The study and literature on the Internet of Things (IoT) and its applications in agriculture for smart farming are increasing worldwide. However, the knowledge mapping trends related to IoT applications in plant disease, pest management, and control are still unclear and rarely reported. The primary aim of the present study is to identify the current trends and explore hot topics of IoT in plant disease and insect pest research for future research direction. Peer review articles published from Web of Science (WoS) Core Collection (2010-2021) were identified using keywords, and extracted database was analysed scientifically via Microsoft Excel 2019, VOSviewer and R programming software. A total of 231 documents with 5321 cited references authored by 878 scholars showed that the knowledge on the studied area has been growing positively and rapidly for the past ten years. India and China are the most productive countries, comprising more than half (52%) of the total access database on the subject area in WoS. IoT application has been integrated with other knowledge domains, such as machine learning, deep learning, image processing, and artificial intelligence, to produce excellent crop and pest disease monitoring research. This study contributes to the current knowledge of the research topic and suggests possible hot topics for future direction.

• Agbo, F. J., Oyelere, S. S., Suhonen, J., & Tukiainen, M. (2021). Scientific production and thematic breakthroughs in smart learning environments: A bibliometric analysis. Smart Learning Environments, 8(1), 1-25. https://doi.org/10.1186/s40561-020-00145-4

• Ale, L., Sheta, A., Li, L., Wang, Y., & Zhang, N. (2019, December 9-13). Deep learning based plant disease detection for smart agriculture. [Paper presentation]. 2019 IEEE Globecom Workshops (GC Wkshps), Waikoloa, Hawaii. https://doi.org/10.1109/GCWkshps45667.2019.9024439

• Alexandratos, N., & Bruinsma, J. (2012). World agriculture towards 2030/2050: The 2012 revision. https://ageconsearch.umn.edu/record/288998/files/a-ap106e.pdf

• Ali, M., Kanwal, N., Hussain, A., Samiullah, F., Iftikhar, A., & Qamar, M. (2020). IoT based smart garden monitoring system using NodeMCU microcontroller. International Journal of Advances in Applied Sciences, 7(8), 117-124.

• Ampatzidis, Y., De Bellis, L., & Luvisi, A. (2017). iPathology: Robotic applications and management of plants and plant diseases. Sustainability, 9(6), Article 1010. https://doi.org/10.3390/su9061010

• Araújo, S. O., Peres, R. S., Barata, J., Lidon, F., & Ramalho, J. C. (2021). Characterising the agriculture 4.0 landscape-Emerging trends, challenges and opportunities. Agronomy, 11(4), Article 667. https://doi.org/10.3390/agronomy11040667

• Aria, M., & Cuccurullo, C. (2017). Bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959-975. https://doi.org/10.1016/j.joi.2017.08.007

• Babu, T. G., & Babu, G. A. (2020). Identification of crop health condition using IoT based automated system. In S. Borah, V. E. Balas & Z. Polkowski (Eds.), Advances in Data Science and Management (pp. 421-433). Springer. https://doi.org/10.1007/978-981-15-0978-0_41

• Back, M. A., Haydock, P. P. J., & Jenkinson, P. (2002). Disease complexes involving plant parasitic nematodes and soilborne pathogens. Plant Pathology, 51(6), 683-697. https://doi.org/10.1046/j.1365-3059.2002.00785.x

• Börner, K., Chen, C., & Boyack, K. W. (2005). Visualising knowledge domains. Annual Review of Information Science and Technology, 37(1), 179-255. https://doi.org/10.1002/aris.1440370106

• Chavan, S. V., Gopalani, D. M., Heda, R. R., Israni, R. G., & Sethiya, R. B. (2020, May 13-15). KrishiAI-An IoT and machine learning based mobile application for farmers. [Paper presentation]. 4th International Conference on Intelligent Computing and Control Systems (ICICCS), Madurai, India. https://doi.org/10.1109/ICICCS48265.2020.9120952

