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An Organized Review of Current AI Trends for Smart Farming to Boost Crop Yield and Its Advantages

At the moment, the advancement in technology is being extensively used for development around the world nowadays. One of the advancements in technology is the use of artificial intelligence (AI) for smart farming for the production of crops and animals in agriculture. Special powers can be programmed into artificial intelligence (AI) systems as needed. Working with agricultural systems, artificial intelligence (AI) helps to raise the standard of agriculture in the world nowadays. The use of this new technology in fundamental industries like agriculture is nothing new as we speak. Utilizing the most recent paper trends will help enhance agricultural yields in a variety of places. This is essential since there is a rising need for food sources and less land is accessible for agriculture use in Nigeria. So, utilizing the features from the most recent year, this systematic review tries to gather the most recent trends in AI studies for Smart Farming publications, and using such a system will help enhance the production of the crops. The impacts of artificial intelligence for smart farming to enhance crop yield are also discussed in detail along with its various applications. We have seen how these sensors can be combined to improve the crop yield production.

Smart Farming, Artificial Intelligence (AI), Crop Yield, Internet of Things (IoT), Wireless Sensor

APA Style

Zaharadeen Yusuf Abdullahi, Amira Musa Saad, Salmanu Safiyanu Abdulsalam, Kassim Sulaiman Abubakar, Adamu Bello, et al. (2022). An Organized Review of Current AI Trends for Smart Farming to Boost Crop Yield and Its Advantages. Control Science and Engineering, 6(1), 1-9.

ACS Style

Zaharadeen Yusuf Abdullahi; Amira Musa Saad; Salmanu Safiyanu Abdulsalam; Kassim Sulaiman Abubakar; Adamu Bello, et al. An Organized Review of Current AI Trends for Smart Farming to Boost Crop Yield and Its Advantages. Control Sci. Eng. 2022, 6(1), 1-9. doi: 10.11648/j.cse.20220601.11

AMA Style

Zaharadeen Yusuf Abdullahi, Amira Musa Saad, Salmanu Safiyanu Abdulsalam, Kassim Sulaiman Abubakar, Adamu Bello, et al. An Organized Review of Current AI Trends for Smart Farming to Boost Crop Yield and Its Advantages. Control Sci Eng. 2022;6(1):1-9. doi: 10.11648/j.cse.20220601.11

