Integrative Use of IoT and Deep Learning for Agricultural Applications

Agriculture is the backbone of Indian economy. Most of the population of the country is directly or indirectly dependent on agriculture. Technology can improve agricultural outcomes. In this modern era, there is a major drift in agricultural methods from traditional approaches. Recent advancements in technology have had a great impact on agriculture and it has been established that IoT can be used in farming to enhance quality of agriculture. Evolution of Machine Learning (ML), Deep Learning (DL) and Internet of Things (IoT) has gathered attention of researchers to apply these techniques in fields like agriculture. It helps farmers to increase the productivity of their land so the worldwide demand for food can be fulfilled. This paper highlights various farming problems that can be solved using the synergistic application of deep learning and IoT. In this paper, previous work done with these technologies is discussed. Moreover, we have presented a comparison between Deep Learning and Machine Learning, with specific focus on the complete process of applying Deep Learning on agriculture data to make predictions for agricultural applications.

[1]  Mianxiong Dong,et al.  Learning IoT in Edge: Deep Learning for the Internet of Things with Edge Computing , 2018, IEEE Network.

[2]  Rijo Jackson Tom,et al.  IoT based hydroponics system using Deep Neural Networks , 2018, Comput. Electron. Agric..

[3]  Muhammad Imran Razzak,et al.  Deep Learning for Medical Image Processing: Overview, Challenges and Future , 2017, ArXiv.

[4]  Liborio Cavaleri,et al.  Prediction of the Fundamental Period of Infilled RC Frame Structures Using Artificial Neural Networks , 2015, Comput. Intell. Neurosci..

[5]  E Prabhu,et al.  Deep Learning and IoT for Smart Agriculture Using WSN , 2017, 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC).

[6]  J. Naren,et al.  Field Monitoring and Automation Using IOT in Agriculture Domain , 2016 .

[7]  Subhas Chandra Mukhopadhyay,et al.  A temperature-compensated graphene sensor for nitrate monitoring in real-time application , 2018 .

[8]  Jirapond Muangprathub,et al.  IoT and agriculture data analysis for smart farm , 2019, Comput. Electron. Agric..

[9]  Pushpendra Kumar Pateriya,et al.  Development of IoT based smart security and monitoring devices for agriculture , 2016, 2016 6th International Conference - Cloud System and Big Data Engineering (Confluence).

[10]  Amit P. Sheth,et al.  Machine learning for Internet of Things data analysis: A survey , 2017, Digit. Commun. Networks.

[11]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[12]  Ibrar Yaqoob,et al.  Big IoT Data Analytics: Architecture, Opportunities, and Open Research Challenges , 2017, IEEE Access.

[13]  Andreas Kamilaris,et al.  Deep learning in agriculture: A survey , 2018, Comput. Electron. Agric..

[14]  Thomas Bartzanas,et al.  Internet of Things in agriculture, recent advances and future challenges , 2017 .

[15]  Mohsen Guizani,et al.  Deep Learning for IoT Big Data and Streaming Analytics: A Survey , 2017, IEEE Communications Surveys & Tutorials.

[16]  Adil Usman,et al.  Internet of Things (IoT): A relief for Indian farmers , 2016, 2016 IEEE Global Humanitarian Technology Conference (GHTC).

[17]  Sangeeta Kumari,et al.  An IoT based smart solution for leaf disease detection , 2017, 2017 International Conference on Big Data, IoT and Data Science (BID).

[18]  Tristan Perez,et al.  DeepFruits: A Fruit Detection System Using Deep Neural Networks , 2016, Sensors.

[19]  J. Wolfert,et al.  Internet of Food and Farm 2020 , 2016 .

[20]  S. Wolfert,et al.  Big Data in Smart Farming – A review , 2017 .

[21]  Darko Stefanovic,et al.  Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification , 2016, Comput. Intell. Neurosci..

[22]  Patrizia Busato,et al.  Machine Learning in Agriculture: A Review , 2018, Sensors.