• Chen, S., Xu, H., Liu, D., Hu, B., & Wang, H. (2014). A vision of IoT: Applications, challenges, and opportunities with China perspective. IEEE Internet of Things Journal, 1(4), 349-359. https://doi.org/10.1109/JIOT.2014.2337336

• Chen, W. L., Lin, Y. B., Ng, F. L., Liu, C. Y., & Lin, Y. W. (2019). RiceTalk: Rice blast detection using Internet of Things and artificial intelligence technologies. IEEE Internet of Things Journal, 7(2), 1001-1010. https://doi.org/10.1109/JIOT.2019.2947624

• de Oliveira, O. J., da Silva, F. F., Juliani, F., Barbosa, L. C. F. M., & Nunhes, T. V. (2019). Bibliometric method for mapping the state-of-the-art and identifying research gaps and trends in literature: An essential instrument to support the development of scientific projects. In S. Kunosic & E. Zerem (Eds.), Scientometrics Recent Advances (pp. 47-64). IntechOpen. https://doi.org/10.5772/intechopen.85856

• Ding, X., & Yang, Z. (2020). Knowledge mapping of platform research: A visual analysis using VOSviewer and CiteSpace. Electronic Commerce Research, 22, 787-809. https://doi.org/10.1007/s10660-020-09410-7

• Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of Business Research, 133, 285-296. https://doi.org/10.1016/j.jbusres.2021.04.070

• Fox, R. T. V, & Narra, H. P. (2006). Plant disease diagnosis. In B. M. Cooke, D. G. Jones & B. Kaye (Eds.), The Epidemiology of Plant Diseases (pp. 1-42). Springer. https://doi.org/10.1007/1-4020-4581-6_1

• Ghazali, M. H. M., Teoh, K., & Rahiman, W. (2021). A systematic review of real-time deployments of UAV-based Lora communication network. IEEE Access, 9, 124817-124830. https://doi.org/10.1109/ACCESS.2021.3110872

• Gupta, A. K., Gupta, K., Jadhav, J., Deolekar, R. V., Nerurkar, A., & Deshpande, S. (2019, March 13-15). Plant disease prediction using deep learning and IoT. [Paper presentation]. 6th International Conference on Computing for Sustainable Global Development (INDIACom), New Delhi, India.

• Hadi, M. K., Kassim, M. S. M., & Wayayok, A. (2021). Development of an automated multidirectional pest sampling detection system using motorised sticky traps. IEEE Access, 9, 67391-67404. https://doi.org/10.1109/ACCESS.2021.3074083

• Harun, A. N., Ani, N. N., Ahmad, R., & Azmi, N. S. (2013, December 02-04). Red and blue LED with pulse lighting control treatment for Brassica chinensis in indoor farming. [Paper presentation]. IEEE Conference on Open Systems (ICOS), Kuching, Malaysia. https://doi.org/10.1109/ICOS.2013.6735080

• Hassan, S. I., Alam, M. M., Illahi, U., Al Ghamdi, M. A., Almotiri, S. H., & Su’ud, M. M. (2021). A systematic review on monitoring and advanced control strategies in smart agriculture. IEEE Access, 9, 32517-32548. https://doi.org/10.1109/ACCESS.2021.3057865

• He, Y., Zeng, H., Fan, Y., Ji, S., & Wu, J. (2019). Application of deep learning in integrated pest management: A real-time system for detection and diagnosis of oilseed rape pests. Mobile Information Systems, 2019, 1-14. https://doi.org/10.1155/2019/4570808

• Hossam, M., Kamal, M., Moawad, M., Maher, M., Salah, M., Abady, Y., Hesham, A., & Khattab, A. (2018, December 17-19). PLANTAE: An IoT-based predictive platform for precision agriculture. [Paper presentation]. International Japan-Africa Conference on Electronics, Communications and Computations (JAC-ECC), Alexandria, Egypt. https://doi.org/10.1109/JEC-ECC.2018.8679571

• Hu, X., Sun, L., Zhou, Y., & Ruan, J. (2020). Review of operational management in intelligent agriculture based on the Internet of Things. Frontiers of Engineering Management, 7(3), 309-322. https://doi.org/10.1007/s42524-020-0107-3

• Jaishetty, S. A., & Patil, R. (2016). IoT sensor network based approach for agricultural field monitoring and control. IJRET: International Journal of Research in Engineering and Technology, 5(6), 45-48.