Copyright © 2022 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. A. Sharma, A. Jain, P. Gupta, and V. Chowdary, “Machine Learning Applications for Precision Agriculture: A Comprehensive Review,” IEEE Access, vol. 9, pp. 4843–4873, 2021, doi: 10.1109/ACCESS.2020.3048415.
2. R. Dolci, “IoT Solutions for Precision Farming and Food Manufacturing: Artificial Intelligence Applications in Digital Food,” in 2017 IEEE 41st Annual Computer Software and Applications Conference (COMPSAC), Jul. 2017, pp. 384–385. doi: 10.1109/COMPSAC.2017.157.
3. M. H. Widianto, T. E. Suherman, and J. Chiedi, “Pathfinding Augmented Reality for Fire Early Warning IoT Escape Purpose,” International Journal of Engineering Trends and Technology, vol. 69, no. 7, pp. 190–197, 2021, doi: 10.14445/22315381/IJETTV69I7P226.
4. M. H. Widianto, A. Ramadhan, A. Trisetyarso, and E. Abdurachman, “Energy saving on IoT using LoRa: a systematic literature review,” International Journal of Reconfigurable and Embedded Systems (IJRES), vol. 11, no. 1, pp. 25–33, Mar. 2022, doi: 10.11591/ijres.v11.i1.pp25-33.
5. R. Yu, X. Zhang, and M. Zhang, “Smart Home Security Analysis System Based on The Internet of Things,” in 2021 IEEE 2nd International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), Mar. 2021, pp. 596–599. doi: 10.1109/ICBAIE52039.2021.9389849.
6. H. Huang, “Architecture of Audio Broadcasting Coverage Monitoring System Based on Internet of Things,” in 2019 IEEE International Conference on Smart Internet of Things (SmartIoT), Aug. 2019, pp. 320–324. doi: 10.1109/SmartIoT.2019.00055.
7. G. Idoje, T. Dagiuklas, and M. Iqbal, “Survey for smart farming technologies: Challenges and issues,” Computers & Electrical Engineering, vol. 92, p. 107104, 2021, doi: 10.1016/j.compeleceng.2021.107104.
8. M. S. D. Abhiram, J. Kuppili, and N. A. Manga, “Smart Farming System using IoT for Efficient Crop Growth,” in 2020 IEEE International Students’ Conference on Electrical, Electronics and Computer Science (SCEECS), Feb. 2020, pp. 1–4. doi: 10.1109/SCEECS48394.2020.147.
9. F. Nolack Fote, S. Mahmoudi, A. Roukh, and S. Ahmed Mahmoudi, “Big Data Storage and Analysis for Smart Farming,” in 2020 5th International Conference on Cloud Computing and Artificial Intelligence: Technologies and Applications (CloudTech), Nov. 2020, pp. 1–8. doi: 10.1109/CloudTech49835.2020.9365869.
10. X. Jiang et al., “Wireless Sensor Network Utilizing Flexible Nitrate Sensors for Smart Farming,” in 2019 IEEE SENSORS, Oct. 2019, pp. 1–4. doi: 10.1109/SENSORS43011.2019.8956915.
11. R. Deepa, V. Moorthy, R. Venkataraman, and S. S. Kundu, “Smart Farming Implementation using Phase based IOT System,” in 2020 International Conference on Communication and Signal Processing (ICCSP), Jul. 2020, pp. 930–934. doi: 10.1109/ICCSP48568.2020.9182078.
12. B. Ban, J. Lee, D. Ryu, M. Lee, and T. D. Eom, “Nutrient Solution Management System for Smart Farms and Plant Factory,” in 2020 International Conference on Information and Communication Technology Convergence (ICTC), Oct. 2020, pp. 1537–1542. doi: 10.1109/ICTC49870.2020.9289192.
13. L. Yu et al., “Comprehensive Evaluation of Soil Moisture Sensing Technology Applications Based on Analytic Hierarchy Process and Delphi,” Agriculture, vol. 11, no. 11, p. 1116, Nov. 2021, doi: 10.3390/agriculture11111116.
14. H. Nasir, A. N. Aris, A. Lajis, K. Kadir, and S. I. Safie, “Development of Android Application for Pest Infestation Early Warning System,” in 2018 IEEE 5th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA), Nov. 2018, pp. 1–5. doi: 10.1109/ICSIMA.2018.8688774.
15. A. Cabarcas, C. Arrieta, D. Cermeño, H. Leal, R. Mendoza, and C. Rosales, “Irrigation System for Precision Agriculture Supported in the Measurement of Environmental Variables,” in 2019 7th International Engineering, Sciences and Technology Conference (IESTEC), Oct. 2019, pp. 671–676. doi: 10.1109/IESTEC46403.2019.00125.
16. M. Ayaz, M. Ammad-Uddin, Z. Sharif, A. Mansour, and E.-H. M. Aggoune, “Internet-of-Things (IoT)-Based Smart Agriculture: Toward Making the Fields Talk,” IEEE Access, vol. 7, pp. 129551– 129583, 2019, doi: 10.1109/ACCESS.2019.2932609.
