rubab F, Hussain M, Shah SAR. Agriculture revolutionized by artificial intelligence: Harvesting the future. In: The 2nd International Online Conference on Agriculture. MDPI; 2023. p. 11. https://doi.org/10.1186/s40643-023-00710-y
  24. 24. Ghatrehsamani S, Jha G, Dutta W, Molaei F, Nazrul F, Fortin M, et al. Artificial intelligence tools and techniques to combat herbicide resistant weeds—A review. Sustain. 2023;15(3):1843. https://doi.org/10.3390/su15031843
  25. 25. Etienne A, Ahmad A, Aggarwal V, Saraswat D. Deep learning-based object detection system for identifying weeds Using UAS imagery. Remote Sens. 2021;13(24):5182. https://doi.org/10.3390/rs13245182
  26. 26. Maraveas C. Incorporating artificial intelligence technology in smart greenhouses: Current state of the art. Appl Sci. 2022;13(1):14. https://doi.org/10.3390/app13010014
  27. 27. Codeluppi G, Cilfone A, Davoli L, Ferrari G. AI at the edge: a smart gateway for greenhouse air temperature forecasting. In: 2020 IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor). Trento, Italy: IEEE; 2020. p. 348-53. https://doi.org/10.1109/MetroAgriFor50201.2020.9277553
  28. 28. Fernando S, Nethmi R, Silva A, Perera A, Silva RD, Abeygunawardhana PWK. AI based greenhouse farming support system with robotic monitoring. In: 2020 IEEE Region 10 Conference (TENCON). Osaka, Japan: IEEE; 2020. p. 1368-73. https://doi.org/10.1109/TENCON50793.2020.9293745
  29. 29. Tace Y, Tabaa M, Elfilali S, Leghris C, Bensag H, Renault E. Smart irrigation system based on IoT and machine learning. Energy Rep. 2022;8:1025-36. https://doi.org/10.1016/j.egyr.2022.07.088
  30. 30. Wei H, Xu W, Kang B, Eisner R, Muleke A, Rodriguez D, et al. Irrigation with artificial intelligence: Problems, premises, promises. Hum-Centric Intell Syst. 2024;4(2):187-205. https://doi.org/10.1007/s44230-024-00072-4
  31. 31. Raouhi EM, Zouizza M, Lachgar M, Zouani Y, Hrimech H, Kartit A. AIDSII: An AI-based digital system for intelligent irrigation. Softw Impacts. 2023;17:100574. https://doi.org/10.1016/j.simpa.2023.100574
  32. 32. Padhiary M, Saha D, Kumar R, Sethi LN, Kumar A. Enhancing precision agriculture: A comprehensive review of machine learning and AI vision applications in all-terrain vehicle for farm automation. Smart Agric Technol. 2024;8:100483. https://doi.org/10.1016/j.atech.2024.100483
  33. 33. Alazzai WK, Abood BShZ, Al-Jawahry HM, Obaid MK. Precision farming: The power of AI and IoT technologies. In: Li G, Subramaniam U, Sekar M, editors. International Conference on Environmental Development Using Computer Science (ICECS’24). Vol. 491. E3S Web Conference; 2024. p. 04006. https://doi.org/10.1051/e3sconf/202449104006
  34. 34. Bao J, Xie Q. Artificial intelligence in animal farming: A systematic literature review. J Clean Prod. 2022;331:129956. https://doi.org/10.1016/j.jclepro.2021.129956
  35. 35. Kandarpa Boruah, Prabodh Kumar Hembram, Debapritam Deb, Shehnaaz Rahman, Nilotpal Ghosh. Internet of Things (IoT) and Artificial Intelligence (AI) in livestock farming. BioNE. 2025;28(32).
  36. 36. Neethirajan S. Artificial intelligence and sensor innovations: Enhancing livestock welfare with a human-centric approach. Hum-Centric Intell Syst. 2023;4(1):77-92. https://doi.org/10.1007/s44230-023-00050-2
  37. 37. Melak A, Aseged T, Shitaw T. The influence of artificial intelligence technology on the management of livestock farms. Int J Distrib Sens Netw. 2024;2024:1-12. https://doi.org/10.1155/2024/8929748
  38. 38. Patel H, Samad A, Hamza M, Muazzam A, Harahap MK. Role of artificial intelligence in livestock and poultry farming. Sinkron. 2022;7(4):2425-9. https://doi.org/10.33395/sinkron.v7i4.11837
  39. 39. Cho Y, Kim J. AI-based intelligent monitoring system for estrus prediction in the livestock industry. Appl Sci. 2023;13(4):2442. https://doi.org/10.3390/app13042442
  40. 40. Nagahara M, Tatemoto S, Ito T, Fujimoto O, Ono T, Taniguchi M, et al. Designing a diagnostic method to predict the optimal artificial insemination timing in cows using artificial intelligence. Front Anim Sci. 2024;5:1399434. https://doi.org/10.3389/fanim.2024.1399434
  41. 41. Haleem A, Javaid M, Asim Qadri M, Pratap Singh R, Suman R. Artificial intelligence (AI) applications for marketing: A literature-based study. Int J Intell Netw. 2022;3:119-32. https://doi.org/10.1016/j.ijin.2022.08.005
  42. 42. Ljepava N. AI-enabled marketing solutions in marketing decision making: AI application in different stages of marketing process. TEM J. 2022;1308-15. https://doi.org/10.18421/TEM113-40
  43. 43. Elufioye OA, Ike CU, Odeyemi O, Usman FO, Mhlongo NZ. Ai-Driven predictive analytics in agricultural supply chains: a review: assessing the benefits and challenges of ai in forecasting demand and optimizing supply in agriculture. Comput Sci IT Res J. 2024;5(2):473-97. https://doi.org/10.51594/csitrj.v5i2.817
  44. 44. Hongbing W, Jing G, Bohan K, Peng L, Yuxian S. Analysis and research on the marketing strategy of agricultural products based on artificial intelligence. Math Probl Eng. 2022;2022:1-7. https://doi.org/10.1155/2022/7798640
  45. 45. Sakr GE, Elhajj IH, Mitri G, Wejinya UC. Artificial intelligence for forest fire prediction. In: 2010 IEEE/ASME International Conference on Advanced Intelligent Mechatronics. Montreal, QC, Canada: IEEE; 2010. p. 1311-6. https://doi.org/10.1109/AIM.2010.5695809
  46. 46. Shivaprakash KN, Swami N, Mysorekar S, Arora R, Gangadharan A, Vohra K, et al. Potential for Artificial Intelligence (AI) and Machine Learning (ML) applications in biodiversity conservation, managing forests and related services in India. Sustain. 2022;14(12):7154. https://doi.org/10.3390/su14127154
  47. 47. Da Silva DQ, Dos Santos FN, Filipe V, Sousa AJ, Oliveira PM. Edge AI-based tree trunk detection for forestry monitoring robotics. Robotics. 2022;11(6):136. https://doi.org/10.3390/robotics11060136
  48. 48. Honarmand Ebrahimi S, Ossewaarde M, Need A. Smart fishery: A systematic review and research agenda for sustainable fisheries in the age of AI. Sustain. 2021;13(11):6037. https://doi.org/10.3390/su13116037
  49. 49. Hari Prasad Mohale1 PJ, Jawahar P, Jayakumar N, Arul Oli G, Ravikumar T. Application of deep learning (AI) in marine fisheries resource management. Trends Agri Sci. 2023;2(9):753-63.
