Smart Agriculture Services Using Deep Learning, Big Data, and IoT (Internet of Things)

Smart Agriculture Services Using Deep Learning, Big Data, and IoT (Internet of Things)

Ajay Sharma (Jaypee University of Information Technology, India)
Copyright: © 2021 |Pages: 37
DOI: 10.4018/978-1-7998-5003-8.ch010
OnDemand PDF Download:
No Current Special Offers


The internet of things is believed to have long-lasting effects in both technology and modern society. In a modern information society, IoT can be seen as a global infrastructure that enables more advanced services by connecting physical and virtual devices and things to currently existing and even upcoming information and communication technologies. IoT takes advantage of identification, data capture, processing, and communication capabilities of modern technology to allow regular machines to provide new data sources to applications, which in turn can offer more advanced services. In terms of ICT technologies, IoT adds any thing communication to any time and any place. An increase in technology also leads to the development of smart agriculture. This chapter deals with the different electronic sensors used for the smart agriculture like soil moisture sensor, node MCU, water flow sensor, relay, water pump, solar system. The next section deals with big data in smart agriculture.
Chapter Preview

Smart Agriculture

Agriculture from the earliest starting point, agriculture is a pivotal piece of human society because of the truth that man and agribusiness are legitimately identified with one another. This reality leads towards the headway advancement and upgraded enhancement of the ordinary, improper and tedious time-consuming systems methodologies, utilized for agriculture-based agribusiness. The fast-moving world, new patterns and innovative progression has changed the way of life of individuals. Developing new advances are turning into a significant piece of schedule. Smart homes and grids planned urban areas, smart campus and smart farming is a portion of the entire progressed and redesigned, data and correspondence advancements that are helping people to spare time and get quicker and aureate results[8]. Smart agriculture can be composed of these main paradigms;

  • 1.

    Smart Consumer

  • 2.

    Smart Farmer

  • 3.

    Smart farms

Key Terms in this Chapter

Deep Learning: A sub branch of Artificial intelligence in which we built the DL model and we don’t need to specify any feature to the learning model . In case of DL the model will classify the data based on the input data.

Machine Learning: It is again a sub set of AI in which we classify the data with the help of input data set, ANN, SVM, Random Forest are some of the algorithm used in this case.

Big Data: It is a term widely used for the unstructured data. People generally confused that what is big data, in simple term when we have lot of data of different kind of some particular problem we can say that its big data for example: crop data of rice plant it include all the aspect of rice like amount of water needed, time period, fertilizer requirement, height, width, etc.

IoT: Internet of things. It is an interdisciplinary field who is associated with the electronics and computer science. Electronics deals with the development of new sensors or hardware for IoT device and computer science deals with the development of software, protocols and cloud based solution to store the data generated form these IoT devices.

Complete Chapter List

Search this Book: