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Plant Science Today (PST)

Research Articles

Vol. 12 No. 2 (2025)

A systematic literature review on artificial intelligence in transforming precision agriculture for sustainable farming: Current status and future directions

DOI
https://doi.org/10.14719/pst.6175
Submitted
19 November 2024
Published
29-01-2025 — Updated on 01-04-2025
Versions

Abstract

Agriculture encounters significant challenges, with the demand to increase food production by 50% by 2050 to sustain a growing global population while tackling the impacts of climate change and resource scarcity. Artificial intelligence (AI) has transformative potential for precision agriculture, optimizing crop management, resource allocation and sustainable farming practices. A systematic literature review (SLR) was conducted using the Scopus database, initially identifying 8145 articles. Based on eligibility criteria, 76 were selected for in-depth analysis. This paper focuses on AI applications in key areas of agriculture, including crop monitoring, irrigation management, weed and pest control, yield prediction, and smart spraying technologies. AI-driven techniques, such as machine learning, computer vision, robotics and the Internet of Things (IoT), enhance agricultural productivity and sustainability through data-driven decision-making and real-time monitoring. AI-based irrigation systems optimize water use efficiency by integrating sensor inputs with weather data, while robotic technologies enhance targeted weed and pest management. Resource efficiency is further enhanced by smart sprayers and yield estimation techniques. Despite these advancements, research gaps remain, particularly in integrating AI with emerging fields such as nutrient management and expanding the use of sensor systems. This paper highlights advancements in AI for precision agriculture, including crop monitoring, irrigation management and yield prediction, while identifying gaps in areas like nutrient management and sensor integration. Addressing these gaps is essential for developing more sustainable and resilient agricultural systems.

References

  1. FAO. The future of food and agriculture – Alternative pathways to 2050 [Internet]. Rome; 2018 [cited 2024 Oct 29]. https://www.fao.org/3/I8429EN/i8429en.pdf
  2. Jaramillo-Hernández JF, Julian V, Marco-Detchart C, Rincón JA. Application of machine vision techniques in low-cost devices to improve efficiency in precision farming. sensors. 2024;24(3). https://doi.org/10.3390/s24030937
  3. Alexander CS, Yarborough M, Smith A. Who is responsible for ‘responsible AI’?: Navigating challenges to build trust in AI agriculture and food system technology. Precis Agric. 2024;25(1):146–185. https://doi.org/10.1007/s11119-023-10063-3
  4. Government of India. Press Information Bureau. 2022. Use of drones in agriculture. https://pib.gov.in/PressReleaseIframePage.aspx?PRID=1842784
  5. Food and Agriculture Organization of the United Nations. E-agriculture in action: Drones for agriculture. [Internet]. Rome; 2017. https://www.fao.org/policy-support/tools-and-publications/resources-details/en/c/1234537/
  6. Saiz-Rubio V, Rovira-Más F, Cuenca-Cuenca A, Alves F. Robotics-based vineyard water potential monitoring at high resolution. Comput Electron Agric. 2021;187. https://doi.org/10.1016/j.compag.2021.106311
  7. Raja R, Slaughter DC, Fennimore SA, Siemens MC. Real-time control of high-resolution micro-jet sprayer integrated with machine vision for precision weed control. Biosyst Eng. 2023;228:31–48. https://doi.org/10.1016/j.biosystemseng.2023.02.006
  8. Zeng YF, Chen CT, Lin GF. Practical application of an intelligent irrigation system to rice paddies in Taiwan. Agric Water Manag. 2023;280. https://doi.org/10.1016/j.agwat.2023.108216
  9. Bancheri M, Basile A, Terribile F, Langella G, Botta M, Lezzi D, et al. A web-based operational tool for the identification of best practices in European agricultural systems. Land Degrad Dev. 2024;35(13):3965–80. https://doi.org/10.1002/ldr.5114
  10. Sharma A, Sharma A, Tselykh A, Bozhenyuk A, Choudhury T, Alomar MA, et al. Artificial intelligence and internet of things oriented sustainable precision farming: Towards modern agriculture. Open Life Sci. 2023;18(1). https://doi.org/10.1515/biol-2022-0713
  11. Mengist W, Soromessa T, Legese G. Ecosystem services research in mountainous regions: A systematic literature review on current knowledge and research gaps. Sci Total Environ. 2020;702:134581. https://doi.org/10.1016/j.scitotenv.2019.134581
  12. Mohammed SP, Natarajan S, Eybishitz A, Jakkula D, Veerasamy R, Prasanthrajan M, et al. Heat stress in tomato plants: current challenges and future directions for sustainable agriculture. N Z J Crop Hortic Sci. 2024:1–25. https://doi.org/10.1080/01140671.2024.2432624
  13. Addas A, Tahir M, Ismat N. Enhancing precision of crop farming towards smart cities: An application of artificial intelligence. Sustainability (Switzerland). 2024;16(1). https://doi.org/10.3390/su16010355
  14. Vasileiou M, Kyrgiakos LS, Kleisiari C, Kleftodimos G, Vlontzos G, Belhouchette H, et al. Transforming weed management in sustainable agriculture with artificial intelligence: A systematic literature review towards weed identification and deep learning. Crop Prot. 2024;176. https://doi.org/10.1016/j.cropro.2023.106522
  15. Gerhards R, Risser P, Spaeth M, Saile M, Peteinatos G. A comparison of seven innovative robotic weeding systems and reference herbicide strategies in sugar beet (Beta vulgaris subsp. vulgaris L.) and rapeseed (Brassica napus L.). Weed Res. 2024;64(1):42–53. https://doi.org/10.1111/wre.12603
  16. Kiti? G, Krklješ D, Pani? M, Petes C, Birgermajer S, Crnojevi? V. Agrobot Lala—An autonomous robotic system for real-time, in-field soil sampling and analysis of nitrates. Sensors. 2022;22(11). https://doi.org/10.3390/s22114207
  17. de Lima J de JA, Maldaner LF, Molin JP. Sensor fusion with NARX neural network to predict the mass flow in a sugarcane harvester. Sensors. 2021;21(13). https://doi.org/10.3390/s21134530
  18. Salamai AA, Al-Nami WT. Sustainable coffee leaf diagnosis: A deep knowledgeable meta-Learning approach. Sustainability (Switzerland). 2023;15(24). https://doi.org/10.3390/su152416791
  19. Falcioni R, Gonçalves JVF, Oliveira KM de, Oliveira CA de, Demattê JAM, Antunes WC, et al. Enhancing pigment phenotyping and classification in lettuce through the integration of reflectance spectroscopy and AI algorithms. Plants. 2023;12(6). https://doi.org/10.3390/plants12061333
  20. Lee J, Nazki H, Baek J, Hong Y, Lee M. Artificial intelligence approach for tomato detection and mass estimation in precision agriculture. Sustainability (Switzerland). 2020;12(21):9138. https://doi.org/10.3390/su12219138
  21. Caldeira RF, Santiago WE, Teruel B. Identification of cotton leaf lesions using deep learning techniques. Sensors. 2021;21(9). https://doi.org/10.3390/s21093169
  22. López-Martínez M de J, Díaz-Flórez G, Villagrana-Barraza S, Castañeda-Miranda CL, Solís-Sánchez LO, Ortíz-Esquivel DI, et al. Pattern classification of an onion crop (Allium Cepa) field using convolutional neural network models. Agronomy. 2024;14(6). https://doi.org/10.3390/agronomy14061206
  23. Neri I, Caponi S, Bonacci F, Clementi G, Cottone F, Gammaitoni L, et al. Real-Time AI-assisted push-broom hyperspectral system for precision agriculture. Sensors. 2024;24(2). https://doi.org/10.3390/s24020344
  24. Singh RK, Rahmani MH, Weyn M, Berkvens R. Joint communication and sensing: A proof of concept and datasets for greenhouse monitoring using LoRaWAN. Sensors. 2022;22(4). https://doi.org/10.3390/s22041326
  25. Osman Y, Dennis R, Elgazzar K. Yield estimation and visualization solution for precision agriculture. Sensors. 2021;21(19). https://doi.org/10.3390/s21196657
  26. Chang L, Li D, Hameed MK, Yin Y, Huang D, Niu Q. Using a hybrid neural network model DCNN-LSTMD for image-based nitrogen nutrition diagnosis in muskmelon. Horticulturae. 2021;7(11). https://doi.org/10.3390/horticulturae7110489
  27. Dong X, Wang Q, Huang Q, Ge Q, Zhao K, Wu X, et al. PDDD-PreTrain: A series of commonly used pre-trained models support image-based plant disease diagnosis. Plant Phenomics. 2023;5. https://doi.org/10.34133/plantphenomics.0054.
