Abstract
Presently it is really difficult to deal with agriculture and its requirements. The majority of the country’s population depends entirely on agriculture. Food production should also be improved as the world population is constantly growing. Recent technological advances have had a major impact on agriculture. Advancement of latest technologies like Internet of Things (IoT), Machine Learning (ML) and Deep Learning (DL) has attracted researcher’s attention to apply these methods to agriculture. Smart agriculture/farming is one of IoT’s emerging areas. Sensing temperature of soil, nutrients and humidity, controlling and analyzing water consumption for growth of plant are some of the recognized IoT based analytics applications. IoT devices collect and generate enormous quantities of data for various fields and applications. This chapter shows different farming issues that can be solved by applying deep learning and IoT technologies in agriculture domain.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Alahi, M. E. E., Nag, A., Mukhopadhyay, S. C., & Burkitt, L. (2018). A temperature-compensated graphene sensor for nitrate monitoring in real-time application. Sensors and Actuators A: Physical, 269, 79–90.
Baranwal, T., Nitika, & Pateriya, P.K. (2016). Development of IoT based smart security and monitoring devices for agriculture. 2016 6th International Conference - Cloud System and Big Data Engineering (Confluence), 597–602.
Food and Agriculture Organization of the United Nations. (n.d.). How to feed the World in 2050. http://www.fao.org/fileadmin/templates/wsfs/docs/expert_paper/How_to_Feed_the_World_in_2050.pdf
Garg, D., Khan, S., & Alam, M. (2020). Integrative use of IoT and deep learning for agricultural applications. In Proceedings of ICETIT 2019 (pp. 521–531). Cham: Springer.
International Atomic Energy Agency. (1998–2019). Agricultural water management, https://www.iaea.org/topics/agricultural-water-management
Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147, 70–90.
Kussul, N., Lavreniuk, M., Skakun, S., & Shelestov, A. (2017). Deep learning classification of land cover and crop types using remote sensing data. IEEE Geoscience and Remote Sensing Letters, 14(5), 778–782.
Li, H., Ota, K., & Dong, M. (2018). Learning IoT in edge: Deep learning for the internet of things with edge computing. IEEE Network, 32(1), 96–101.
Liakos, K., Busato, P., Moshou, D., Pearson, S., & Bochtis, D. (2018). Machine learning in agriculture: A review. Sensors, 18(8), 2674.
Mahdavinejad, M. S., Rezvan, M., Barekatain, M., Adibi, P., Barnaghi, P., & Sheth, A. P. (2018). Machine learning for Internet of Things data analysis: A survey. Digital Communications and Networks, 4(3), 161–175.
Marjani, M., Nasaruddin, F., Gani, A., Karim, A., Hashem, I. A. T., Siddiqa, A., & Yaqoob, I. (2017). Big IoT data analytics: architecture, opportunities, and open research challenges. IEEE Access, 5, 5247–5261.
Mehra, M., Saxena, S., Sankaranarayanan, S., Tom, R. J., & Veeramanikandan, M. (2018). IoT based hydroponics system using deep neural networks. Computers and Electronics in Agriculture, 155, 473–486.
Mohammadi, M., Al-Fuqaha, A., Sorour, S., & Guizani, M. (2018). Deep learning for IoT big data and streaming analytics: A survey. IEEE Communications Surveys & Tutorials.
Mohanraj, I., Ashokumar, K., & Naren, J. (2016). Field monitoring and automation using IOT in agriculture domain. Procedia Computer Science, 93, 931–939.
Mohanty, S. P., Hughes, D. P., & Salathé, M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7, 1419.
Muangprathub, J., Boonnam, N., Kajornkasirat, S., Lekbangpong, N., Wanichsombat, A., & Nillaor, P. (2019). IoT and agriculture data analysis for smart farm. Computers and Electronics in Agriculture, 156, 467–474.
Projectguru. (n.d.). Modern agriculture technology versus India’s agricultural practices. https://www.projectguru.in/publications/technology-indias-agricultural-practices/
Rahnemoonfar, M., & Sheppard, C. (2017). Deep count: Fruit counting based on deep simulated learning. Sensors, 17(4), 905.
Razzak, M. I., Naz, S., & Zaib, A. (2018). Deep learning for medical image processing: Overview, challenges and the future. In Classification in BioApps (pp. 323–350). Cham: Springer.
Reyes, A. K., Caicedo, J. C., & Camargo, J. E. (2015). Fine-tuning deep convolutional networks for plant recognition. CLEF (Working Notes), 1391.
Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., & McCool, C. (2016). Deepfruits: A fruit detection system using deep neural networks. Sensors, 16(8), 1222.
Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117.
Shekhar, Y., Dagur, E., Mishra, S., & Sankaranarayanan, S. (2017). Intelligent IoT Based Automated Irrigation System. International Journal of Applied Engineering Research, 12(18), 7306–7320.
Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., & Stefanovic, D. (2016). Deep neural networks based recognition of plant diseases by leaf image classification. Computational Intelligence and Neuroscience.
Song, X., Zhang, G., Liu, F., Li, D., Zhao, Y., & Yang, J. (2016). Modeling spatio-temporal distribution of soil moisture by deep learning-based cellular automata model. Journal of Arid Land, 8(5), 734–748.
Sundmaeker, H., Verdouw, C., Wolfert, S., & Pérez Freire, L. (2016). Internet of food and farm 2020. Digitising the Industry-Internet of Things connecting physical, digital and virtual worlds. Vermesan, O., & Friess, P. (Eds.), 129–151.
The state of food and agriculture. (2009). http://www.fao.org/3/a-i0680e.pdf
Thorat, A., Kumari, S., & Valakunde, N. D. (2017). An IoT based smart solution for leaf disease detection. In Big Data, IoT and data science, 2017 International conference on (pp. 193–198). IEEE.
Tzounis, A., Katsoulas, N., Bartzanas, T., & Kittas, C. (2017). Internet of things in agriculture, recent advances and future challenges. Biosystems Engineering, 164, 31–48.
Varman, S. A. M., et al. (2017). Deep learning and IoT for smart agriculture using WSN. 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), IEEE.
Verma, N. K., & Usman, A. (2016). Internet of Things (IoT): A relief for Indian farmers. In Global Humanitarian Technology Conference (GHTC), 2016 (pp. 831–835). IEEE.
Wolfert, S., Ge, L., Verdouw, C., & Bogaardt, M. J. (2017). Big data in smart farming – A review. Agricultural Systems, 153, 69–80.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this chapter
Cite this chapter
Garg, D., Alam, M. (2020). Deep Learning and IoT for Agricultural Applications. In: Alam, M., Shakil, K., Khan, S. (eds) Internet of Things (IoT). Springer, Cham. https://doi.org/10.1007/978-3-030-37468-6_14
Download citation
DOI: https://doi.org/10.1007/978-3-030-37468-6_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-37467-9
Online ISBN: 978-3-030-37468-6
eBook Packages: Computer ScienceComputer Science (R0)