17. H. WANG, Y. LIU, Z. HAN, and J. WU, “Extension of media literacy from the perspective of artificial intelligence and implementation strategies of artificial intelligence courses in junior high schools,” in 2020 International Conference on Artificial Intelligence and Education (ICAIE), Jun. 2020, pp. 63–66. doi: 10.1109/ICAIE50891.2020.00022.
18. Z. Li, “Analysis on the Influence of Artificial Intelligence Development on Accounting,” in 2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), Jun. 2020, pp. 260–262. doi: 10.1109/ICBAIE49996.2020.00061.
19. X. Fu, “The Application of Artificial Intelligence Technology in College Physical Education,” in 2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), Jun. 2020, pp. 263–266. doi: 10.1109/ICBAIE49996.2020.00062.
20. N. Wang, Y. Liu, Z. Liu, and X. Huang, “Application of Artificial Intelligence and Big Data in Modern Financial Management,” in 2020 International Conference on Artificial Intelligence and Education (ICAIE), Jun. 2020, pp. 85–87. doi: 10.1109/ICAIE50891.2020.00027.
21. S. Zel and E. Kongar, “Transforming Digital Employee Experience with Artificial Intelligence,” in 2020 IEEE / ITU International Conference on Artificial Intelligence for Good (AI4G), Sep. 2020, pp. 176–179. doi: 10.1109/AI4G50087.2020.9311088.
22. R. Sharma, “Artificial Intelligence in Agriculture: A Review,” in 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS), May 2021, pp. 937–942. doi: 10.1109/ICICCS51141.2021.9432187.
23. K. Pahwa and N. Agarwal, “Stock Market Analysis using Supervised Machine Learning,” in 2019 International Conference on Machine Learning, Big Data, Cloud and Parallel Computing (COMITCon), Feb. 2019, pp. 197–200. doi: 10.1109/COMITCon.2019.8862225.
24. Q. Zhao, J. Sun, H. Ren, and G. Sun, “Machine-Learning Based TCP Security Action Prediction,” in 2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE), Dec. 2020, pp. 1329–1333. doi: 10.1109/ICMCCE51767.2020.00291.
25. J. Ma, “Machine Learning in Predicting Diabetes in the Early Stage,” in 2020 2nd International Conference on Machine Learning, Big Data and Business Intelligence (MLBDBI), Oct. 2020, pp. 167–172. doi: 10.1109/MLBDBI51377.2020.00037.
26. N. Li, T. Zong, and Z. Zhang, “Prediction of the Electronic Work Function by Regression Algorithm in Machine Learning,” in 2021 IEEE 6th International Conference on Big Data Analytics (ICBDA), Mar. 2021, pp. 87–91. doi: 10.1109/ICBDA51983.2021.9403202.
27. H. C. Kaskavalci and S. Gören, “A Deep Learning Based Distributed Smart Surveillance Architecture using Edge and Cloud Computing,” in 2019 International Conference on Deep Learning and Machine Learning in Emerging Applications (Deep-ML), Aug. 2019, pp. 1–6. doi: 10.1109/Deep-ML.2019.00009.
28. H. S. DIKBAYIR and H. Ïbrahim BÜLBÜL, “Deep Learning Based Vehicle Detection From Aerial Images,” in 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), Dec. 2020, pp. 956–960. doi: 10.1109/ICMLA51294.2020.00155.
29. N. Shen, “A Deep Learning Approach of English Vocabulary for Mobile Platform,” in 2021 13th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA), Jan. 2021, pp. 463–466. doi: 10.1109/ICMTMA52658.2021.00106.
30. A. Karami, M. Crawford, and E. J. Delp, “A Weakly Supervised Deep Learning Approach for Plant Center Detection and Counting,” in IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium, Sep. 2020, pp. 1584–1587. doi: 10.1109/IGARSS39084.2020.9324354.
31. I. Tudosa, F. Picariello, E. Balestrieri, L. de Vito, and F. Lamonaca, “Hardware Security in IoT era: the Role of Measurements and Instrumentation,” in 2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0 IoT), Jun. 2019, pp. 285–290. doi: 10.1109/METROI4.2019.8792895.
32. W. Shalannanda, I. Zakia, F. Fahmi, and E. Sutanto, “Implementation of the Hardware Module of IoT-based Infant Incubator Monitoring System,” in 2020 14th International Conference on Telecommunication Systems, Services, and Applications (TSSA, Nov. 2020, pp. 1–5. doi: 10.1109/TSSA51342.2020.9310901.