  28. Partel V, Costa L, Ampatzidis Y. Smart tree crop sprayer utilizing sensor fusion and artificial intelligence. Comput Electron Agric. 2021;191. https://doi.org/10.1016/j.compag.2021.106556
  29. Moon T, Kim D, Kwon S, Ahn TI, Son JE. Non-destructive monitoring of crop fresh weight and leaf area with a simple formula and a convolutional neural network. Sensors (Basel). 2022;22(20). https://doi.org/10.3390/s22207728
  30. Siedliska A, Baranowski P, Pastuszka-Wo?niak J, Zubik M, Krzyszczak J. Identification of plant leaf phosphorus content at different growth stages based on hyperspectral reflectance. BMC Plant Biol. 2021;21(1). https://doi.org/10.1186/s12870-020-02807-4
  31. Costa L, Kunwar S, Ampatzidis Y, Albrecht U. Determining leaf nutrient concentrations in citrus trees using UAV imagery and machine learning. Precis Agric. 2022;23(3):854–75. https://doi.org/10.1007/s11119-021-09864-1
  32. Dolaptsis K, Pantazi XE, Paraskevas C, Arslan S, Tekin Y, Bantchina BB, et al. A hybrid LSTM approach for irrigation scheduling in maize crop. Agriculture (Switzerland). 2024;14(2). https://doi.org/10.3390/agriculture14020210
  33. Munnaf MA, Mouazen AM. An automated system of soil sensor-based site-specific seeding for silage maize: A proof of concept. Comput Electron Agric. 2023;209:107872. https://doi.org/10.1016/j.compag.2023.107872
  34. Wiegman CR, Venkatesh R, Shearer SA. Intra-canopy sensing using multi-rotors UAS: A new approach for crop stress detection and diagnosis. J ASABE. 2022;65(4):913–25. https://doi.org/10.13031/ja.14342
  35. Hasan ASMM, Diepeveen D, Laga H, Jones MGK, Sohel F. Image patch-based deep learning approach for crop and weed recognition. Ecol Inform. 2023;78. https://doi.org/10.1016/j.ecoinf.2023.102361
  36. Eron F, Noman M, Ricon de Oliveira R, Chalfun-Junior A. Computer vision-aided intelligent monitoring of coffee: Towards sustainable coffee Production. Sci Hortic. 2024;327:112847. https://doi.org/10.1016/j.scienta.2024.112847
  37. de Lima Silva YK, Furlani CEA, Canata TF. AI-based prediction of carrot yield and quality on tropical agriculture. AgriEngineering. 2024;6(1):361–74. https://doi.org/10.3390/agriengineering6010022
  38. Hachimi C El, Belaqziz S, Khabba S, Sebbar B, Dhiba D, Chehbouni A. Smart weather data management based on artificial intelligence and big data analytics for precision agriculture. Agriculture (Switzerland). 2023;13(1). https://doi.org/10.3390/agriculture13010095
  39. Abdulridha J, Ampatzidis Y, Qureshi J, Roberts P. Identification and classification of Downy Mildew severity stages in watermelon utilizing aerial and ground remote sensing and machine learning. Front Plant Sci. 2022;13. https://doi.org/10.3389/fpls.2022.791018