  50. 50. Ahmed MS, Aurpa TT, Azad MdAK. Fish disease detection using image based machine learning technique in aquaculture. J King Saud Univ - Comput Inf Sci. 2022;34(8):5170-82. https://doi.org/10.1016/j.jksuci.2021.05.003
  51. 51. Rejeb A, Rejeb K, Zailani S, Keogh JG, Appolloni A. Examining the interplay between artificial intelligence and the agri-food industry. Artif Intell Agric. 2022;6:111-28. https://doi.org/10.1016/j.aiia.2022.08.002
  52. 52. Kakani V, Nguyen VH, Kumar BP, Kim H, Pasupuleti VR. A critical review on computer vision and artificial intelligence in food industry. J Agric Food Res. 2020;2:100033. https://doi.org/10.1016/j.jafr.2020.100033
  53. 53. Misra NN, Dixit Y, Al-Mallahi A, Bhullar MS, Upadhyay R, Martynenko A. IoT, big data and artificial intelligence in agriculture and food industry. IEEE Internet Things J. 2022;9(9):6305-24. https://doi.org/10.1109/JIOT.2020.2998584
  54. 54. Kutyauripo I, Rushambwa M, Chiwazi L. Artificial intelligence applications in the agrifood sectors. J Agric Food Res. 2023;11:100502. https://doi.org/10.1016/j.jafr.2023.100502
  55. 55. Golshani T. The role of AI in managing risk in agricultural engineering. SSRN Electron J. 2024. https://doi.org/10.2139/ssrn.4842193
  56. 56. Wakchaure M, Patle BK, Mahindrakar AK. Application of AI techniques and robotics in agriculture: A review. Artif Intell Life Sci. 2023;3:100057. https://doi.org/10.1016/j.ailsci.2023.100057
  57. 57. Subeesh A, Mehta CR. Automation and digitization of agriculture using artificial intelligence and internet of things. Artif Intell Agric. 2021;5:278-91. https://doi.org/10.1016/j.aiia.2021.11.004
  58. 58. Nagar H, Machavaram R, Kulkarni P, Soni P. AI-based engine performance prediction cum advisory system to maximise fuel efficiency and field performance of the tractor for optimum tillage. Syst Sci Control Eng. 2024;12(1):2347936. https://doi.org/10.1080/21642583.2024.2347936.
  59. 59. Singh R, Singh R, Gehlot A, Akram SV, Priyadarshi N, Twala B. Horticulture 4.0: Adoption of industry 4.0 technologies in horticulture for meeting sustainable farming. Appl Sci. 2022;12(24):12557. https://doi.org/10.3390/app122412557
  60. 60. Kumar V, Jakhwal R, Chaudhary N, Singh S. Artificial intelligence in horticulture crops. Ann Hortic. 2023;16(1):72-9. https://doi.org/10.5958/0976-4623.2023.00014.2
  61. 61. Gammanpila HW, Sashika MAN, Priyadarshani SVGN. Advancing horticultural crop loss reduction through robotic and AI technologies: Innovations, applications and practical implications. Xiao X, editor. Adv Agric. 2024;2024(1):2472111. https://doi.org/10.1155/2024/2472111
  62. 62. Opara IK, Opara UL, Okolie JA, Fawole OA. Machine learning application in horticulture and prospects for predicting fresh produce losses and waste: A Review. Plants. 2024;13(9):1200. https://doi.org/10.3390/plants13091200
  63. 63. Meghwanshi S. Artificial intelligence in agriculture: A Review. Int Res J Mod Eng Technol Sci. 2024;6:4358-63.
  64. 64. Hussein AHA, Jabbar KA, Mohammed A, Jasim L. Harvesting the future: AI and IoT in agriculture. In: Slimani K, Gerasymov O, Kerkeb ML, editors. International Conference on Smart Technologies and Applied Research (STAR'2023). Vol. 477. E3S Web Conference; 2024. p. 00090. https://doi.org/10.1051/e3sconf/202447700090
  65. 65. Verma A, Verma S. Role of artificial intelligence in agriculture. Agric Magazine. 2023;2:281-6. https://doi.org/10.58532/V2BS16CH8
  66. 66. Mathur R. Artificial intelligence in sustainable agriculture. Int J Res Appl Sci Eng Technol. 2023;11(6):4047-52. https://doi.org/10.22214/ijraset.2023.54360
  67. 67. Olabimpe Banke Akintuyi. Adaptive AI in precision agriculture: A review: Investigating the use of self-learning algorithms in optimizing farm operations based on real-time data. Open Access Res J Multidiscip Stud. 2024;7(2):016-30. https://doi.org/10.53022/oarjms.2024.7.2.0023

Downloads

Download data is not yet available.