• Jarial, S. (2022). Internet of things application in Indian agriculture, challenges and effect on the extension advisory services-A review. Journal of Agribusiness in Developing and Emerging Economies, 1-15. https://doi.org/10.1108/JADEE-05-2021-0121

• Jawad, H. M., Nordin, R., Gharghan, S. K., Jawad, A. M., & Ismail, M. (2017). Energy-efficient wireless sensor networks for precision agriculture: A review. Sensors, 17(8), 1781. https://doi.org/10.3390/s17081781

• Jusoh, M. F., Muttalib, M. F. A., Krishnan, K. T., & Katimon, A. (2021). An overview of the internet of things (IoT) and irrigation approach through bibliometric analysis. IOP Conference Series: Earth and Environmental Science 756(1), Article 012041. https://doi.org/10.1088/1755-1315/756/1/012041

• Karnati, R., Rao, H. J., PG, O. P., & Maram, B. (2022). Deep computation model to the estimation of sulphur dioxide for plant health monitoring in IoT. International Journal of Intelligent Systems, 37(1), 944-971. https://doi.org/10.1002/int.22653

• Kavitha, B. C., Vallikannu, R., & Sankaran, K. S. (2020). Delay-aware concurrent data management method for IoT collaborative mobile edge computing environment. Microprocessors and Microsystems, 74, Article 103021. https://doi.org/10.1016/j.micpro.2020.103021

• Khan, F. A., Ibrahim, A. A., & Zeki, A. M. (2020). Environmental monitoring and disease detection of plants in smart greenhouse using internet of things. Journal of Physics Communications, 4(5), Article 055008. https://doi.org/10.1088/2399-6528/ab90c1

• Khanna, A., & Kaur, S. (2019). Evolution of Internet of Things (IoT) and its significant impact in the field of Precision Agriculture. Computers and Electronics in Agriculture, 157, 218-231. https://doi.org/10.1016/j.compag.2018.12.039

• Kim, S., Lee, M., & Shin, C. (2018). IoT-based strawberry disease prediction system for smart farming. Sensors, 18(11), Article 4051. https://doi.org/10.3390/s18114051

• Koubaa, A., Aldawood, A., Saeed, B., Hadid, A., Ahmed, M., Saad, A., Alkhouja, H., Ammar, A., & Alkanhal, M. (2020). Smart Palm: An IoT framework for red palm weevil early detection. Agronomy, 10(7), Article 987. https://doi.org/10.3390/agronomy10070987

• Kundu, N., Rani, G., Dhaka, V. S., Gupta, K., Nayak, S. C., Verma, S., Ijaz, M. F., & Woźniak, M. (2021). IoT and interpretable machine learning based framework for disease prediction in pearl millet. Sensors, 21(16), Article 5386. https://doi.org/10.3390/s21165386

• Lee, M., Hwang, J., & Yoe, H. (2013, December 03-05). Agricultural production system based on IoT. [Paper presentation]. IEEE 16Th International Conference on Computational Science and Engineering, Sydney, Australia. https://doi.org/10.1109/CSE.2013.126

• Lin, Y. B., Lin, Y. W., Lin, J. Y., & Hung, H. N. (2019). SensorTalk: An IoT device failure detection and calibration mechanism for smart farming. Sensors, 19(21), Article 4788. https://doi.org/10.3390/s19214788

• Linnenluecke, M. K., Marrone, M., & Singh, A. K. (2020). Conducting systematic literature reviews and bibliometric analyses. Australian Journal of Management, 45(2), 175-194. https://doi.org/10.1177/031289621987767