  40. Linaza MT, Posada J, Bund J, Eisert P, Quartulli M, Döllner J, et al. Data-driven artificial intelligence applications for sustainable precision agriculture. Agronomy. 2021;11(6). https://doi.org/10.3390/agronomy11061227
  41. Zu D, Zhang F, Wu Q, Lu C, Wang W, Chen X. Sundry bacteria contamination identification of Lentinula edodes logs based on deep learning model. Agronomy. 2022;12(9). https://doi.org/10.3390/agronomy12092121
  42. Md-Tahir H, Mahmood HS, Husain M, Khalil A, Shoaib M, Ali M, et al. Localized crop classification by NDVI time series analysis of remote sensing satellite data; applications for mechanization strategy and integrated resource management. Agri Engineering. 2024;6(3):2429–44. https://doi.org/10.3390/agriengineering6030142
  43. Fathy C, Ali HM. A secure IoT-based irrigation system for precision agriculture using the expeditious cipher. Sensors. 2023;23(4). https://doi.org/10.3390/s23042091
  44. Sami M, Khan SQ, Khurram M, Farooq MU, Anjum R, Aziz S, et al. A deep learning-based sensor modeling for smart irrigation system. Agronomy. 2022;12(1). https://doi.org/10.3390/agronomy12010212
  45. Rozenstein O, Cohen Y, Alchanatis V, Behrendt K, Bonfil DJ, Eshel G, et al. Data-driven agriculture and sustainable farming: friends or foes?. Precis Agric. 2024;25(1):520–31. https://doi.org/10.1007/s11119-023-10061-5
  46. Li R, He Y, Li Y, Qin W, Abbas A, Ji R, et al. Identification of cotton pest and disease based on CFNet- VoV-GCSP -LSKNet-YOLOv8s: a new era of precision agriculture. Front Plant Sci. 2024;15. https://doi.org/10.3389/fpls.2024.1348402
  47. Gardezi M, Joshi B, Rizzo DM, Ryan M, Prutzer E, Brugler S, et al. Artificial intelligence in farming: Challenges and opportunities for building trust. Agron J. 2024;116(3):1217–28. https://doi.org/10.1002/agj2.21353
  48. Ferraz MAJ, Barboza TOC, De Sousa AP, Von Pinho RG, Santos AFD. Integrating satellite and UAV technologies for maize plant height estimation using advanced machine learning. Agri Engineering. 2024;6(1):20–33. https://doi.org/10.3390/agriengineering6010002
  49. Vijayakumar V, Ampatzidis Y, Costa L. Tree-level citrus yield prediction utilizing ground and aerial machine vision and machine learning. Smart Agric Technol. 2023;3. https://doi.org/10.1016/j.atech.2022.100077
  50. Kim HT, AlZubi AA. AI-enhanced precision irrigation in legume farming: Optimizing water use efficiency. Legume Res. 2024;47(8):1382–89. https://doi.org/10.18805/lrf-791
  51. Xie S, Bai Y, An Q, Song J, Tang X, Xie F. Identification system of tomato leaf diseases based on optimized MobileNetV2 INMATEH. Agric Eng. 2022;68(3):589–98. http://dx.doi.org/10.35633/inmateh-68-58.
