Artificial Intelligence in Agriculture: Benefits, Challenges, and Trends
Source abstract
Abstract
The world’s population has reached 8 billion and is projected to reach 9.7 billion by 2050, increasing the demand for food production. Artificial intelligence (AI) technologies that optimize resources and increase productivity are vital in an environment that has tensions in the supply chain and increasingly frequent weather events. This study performed a systemic review of the literature using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology on artificial intelligence technologies applied to agriculture. It retrieved 906 relevant studies from five electronic databases and selected 176 studies for bibliometric analysis. The quality appraisal step selected 17 studies for the analysis of the benefits, challenges, and trends of AI technologies used in agriculture. This work showed an evolution in the area with increased publications over the last five years, with more than 20 different AI techniques applied in the 176 studies analyzed, with machine learning, convolutional neural networks, IoT, big data, robotics, and computer vision being the most used technologies. Considering a worldwide scope, the countries highlighted were India, China, and the USA. Agricultural sectors included crop management and prediction and disease and pest management. Finally, it presented challenges and trends that are promising when considering the future directions in AI for agriculture.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Smart Agriculture and AI · Date Palm Research Studies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Computer Science Graduate Program, Federal University of the Semi-Arid Region—UFERSA, Street Francisco Mota, n° 572, Mossoró 59625-900, Brazil
Integrated Center for Technological Innovation in the Semi-Arid Region—CITED (East Campus), Electrical Engineering Graduate Program, Federal University of the Semi-Arid Region—UFERSA, Street Francisco Mota, n° 572, Mossoró 59625-900, Brazil
References (55)
Show source-provided references
1. Aminetzah, D., Baroyan, A., Denis, N., Dewilde, S., Ferreira, N., Kravchenko, O., Revellat, J., and Verlan, I. (2022). A reflection on global food security challenges amid the war in Ukraine and the early impact of climate change. McKinsey’s Agric. Pract., Available online: https://www.mckinsey.com/industries/agriculture/our-insights/a-reflection-on-global-food-security-challenges-amid-the-war-in-ukraine-and-the-early-impact-of-climate-change#/.
2. FAO (2022). The State of Food Security and Nutrition in the World 2022, FAO.
3. Alexandratos, N., and Bruinsma, J. (2012). World Agriculture Towards 2030/2050: The 2012 Revision 2012, FAO.
4. Understanding the potential applications of artificial intelligence in agriculture sector
5. Crop yield prediction using machine learning: A systematic literature review10.1016/j.compag.2020.105709 ↗
6. Advanced agricultural disease image recognition technologies: A review
7. Farooq, M.S., Riaz, S., Abid, A., Umer, T., and Zikria, Y.B. (2020). Role of IoT technology in agriculture: A systematic literature review. Electronics, 9.10.3390/electronics9020319 ↗
8. Big data in smart farming—A review10.1016/j.agsy.2017.01.023 ↗
9. Agriculture 4.0: A systematic literature review on the paradigm, technologies and benefits10.1016/j.futures.2022.102998 ↗
10. Araújo, S.O., Peres, R.S., Barata, J., Lidon, F., and Ramalho, J.C. (2021). Characterising the Agriculture 4.0 landscape—Emerging trends, challenges and opportunities. Agronomy, 11.10.3390/agronomy11040667 ↗
11. PRISMA 2020 explanation and elaboration: Updated guidance and exemplars for reporting systematic reviews10.1136/bmj.n160 ↗
12. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews10.1136/bmj.n71 ↗
13. Fruit detection for strawberry harvesting robot in non-structural environment based on Mask-RCNN10.1016/j.compag.2019.06.001 ↗
14. Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides
15. IoT based low cost and intelligent module for smart irrigation system10.1016/j.compag.2019.05.027 ↗
16. Transfer learning for multi-crop leaf disease image classification using convolutional neural network VGG
17. A deep learning and social IoT approach for plants disease prediction toward a sustainable agriculture10.1109/JIOT.2021.3097379 ↗
18. Citrus pests classification using an ensemble of deep learning models10.1016/j.compag.2021.106192 ↗
19. Robotic kiwifruit harvesting using machine vision, convolutional neural networks, and robotic arms10.1016/j.biosystemseng.2019.03.007 ↗
20. Fertigation Management for sustainable precision agriculture based on internet of things10.1016/j.jclepro.2020.124119 ↗
21. Empirical and learning machine approaches to estimating reference evapotranspiration based on temperature data10.1016/j.compag.2019.104937 ↗
22. Introducing digital twins to agriculture10.1016/j.compag.2020.105942 ↗
23. Emerging trends in machine learning to predict crop yield and study its influential factors: A survey10.1007/s11831-021-09569-8 ↗
24. AgriFusion: An architecture for IoT and emerging technologies based on a precision agriculture survey10.1109/ACCESS.2021.3116814 ↗
25. Agroview: Cloud-based application to process, analyze and visualize UAV-collected data for precision agriculture applications utilizing artificial intelligence10.1016/j.compag.2020.105457 ↗
26. Albarrak, K., Gulzar, Y., Hamid, Y., Mehmood, A., and Soomro, A.B. (2022). A deep learning-based model for date fruit classification. Sustainability, 14.10.3390/su14106339 ↗
27. Impacts of agricultural practices on subsurface microbial ecology10.1016/S0065-2113(08)60897-4 ↗
28. Forecasting yield by integrating agrarian factors and machine learning models: A survey10.1016/j.compag.2018.10.024 ↗
29. Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs10.3390/rs5020949 ↗
30. Thiele, G., Friedmann, M., Campos, H., Polar, V., and Bentley, J.W. (2022). Root, Tuber and Banana Food System Innovations, Springer International Publishing.10.1007/978-3-030-92022-7 ↗
31. Koech, R., and Langat, P. (2018). Improving irrigation water use efficiency: A review of advances, challenges and opportunities in the australian context. Water, 10.10.3390/w10121771 ↗
32. Evolution of internet of things (IoT) and its significant impact in the field of precision agriculture10.1016/j.compag.2018.12.039 ↗
