Towards Intelligent Agriculture Using Smart IoT Sensors

The word “smart” is quite commonly associated with different types of products of IoT sensors and its contemporary technology. The frequent progress in the contemporary technology includes convention and the progressive integration of microprocessor. This gives the smart sensors application to a wide range of applications. Smart sensors when associated with agriculture are known as smart agriculture. With the help of smart sensors, technology of internet of things has helped agriculture in facilitating its efficiency, which further helps in decreasing the impact of environment on the production of the crops and deprecate the expenses. This is done by a few methods like calculating the condition of the environment, which affects the production of the crops, keeping a check on the cattle health and indicating when some problem occurs. The author discussed about sensors, their nature and evolution, generations of smarts sensors, and how they became better with the course of time in terms of smart agriculture.

Internet of Things

Internet of things (IoT) is a system of interrelated computing devices, mechanical and digital machines, objects, animals or people that are provided with unique identifiers and the ability to transfer data over a network without requiring human-to-human or human-to-computer interaction. It has three layers. First layer is data acquisition through sensors and actuators, data transferring using different devices and last is data analysis with different analytic techniques. In this chapter, a conceptual overview of internet of things is mentioned. Different sensors and actuators which are responsible for data acquiring are described with their specification. Networking devices which are responsible for transferring data from sensors to server are also described with their applications. Data analytics techniques like descriptive, predictive, and perspective are also explained. Internet of things is now proven as boon for agriculture development. In the last section, different techniques are explained that are used in information and communication technique-enabled agriculture practices.

A Neural Network-Based Approach for Pest Detection and Control in Modern Agriculture Using Internet of Things

The networks acquire an altered move towards the difficulty solving skills rather than that of conventional computers. Artificial neural networks are comparatively crude electronic designs based on the neural structure of the brain. The chapter describes two different types of approaches to training, supervised and unsupervised, as well as the real-time applications of artificial neural networks. Based on the character of the application and the power of the internal data patterns we can normally foresee a network to train quite well. ANNs offers an analytical solution to conventional techniques that are often restricted by severe presumptions of normality, linearity, variable independence, etc. The chapter describes the necessities of items required for pest management through pheromones such as different types of pest are explained and also focused on use of pest control pheromones.

An Analysis of Big Data Analytics

In the development of the advanced world, information has been created each second in numerous regions like astronomy, social locales, medical fields, transportation, web-based business, logical research, horticulture, video, and sound download. As per an overview, in 60 seconds, 600+ new clients on YouTube and 7 billion queries are executed on Google. In this way, we can say that the immense measure of organized, unstructured, and semi-organized information are produced each second around the cyber world, which should be managed efficiently. Big data conveys properties such as unpredictability, 'V' factor, multivariable information, and it must be put away, recovered, and dispersed. Logical arranged data may work as information in the field of digital world. In the past century, the sources of data as to size were very limited and could be managed using pen and paper. The next generation of data generation tools include Microsoft Excel, Access, and database tools like SQL, MySQL, and DB2.

Plant Diseases Concept in Smart Agriculture Using Deep Learning

In the agricultural sector, plant leaf diseases and harmful insects represent a major challenge. Faster and more reliable prediction of leaf diseases in crops may help develop an early treatment technique while reducing economic losses considerably. Current technological advances in deep learning have made it possible for researchers to improve the performance and accuracy of object detection and recognition systems significantly. In this chapter, using images of plant leaves, the authors introduced a deep-learning method with different datasets for detecting leaf diseases in different plants and concerned with a novel approach to plant disease recognition model, based on the classification of the leaf image, by the use of deep convolutional networks. Ultimately, the approach of developing deep learning methods on increasingly large and accessible to the public image datasets provides a viable path towards massive global diagnosis of smartphone-assisted crop disease.

Internet of Things and the Role of Wireless Sensor Networks in IoT

Internet of things (IoT) is a network of connected devices that work together and exchange information. In IoT, things or devices means any object with its own IP address that is able to connect to a network and can send and receive using internet. Examples of IoT devices are computers, laptops, smart phones, and objects that are operational with chips to collect and correspond data over a network. The range of internet of things devices is huge. Consumers use smart phones to correspond with IoT devices.

Green Cloud

Distributed computing is an incredible region of information and correspondence progressions, introducing current challenges for environmental security. These advances have a diversity of use spaces, since they offer flexibility, are trustworthy and dependable, and offer prevalent at tolerably negligible exertion. The conveyed figuring rebellion is updating current frameworks organization, and offering promising biological protection prospects, for example, money related and inventive field of premium. The developments can improve quality yield and to shrink carbon impressions and (e-)waste. The structures can change by dispersed processing into green circulated registering. Finally, future research headings and open issues with respect to green circulated registering are shown. This outline is intended to fill in as best in class bearing to investigate green distributed computing.

Smart Agriculture and Farming Services Using IoT

IoT technology is used in many areas like the smart wearables, connected devices, automated machines, and driverless cars. However, in agriculture, the IoT has brought the greatest impact. The industrial IoT is a driving force behind increased agricultural production at a lower cost. In the next several years, the use of smart solutions powered by IoT will increase in the agriculture operations. The number of connected devices in agriculture will grow from 13 million in 2014 to 225 million by 2024. The applications of IoT in the agriculture industry have helped the farmers to monitor the water tank levels in real-time, which make the irrigation process more efficient. The advancement of IoT technology in agriculture operations has brought the use of sensors in every step of the farming process like how much time and resources a seed takes to become a fully grown vegetable. Internet of things in agriculture has come up as a second wave of the green revolution.

IoT-Based Agri-Safety Model

There are many problems in the agricultural sector. One of the major issues is the safety of crops from the animals. The crop land near forest or any reserved wildlife get affected by the animals, decreasing production. The result is the conflict between animals and farmers. This chapter proposes an inexpensive and effective way to alert the farmer of animal intrusion in the farm by employing a pressure load sensor deployed in the ground wired with a vertical mounted unit with the actuators. The vertically mounted unit produces a loud sound and LED light strobes to deter the animal and also alert the farmer.

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

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.