  52. Íñiguez R, Gutiérrez S, Poblete-Echeverría C, Hernández I, Barrio I, Tardáguila J. Deep learning modelling for non-invasive grape bunch detection under diverse occlusion conditions. Comput Electron Agric. 2024;226. https://doi.org/10.1016/j.compag.2024.109421
  53. Liu LW, Lu CT, Wang YM, Lin KH, Ma X, Lin WS. Rice (Oryza sativa L.) growth modeling based on growth degree day (GDD) and artificial intelligence algorithms. Agriculture (Switzerland). 2022;12(1). https://doi.org/10.3390/agriculture12010059
  54. Beloev I, Kinaneva D, Georgiev G, Hristov G, Zahariev P. Artificial intelligence-driven autonomous robot for precision agriculture. Acta Technol Agric. 2021;24(1):48–54. http://dx.doi.org/10.2478/ata-2021-0008
  55. Musanase C, Vodacek A, Hanyurwimfura D, Uwitonze A, Kabandana I. Data-driven analysis and machine learning-based crop and fertilizer recommendation system for revolutionizing farming practices. Agriculture (Switzerland). 2023;13(11). https://doi.org/10.3390/agriculture13112141
  56. Neményi M, Ambrus B, Teschner G, Alahmad T, Nyéki A, Kovács AJ. Challenges of ecocentric sustainable development in agriculture with special regard to the internet of things (IoT), an ICT perspective. Prog Agric Eng Sci. 2023;19(1):113–22. https://doi.org/10.1556/446.2023.00099
  57. Khalid H, Hashim SJ, Ahmad SMS, Hashim F, Chaudhary MA. Robust multi-gateway authentication scheme for agriculture wireless sensor network in society 5.0 smart communities. Agriculture (Switzerland). 2021;11(10). https://doi.org/10.3390/agriculture11101020
  58. Foster L, Szilagyi K, Wairegi A, Oguamanam C, de Beer J. Smart farming and artificial intelligence in East Africa: Addressing indigeneity, plants and gender. Smart Agric Technol. 2023;3. https://doi.org/10.1016/j.atech.2022.100132
  59. Khan H, Haq IU, Munsif M, Mustaqeem, Khan SU, Lee MY. Automated wheat diseases classification framework using advanced machine learning technique. Agriculture (Switzerland). 2022;12(8). https://doi.org/10.3390/agriculture12081226
  60. Emmi L, Fernández R, Gonzalez-de-Santos P, Francia M, Golfarelli M, Vitali G, et al. Exploiting the internet resources for autonomous robots in agriculture. Agriculture (Switzerland). 2023;13(5). https://doi.org/10.3390/agriculture13051005
  61. Rai N, Zhang Y, Ram BG, Schumacher L, Yellavajjala RK, Bajwa S, et al. Applications of deep learning in precision weed management: A review. Comput Electron Agric. 2023;206. https://doi.org/10.1016/j.compag.2023.107698
  62. Sassu A, Motta J, Deidda A, Ghiani L, Carlevaro A, Garibotto G, et al. Artichoke deep learning detection network for site-specific agrochemicals UAS spraying. Comput Electron Agric. 2023;213. https://doi.org/10.1016/j.compag.2023.108185
  63. Deng B, Lu Y, Xu J. Weed database development: An updated survey of public weed datasets and cross-season weed detection adaptation. Ecol Inform. 2024;81:102546. https://doi.org/10.1016/j.ecoinf.2024.102546
  64. Storm H, Seidel SJ, Klingbeil L, Ewert F, Vereecken H, Amelung W, et al. Research priorities to leverage smart digital technologies for sustainable crop production. Eur J Agron. 2024;156. https://doi.org/10.1016/j.eja.2024.127178
  65. Subbarayudu C, Kubendiran M. A comprehensive survey on machine learning and deep learning techniques for crop disease prediction in smart agriculture. Nat Environ Pollut Technol. 2024;23(2):619–32. http://dx.doi.org/10.46488/NEPT.2024.v23i02.003
  66. Bütüner AK, ?ahin YS, Erdinç A, Erdo?an H, Lewis E. Enhancing pest detection: Assessing Tuta absoluta (Lepidoptera: Gelechiidae) damage intensity in field images through advanced machine learning. Tarim Bilimleri Derg. 2024;30(1):99–107. https://doi.org/10.15832/ankutbd.1308406