33. Carter, P.G., and Johannsen, C.J. (2017). Reference Module in Earth Systems and Environmental Sciences, Elsevier.
34. The way of yield increasing and cost reducing in agriculture: Smart irrigation and fertigation10.24925/turjaf.v6i10.1370-1380.1985 ↗
35. Vashisht, S., Kumar, P., and Trivedi, M.C. (2022, January 27–29). Improvised extreme learning machine for crop yield prediction. Proceedings of the 2022 3rd International Conference on Intelligent Engineering and Management (ICIEM), London, UK.10.1109/ICIEM54221.2022.9853054 ↗
36. Chen, K.-H., Lin, C.-C., Chen, C.-H., Lee, J.-C., and Wu, C.-T. (2022, January 14–16). Crop classification on deep learning. Proceedings of the 2022 IET International Conference on Engineering Technologies and Applications (IET-ICETA), Changhua, Taiwan.10.1109/IET-ICETA56553.2022.9971512 ↗
37. Advances in plant disease and pest management10.1017/S0021859610000997 ↗
38. Tivy, J. (2014). Agricultural Ecology, Routledge.10.4324/9781315841168 ↗
39. Autonomous application controls on smart irrigation10.1016/j.compeleceng.2022.107855 ↗
40. Cambra Baseca, C., Sendra, S., Lloret, J., and Tomas, J. (2019). A smart decision system for digital farming. Agronomy, 9.10.3390/agronomy9050216 ↗
41. Selea, T., and Pslaru, M.-F. (2020, January 1–4). AgriSen—A dataset for crop classification. Proceedings of the 2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC), Timisoara, Romania.10.1109/SYNASC51798.2020.00049 ↗
42. Ruiz-Real, J.L., Uribe-Toril, J., Torres Arriaza, J.A., and de Pablo Valenciano, J. (2020). A look at the past, present and future research trends of artificial intelligence in agriculture. Agronomy, 10.10.3390/agronomy10111839 ↗
43. Russell, S.J., and Norvig, P. (2021). Artificial Intelligence: A Modern Approach, Pearson. [4th ed.].
44. Nasirahmadi, A., and Hensel, O. (2022). Toward the next generation of digitalization in agriculture based on digital twin paradigm. Sensors, 22.10.3390/s22020498 ↗
45. Kumar1, L., Ahlawat, P., Rajput, P., Navsare, R.I., and Kumar Singh, P. (2021). Internet of things (IOT) for smart precision farming and agricultural systems productivity: A review. IJEAST, 5, 141–146.10.33564/IJEAST.2021.v05i09.022 ↗
46. Patil, V.C., Al-Gaadi, K.A., Biradar, D., and Madugundu, R. (2012, January 1–3). Internet of things (IOT) and cloud computing for agriculture: An overview. Proceedings of the Agro-Informatics and Precision Agriculture (AIPA 2012), Hyderabad, India. Available online: https://www.researchgate.net/publication/342144510_INTERNET_OF_THINGS_IOT_AND_CLOUD_COMPUTING_FOR_AGRICULTURE_AN_OVERVIEW.
47. Mekala, M.S., and Viswanathan, P. (2017, January 10–12). A survey: Smart agriculture IoT with cloud computing. Proceedings of the 2017 International conference on Microelectronic Devices, Circuits and Systems (ICMDCS), Vellore, India.10.1109/ICMDCS.2017.8211551 ↗
48. Ajah, I.A., and Nweke, H.F. (2019). Big data and business analytics: Trends, platforms, success factors and applications. BDCC, 3.10.3390/bdcc3020032 ↗
49. Hemathilake, D.M.K.S., and Gunathilake, D.M.C.C. (2022). Future Foods, Elsevier.
50. An intelligent WSN-UAV-based IoT framework for precision agriculture application10.1016/j.compeleceng.2022.107912 ↗
51. Agri-Food 4.0: A survey of the supply chains and technologies for the future agriculture10.1016/j.compind.2020.103187 ↗
52. Zambon, I., Cecchini, M., Egidi, G., Saporito, M.G., and Colantoni, A. (2019). Revolution 4.0: Industry vs. agriculture in a future development for SMEs. Processes, 7.10.3390/pr7010036 ↗
53. The digital agricultural revolution: A bibliometric analysis literature review10.1109/ACCESS.2021.3115258 ↗
54. Valle, S.S., and Kienzle, J. (2020). Agriculture 4.0 agricultural robotics and automated equipment for sustainable crop production. Integr. Crop Manag., 24, Available online: https://www.fao.org/3/cb2186en/CB2186EN.pdf.
55. Survey for smart farming technologies: Challenges and issues10.1016/j.compeleceng.2021.107104 ↗