• Magdama, F., Monserrate-Maggi, L., Serrano, L., Sosa, D., Geiser, D. M., & Jiménez-Gasco, M. D. M. (2019). Comparative analysis uncovers the limitations of current molecular detection methods for Fusarium oxysporum f. sp. cubense race 4 strains. PLoS One, 14(9), Article e0222727. https://doi.org/10.1371/journal.pone.0222727

• Materne, N., & Inoue, M. (2018, March 12-13). IoT monitoring system for early detection of agricultural pests and diseases. [Paper presentation]. 12th South East Asian Technical University Consortium (SEATUC), Yogyakarta, Indonesia. https://doi.org/10.1109/SEATUC.2018.8788860

• Mishra, M., Choudhury, P., & Pati, B. (2021). Modified ride-NN optimiser for the IoT based plant disease detection. Journal of Ambient Intelligence and Humanized Computing, 12(1), 691-703. https://doi.org/10.1007/s12652-020-02051-6

• Mohamed Shaffril, H. A., Samsuddin, S. F., & Abu Samah, A. (2021). The ABC of systematic literature review: The basic methodological guidance for beginners. Quality & Quantity, 55(4), 1319-1346. https://doi.org/10.1007/s11135-020-01059-6

• Mongeon, P., & Paul-Hus, A. (2016). The journal coverage of Web of Science and Scopus: A comparative analysis. Scientometrics, 106(1), 213-228. https://doi.org/10.1007/s11192-015-1765-5

• Nasir, I. M., Bibi, A., Shah, J. H., Khan, M. A., Sharif, M., Iqbal, K., Nam, Y., & Kadry, S. (2021). Deep learning-based classification of fruit diseases: An application for precision agriculture. Computers, Materials & Continua, 66(2), 1949-1962. https://doi.org/10.32604/cmc.2020.012945

• Nawaz, M. A., Khan, T., Rasool, R. M., Kausar, M., Usman, A., Bukht, T. F. N., Ahmad, R. & Ahmad, J. (2020). Plant disease detection using Internet of Thing (IoT). International Journal of Advanced Computer Science and Applications, 11(1), 505-509. https://doi.org/10.14569/ijacsa.2020.0110162

• Oerke, E. C. (2006). Crop losses to pests. The Journal of Agricultural Science, 144(1), 31-43. https://doi.org/10.1017/s0021859605005708

• Olivares, B. O., Rey, J. C., Lobo, D., Navas-Cortés, J. A., Gómez, J. A., & Landa, B. B. (2021). Fusarium wilt of bananas: A review of agro-environmental factors in the Venezuelan production system affecting its development. Agronomy, 11(5), Article 986. https://doi.org/10.3390/agronomy11050986

• Omar, N., Zen, H., Anak Aldrin, N. N. A. A., Waluyo, W., & Hadiatna, F. (2020). Accuracy and reliability of data in IoT system for smart agriculture. International Journal of Integrated Engineering, 12(6), 105-116. https://doi.org/10.30880/ijie.2020.12.06.013

• Ostertagová, E. (2012). Modelling using polynomial regression. Procedia Engineering, 48, 500-506. https://doi.org/10.1016/j.proeng.2012.09.545

• Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L. Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R. Glanville, J., Grimshaw, J. M., Hrobjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Wilson, E. M., McDonald, S., … & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. International Journal Of Surgery, 88, Article 105906. https://doi.org/10.1016/j.ijsu.2021.105906

• Patil, S. S., & Thorat, S. A. (2016, August 12-13). Early detection of grapes diseases using machine learning and IoT. [Paper presentation]. Second International Conference on Cognitive Computing and Information Processing (CCIP), Mysuru, India. https://doi.org/10.1109/CCIP.2016.7802887

• Pawara, S., Nawale, D., Patil, K., & Mahajan, R. (2018, April 06-08). Early detection of pomegranate disease using machine learning and internet of things. [Paper presentation] 3rd International Conference for Convergence in Technology (I2CT), Pune, India. https://doi.org/10.1109/I2CT.2018.8529583