  67. Preite L, Vignali G. Artificial intelligence to optimize water consumption in agriculture: A predictive algorithm-based irrigation management system. Comput Electron Agric. 2024;223. https://doi.org/10.1016/j.compag.2024.109126
  68. Visentin F, Cremasco S, Sozzi M, Signorini L, Signorini M, Marinello F, et al. A mixed-autonomous robotic platform for intra-row and inter-row weed removal for precision agriculture. Comput Electron Agric. 2023;214. https://doi.org/10.1016/j.compag.2023.108270
  69. Russo C, Cirillo V, Esposito M, Lentini M, Pollaro N, Maggio A. Convolutional neural network for the early identification of weeds: A technological support to biodiversity and yield losses mitigation. Smart Agric Technol. 2024;9. https://doi.org/10.1016/j.atech.2024.100594
  70. Yousefi DBM, Mohd Rafie AS, Abd Aziz S, Azrad S, Mazmira Mohd MM, Shahi A, et al. Classification of oil palm female inflorescences anthesis stages using machine learning approaches. Inf Process Agric. 2021;8(4):537–49. https://doi.org/10.1016/j.inpa.2020.11.007
  71. Bono A, Marani R, Guaragnella C, D’Orazio T. Biomass characterization with semantic segmentation models and point cloud analysis for precision viticulture. Comput Electron Agric. 2024;218. https://doi.org/10.1016/j.compag.2024.108712
  72. Giménez-Gallego J, González-Teruel JD, Soto-Valles F, Jiménez-Buendía M, Navarro-Hellín H, Torres-Sánchez R. Intelligent thermal image-based sensor for affordable measurement of crop canopy temperature. Comput Electron Agric. 2021;188. https://doi.org/10.1016/j.compag.2021.106319
  73. Ferrarezi RS, Peng TW. Smart system for automated irrigation using internet of things devices. Hort Technology. 2021;31(6):642–49. https://doi.org/10.21273/horttech04860-21
  74. Ampatzidis Y, Partel V, Costa L. Agroview: Cloud-based application to process, analyze and visualize UAV-collected data for precision agriculture applications utilizing artificial intelligence. Comput Electron Agric. 2020;174. https://doi.org/10.1016/j.compag.2020.105457
  75. Shao Y, Yang W, Wang J, Lu Z, Zhang M, Chen D. Cotton disease recognition method in natural environment based on convolutional neural network. Agriculture (Switzerland). 2024 Sep 1;14(9). https://doi.org/10.3390/agriculture14091577
  76. Partel V, Charan KS, Ampatzidis Y. Development and evaluation of a low-cost and smart technology for precision weed management utilizing artificial intelligence. Comput Electron Agric. 2019;157: 339–50. https://doi.org/10.1016/j.compag.2018.12.048
  77. Kanwal T, Rehman SU, Ali T, Mahmood K, Villar SG, Lopez LAD, et al. An intelligent dual-axis solar tracking system for remote weather monitoring in the agricultural field. Agriculture (Switzerland). 2023;13(8). https://doi.org/10.3390/agriculture13081600
  78. Ogawa S, Gomez SM, Ishitani M. Crop development with data-driven approach towards sustainable agriculture: Lifting the achievements and opportunities of collaborative research between CIAT and Japan. Jpn Agric Res Q (JARQ). 2021;55(Special):463–72. https://doi.org/10.6090/jarq.55.463
  79. Mana AA, Allouhi A, Hamrani A, Rahman S, el Jamaoui I, Jayachandran K. Sustainable AI-based production agriculture: Exploring AI applications and implications in agricultural practices. Smart Agric Technol. 2024;7. https://doi.org/10.1016/j.atech.2024.100416
  80. Ampatzidis Y, Partel V, Meyering B, Albrecht U. Citrus rootstock evaluation utilizing UAV-based remote sensing and artificial intelligence. Comput Electron Agric. 2019;164. https://doi.org/10.1016/j.compag.2019.104900
  81. Chamara N, Bai G, Ge Y. AICropCAM: Deploying classification, segmentation, detection and counting deep-learning models for crop monitoring on the edge. Comput Electron Agric. 2023;215:108420. https://doi.org/10.1016/j.compag.2023.108420

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