33. H. Tao, M. Z. A. Bhuiyan, A. N. Abdalla, M. M. Hassan, J. M. Zain, and T. Hayajneh, “Secured Data Collection With Hardware-Based Ciphers for IoT-Based Healthcare,” IEEE Internet of Things Journal, vol. 6, no. 1, pp. 410–420, Feb. 2019, doi: 10.1109/JIOT.2018.2854714.
34. H. Q. T. Ngo, T. P. Nguyen, and H. Nguyen, “Hardware Design for Intelligent IoT Approach to Optimize Parking Slots,” in 2019 International Conference on Advanced Computing and Applications (ACOMP), Nov. 2019, pp. 171–175. doi: 10.1109/ACOMP.2019.00034.
35. S. Qazi, B. A. Khawaja, and Q. U. Farooq, “IoT-Equipped and AIEnabled Next Generation Smart Agriculture: A Critical Review, Current Challenges and Future Trends,” IEEE Access, vol. 10, pp. 21219–21235, 2022, doi: 10.1109/ACCESS.2022.3152544.
36. S. Terence and G. Purushothaman, “Systematic Review of Internet of Things in Smart Farming,” Trans. Emerg. Telecommun. Technol., vol. 31, no. 6, Jun. 2020, doi: 10.1002/ett.3958.
37. Z. Ünal, “Smart Farming Becomes even Smarter with Deep Learning - A Bibliographical Analysis,” IEEE Access, vol. 8, pp. 105587– 105609, 2020, doi: 10.1109/ACCESS.2020.3000175.
38. E. Navarro, N. Costa, and A. Pereira, “A Systematic Review of IoT Solutions for Smart Farming,” Sensors, vol. 20, no. 15, p. 4231, Jul. 2020, doi: 10.3390/s20154231.
39. D. Moher, A. Liberati, J. Tetzlaff, and D. G. Altman, “Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement,” J Clin Epidemiol, vol. 62, no. 10, pp. 1006– 1012, 2009, doi: 10.1016/j.jclinepi.2009.06.005.
40. W. S. Alaloul, M. Altaf, M. A. Musarat, M. F. Javed, and A. Mosavi, “Systematic Review of Life Cycle Assessment and Life Cycle Cost Analysis for Pavement and a Case Study,” Sustainability, vol. 13, no. 8, p. 4377, Apr. 2021, doi: 10.3390/su13084377.
41. U. Shandilya and V. Khanduja, “Intelligent Farming System With Weather Forecast Support and Crop Prediction,” in 2020 5th International Conference on Computing, Communication and Security (ICCCS), Oct. 2020, pp. 1–6. doi: 10.1109/ICCCS49678.2020.9277437.
42. S. B. Kamatchi and R. Parvathi, “Improvement of Crop Production Using Recommender System by Weather Forecasts,” Procedia Computer Science, vol. 165, pp. 724–732, 2019, doi: 10.1016/j.procs.2020.01.023.
43. H. Tarik and O. M. Jamil, “Weather Data For The Prevention Of Agricultural Production With Convolutional Neural Networks,” in 2019 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS), Apr. 2019, pp. 1–6. doi: 10.1109/WITS.2019.8723765.
44. S. Suhag, N. Singh, S. Jadaun, P. Johri, A. Shukla, and N. Parashar, “IoT based Soil Nutrition and Plant Disease Detection System for Smart Agriculture,” in 2021 10th IEEE International Conference on Communication Systems and Network Technologies (CSNT), Jun. 2021, pp. 478–483. doi: 10.1109/CSNT51715.2021.9509719.
45. R. Anand, D. Sethi, K. Sharma, and P. Gambhir, “Soil Moisture and Atmosphere Components Detection System Using IoT and Machine Learning,” in 2019 International Conference on Smart Systems and Inventive Technology (ICSSIT), Nov. 2019, pp. 842–847. doi: 10.1109/ICSSIT46314.2019.8987754.
46. L. D. Rodić, T. Županović, T. Perković, P. Šolić, and J. J. P. C. Rodrigues, “Machine Learning and Soil Humidity Sensing: Signal Strength Approach,” ACM Trans. Internet Technol., vol. 22, no. 2, Oct. 2021, doi: 10.1145/3418207.
47. R. Reshma, V. Sathiyavathi, T. Sindhu, K. Selvakumar, and L. SaiRamesh, “IoT based Classification Techniques for Soil Content Analysis and Crop Yield Prediction,” in 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Oct. 2020, pp. 156–160. doi: 10.1109/ISMAC49090.2020.9243600.
48. A. Goap, D. Sharma, A. K. Shukla, and C. Rama Krishna, “An IoT based smart irrigation management system using Machine learning and open source technologies,” Computers and Electronics in Agriculture, vol. 155, pp. 41–49, 2018, doi: 10.1016/j.compag.2018.09.040.
49. J. Kwok and Y. Sun, “A Smart IoT-Based Irrigation System with Automated Plant Recognition Using Deep Learning,” in Proceedings of the 10th International Conference on Computer Modeling and Simulation, 2018, pp. 87–91. doi: 10.1145/3177457.3177506.