• Pérez-Expósito, J. P., Fernández-Caramés, T. M., Fraga-Lamas, P., & Castedo, L. (2017). VineSens: An eco-smart decision-support viticulture system. Sensors, 17(3), Article 465. https://doi.org/10.3390/s17030465

• Ramesh, B., Divya, M., & Revathi, G. P. (2020, November 12-13). Farm easy-IoT based automated irrigation, monitoring and pest detection using thingspeak for analysis of ladies finger plant. [Paper presentation]. International Conference on Recent Trends on Electronics, Information, Communication & Technology (RTEICT), Bangalore, India. https://doi.org/10.1109/RTEICT49044.2020.9315688

• Ratnaparkhi, S., Khan, S., Arya, C., Khapre, S., Singh, P., Diwakar, M., & Shankar, A. (2020). Smart agriculture sensors in IoT: A review. Materials Today: Proceedings. https://doi.org/10.1016/j.matpr.2020.11.138

• Rawi, R., Hasnan, M. S. I., & Sajak, A. A. S. (2020). Palm oil soil monitoring system for smart agriculture. International Journal of Integrated Engineering, 12(6), 189-199. https://doi.org/10.30880/ijie.2020.12.06.022

• Rochester, E., Ma, J., Lee, B., & Ghaderi, M. (2019, April 15-18). Mountain pine beetle monitoring with IoT. [Paper presentation]. IEEE 5th World Forum on Internet of Things (WF-IoT), Limerick, Ireland. https://doi.org/10.1109/WF-IoT.2019.8767291.

• Roslin, N. A., Che’Ya, N. N., Rosle, R., & Ismail, M. R. (2021). Smartphone application development for rice field management through aerial imagery and Normalised Difference Vegetation Index (NDVI) analysis. Pertanika Journal of Science & Technology, 29(2), 809-836. https://doi.org/10.47836/pjst.29.2.07

• Saleem, R. M., Kazmi, R., Bajwa, I. S., Ashraf, A., Ramzan, S., & Anwar, W. (2021). IOT-Based cotton whitefly prediction using deep learning. Scientific Programming, 2021, 1-17. https://doi.org/10.1155/2021/8824601

• Sarangi, S., Umadikar, J., & Kar, S. (2016). Automation of agriculture support systems using wisekar: Case study of a crop-disease advisory service. Computers and Electronics in Agriculture, 122, 200-210. https://doi.org/10.1016/j.compag.2016.01.009

• Seo, Y., & Umeda, S. (2021). Evaluating farm management performance by the choice of pest-control sprayers in rice farming in Japan. Sustainability, 13(5), Article 2618. https://doi.org/10.3390/su13052618

• Sethy, P. K., Behera, S. K., Kannan, N., Narayanan, S., & Pandey, C. (2021). Smart paddy field monitoring system using deep learning and IoT. Concurrent Engineering, 29(1), 16-24. https://doi.org/10.1177/1063293X21988944

• Shafi, U., Mumtaz, R., Iqbal, N., Zaidi, S. M. H., Zaidi, S. A. R., Hussain, I., & Mahmood, Z. (2020). A multi-modal approach for crop health mapping using low altitude remote sensing, internet of things (IoT) and machine learning. IEEE Access, 8, 112708-112724. https://doi.org/10.1109/ACCESS.2020.3002948

• Singh, V., & Misra, A. K. (2017). Detection of plant leaf diseases using image segmentation and soft computing techniques. Information Processing in Agriculture, 4(1), 41-49. https://doi.org/10.1016/j.inpa.2016.10.005

• Sobreiro, L. F., Branco, S., Cabral, J., & Moura, L. (2019, October 14-17). Intelligent insect monitoring system (I2MS): Using internet of things technologies and cloud based services for early detection of pests of field crops. [Paper presentation]. IECON 2019 - 45th Annual Conference of the IEEE Industrial Electronics Society, Lisbon, Portugal. https://doi.org/10.1109/IECON.2019.8927085

• Uddin, M. A., Mansour, A., Le Jeune, D., Ayaz, M., & Aggoune, E. H. M. (2018). UAV-Assisted dynamic clustering of wireless sensor networks for crop health monitoring. Sensors, 18(2), Article 555. https://doi.org/10.3390/s18020555

• Udutalapally, V., Mohanty, S. P., Pallagani, V., & Khandelwal, V. (2020). Scrop: A novel device for sustainable automatic disease prediction, crop selection, and irrigation in internet-of-agro-things for smart agriculture. IEEE Sensors Journal, 21(16), 17525-17538. https://doi.org/10.1109/JSEN.2020.3032438

• Van den Berg, W., Vos, J., & Grasman, J. (2012). Multimodel inference for the prediction of disease symptoms and yield loss of potato in a two-year crop rotation experiment. International Journal of Agronomy, 2012, Article 438906. https://doi.org/10.1155/2012/438906

• Van Eck, N. J., & Waltman, L. (2021). VOSviewer Manual Version 1.6.17. Universiteit Leiden.

• Varandas, L., Faria, J., Gaspar, P. D., & Aguiar, M. L. (2020). Low-cost IoT remote sensor mesh for large-scale orchard monitorization. Journal of Sensor and Actuator Networks, 9(3), Article 44. https://doi.org/10.3390/jsan9030044

• Vazquez, J. P. G., Torres, R. S., & Perez, D. B. P. (2021). Scientometric analysis of the application of artificial intelligence in agriculture. Journal of Scientometric Research, 10(1), 55-62. https://doi.org/10.5530/jscires.10.1.7

• Verma, S., Chug, A., & Singh, A. P. (2018, September 19-22). Prediction models for identification and diagnosis of tomato plant diseases. [Paper presentation]. International Conference on advances in computing, communications and informatics (ICACCI), Bangalore, India. https://doi.org/10.1109/ICACCI.2018.8554842.

• Wang, K. Q., & Cai, K. (2011). On design of sensor nodes in the rice planthopper monitoring system based on the internet of things. PIAGENG 2010: Photonics and Imaging for Agricultural Engineering, 7752, 74-81. https://doi.org/10.1117/12.887423

• Wang, X., Wang, Z., Zhang, S., & Shi, Y. (2015, September). Monitoring and discrimination of plant disease and insect pests based on agricultural IoT. [Paper presentation]. 4th International Conference on Information Technology and Management Innovation, Shenzhen, China. https://doi.org/10.2991/icitmi-15.2015.21

• Wei, X., Aguilera, M., Walcheck, R., Tholl, D., Li, S., Langston Jr, D. B., & Mehl, H. L. (2021). Detection of soilborne disease utilizing sensor technologies: Lessons learned from studies on stem rot of peanut. Plant Health Progress, 22(4), 436-444. https://doi.org/10.1094/PHP-03-21-0055-SYN

• Xie, H., Zhang, Y., & Duan, K. (2020). Evolutionary overview of urban expansion based on bibliometric analysis in Web of Science from 1990 to 2019. Habitat International, 95, Article 102100. https://doi.org/10.1016/j.habitatint.2019.102100

• Xu, J., Gu, B., & Tian, G. (2022). Review of agricultural IoT technology. Artificial Intelligence in Agriculture, 6, 10-22. https://doi.org/10.1016/j.aiia.2022.01.001

• Zhang, J., Liu, J., Chen, Y., Feng, X., & Sun, Z. (2021). Knowledge mapping of machine learning approaches applied in agricultural management - A scientometric review with CiteSpace. Sustainability, 13(14), Article 7662. https://doi.org/10.3390/su13147662

• Zhang, J., Rao, Y., Man, C., Jiang, Z., & Li, S. (2021). Identification of cucumber leaf diseases using deep learning and small sample size for agricultural internet of things. International Journal of Distributed Sensor Networks, 17(4), 1-13. https://doi.org/10.1177/15501477211007407