50. A. Murthy, C. Green, R. Stoleru, S. Bhunia, C. Swanson, and T. Chaspari, “Machine Learning-Based Irrigation Control Optimization,” in Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, 2019, pp. 213–222. doi: 10.1145/3360322.3360854.
51. V. Psiroukis, B. Espejo-Garcia, A. Chitos, A. Dedousis, K. Karantzalos, and S. Fountas, “Assessment of Different Object Detectors for the Maturity Level Classification of Broccoli Crops Using UAV Imagery,” Remote Sensing, vol. 14, no. 3, p. 731, Feb. 2022, doi: 10.3390/rs14030731.
52. D. Li et al., “Improving Potato Yield Prediction by Combining Cultivar Information and UAV Remote Sensing Data Using Machine Learning,” Remote Sensing, vol. 13, no. 16, p. 3322, Aug. 2021, doi: 10.3390/rs13163322.
53. L. el Hoummaidi, A. Larabi, and K. Alam, “Using unmanned aerial systems and deep learning for agriculture mapping in Dubai,” Heliyon, vol. 7, no. 10, p. e08154, 2021, doi: 10.1016/j.heliyon.2021.e08154.
54. C. Li, T. Zhen, and Z. Li, “Image Classification of Pests with Residual Neural Network Based on Transfer Learning,” Applied Sciences, vol. 12, no. 9, p. 4356, Apr. 2022, doi: 10.3390/app12094356.
55. Z. Hu et al., “Research on Identification Technology of Field Pests with Protective Color Characteristics,” Applied Sciences, vol. 12, no. 8, p. 3810, Apr. 2022, doi: 10.3390/app12083810.
56. N. T. Nam and P. D. Hung, “Pest Detection on Traps Using Deep Convolutional Neural Networks,” in Proceedings of the 2018 International Conference on Control and Computer Vision, 2018, pp. 33–38. doi: 10.1145/3232651.3232661.
57. E. L. Mique and T. D. Palaoag, “Rice Pest and Disease Detection Using Convolutional Neural Network,” in Proceedings of the 2018 International Conference on Information Science and System, 2018, pp. 147–151. doi: 10.1145/3209914.3209945.
58. R. Reedha, E. Dericquebourg, R. Canals, and A. Hafiane, “Transformer Neural Network for Weed and Crop Classification of High Resolution UAV Images,” Remote Sensing, vol. 14, no. 3, p. 592, Jan. 2022, doi: 10.3390/rs14030592.
59. F. Garibaldi-Márquez, G. Flores, D. A. Mercado-Ravell, A. RamírezPedraza, and L. M. Valentín-Coronado, “Weed Classification from Natural Corn Field-Multi-Plant Images Based on Shallow and Deep Learning,” Sensors, vol. 22, no. 8, p. 3021, Apr. 2022, doi: 10.3390/s22083021.
60. N. Razfar, J. True, R. Bassiouny, V. Venkatesh, and R. Kashef, “Weed detection in soybean crops using custom lightweight deep learning models,” Journal of Agriculture and Food Research, vol. 8, p. 100308, 2022, doi: 10.1016/j.jafr.2022.100308.
61. J. Haichen, C. Qingrui, and L. Zheng Guang, “Weeds and Crops Classification Using Deep Convolutional Neural Network,” in 2020 the 3rd International Conference on Control and Computer Vision, 2020, pp. 40–44. doi: 10.1145/3425577.3425585.
62. U. F. Ukaegbu, L. K. Tartibu, M. O. Okwu, and I. O. Olayode, “Deep Learning Application in Diverse Fields with Plant Weed Detection as a Case Study,” in Proceedings of the International Conference on Artificial Intelligence and Its Applications, 2021, pp. 1–9. doi: 10.1145/3487923.3487926.
63. R. G. de Luna, E. P. Dadios, and A. A. Bandala, “Automated Image Capturing System for Deep Learning-based Tomato Plant Leaf Disease Detection and Recognition,” in TENCON 2018 - 2018 IEEE Region 10 Conference, Oct. 2018, pp. 1414–1419. doi: 10.1109/TENCON.2018.8650088.
64. U. Afzaal, B. Bhattarai, Y. R. Pandeya, and J. Lee, “An Instance Segmentation Model for Strawberry Diseases Based on Mask R-CNN,” Sensors, vol. 21, no. 19, p. 6565, Sep. 2021, doi: 10.3390/s21196565.
65. M. H. Widianto, M. I. Ardimansyah, H. I. Pohan, D. R. Hermanus, “A Systematic Review of Current Trends in Artificial Intelligence for Smart Farming to Enhance Crop Yield”, Journal of Robotics and Control (JRC), Vol. 3, No. 3,, DOI: 10.18196/jrc.v3i3.13760, pp. 269-278, May 2022.
67. M. A. Baballe, M. I. Bello, A. U. Alkali, Z. Abdulkadir, A. S. Muhammad, F. Muhammad, “The Unmanned Aerial Vehicle (UAV): Its Impact and Challenges”, Global Journal of Research in Engineering & Computer Sciences, Vol. 02, No 03, Journal homepage:, pp. 35-39, May-June | 2022.