Deep Learning Applications in Agriculture

Deep learning (DL), a part of machine learning (ML), comprises a contemporary technique for processing the images and analyzing the big data with promising outcomes. Deep learning methods are successfully being used in various sectors to gain better results. Agriculture sector is one of the sectors that could be benefitted from the deep learning techniques since the current agriculture techniques cannot keep up with the rapid growth in population. In this chapter, the recent trends in the applications of deep learning techniques in the agricultural sector and the survey of the research efforts that employ deep learning techniques are going to be discussed. Also, the models that are implemented are going to be analyzed and compared with the other existing models.

Smart Agriculture Using WSN and IoT

In India, the agriculture sector has an adverse effect and day by day the crop production is getting reduced. So, it is important to identify and implement a solution for the problem in order to increase the production. Smart technologies are introduced in this domain to improve the agriculture industry. The technologies like IoT, big data, cloud-based services, and GPS are gaining its importance in the field of agriculture. There is a rising need due to the requirement of higher precision in crop analysis, transformation of live data from the field and automated farming techniques for further improvement. The expected result of this is to have smart agriculture industry with the implementation of these smart techniques. In this chapter, the authors have discussed the challenges and benefits of IoT and various types of sensor for data acquisition.

Artificial and Natural Intelligence Techniques as IoP- and IoT-Based Technologies for Sustainable Farming and Smart Agriculture

In this chapter, the author describes the main new challenges and opportunities of blockchain technology for digital economy in Russia. The study in Russia showed that the Russian research community has not addressed a majority of these challenges, and he notes that blockchain developer communities actively discuss some of these challenges and suggest myriad potential solutions. Some of them can be addressed by using private or consortium blockchain instead of a fully open network. In general, the technological challenges are limited at this point, in terms of both developer support (lack of adequate tooling) and end-user support (hard to use and understand). The recent advances on developer support include efforts by of the towards model-driven development of blockchain applications sliding mode in intellectual control and communication and help the technological challenges and created tools. The chapter shows how avatars may communicate with each other by utilizing a variety of communications methods for sustainable farming and smart agriculture.

Artificial Intelligence and Its Applications in Agriculture With the Future of Smart Agriculture Techniques

Agriculture is the oldest and most dynamic occupation throughout the world. Since the population of world is always increasing and land is becoming rare, there evolves an urgent need for the entire society to think inventive and to find new affective solutions to farm, using less land to produce extra crops and growing the productivity and yield of those farmed acres. Agriculture is now turning to artificial intelligence (AI) technology worldwide to help yield healthier crops, track soil, manage pests, growing conditions, coordinate farmers' data, help with the workload, and advance a wide range of agricultural tasks across the entire food supply chain.

Using Drones in Smart Farming

Smart farming is the one area that has dependably been entrusted with giving nourishment to the world. With the consistently expanding populace, the horticultural segment needs to ensure that it copes with technology in order to build the measure of yield to meet the nourishment prerequisites of the world. To build the produce from farming, every single agrarian partner needs to accordingly get rid of customary rural practices and grasp current horticultural practices that will upset the field of agribusiness. One of these innovations that are intended to alter the field of agribusiness is the fuse of drones into cultivating. Drones can help famers in a range of tasks from analysis and planning to the real planting of yields and the ensuing observing of fields to find out wellbeing and development. This aim of this chapter is to provide an overview of how drones can help take agriculture to new sustainability heights.

Food 4.0

The fourth industrial revolution is a prospective innovation path for human life to possibly replace human intelligence and manual labour with artificial intelligence and robotics. The concept of 4IR is being embraced and applied in all sectors of human life. The academics are researching intensely into the revolution, while industry captain braces up to the inevitable and fast implementation in energy, automobile, telecommunication, services, security, medicine, and other industrial sectors. Agriculture and food sector, which is termed Food 4.0, being the highest employer of human resources, is a major sector that is expected to benefit tremendously from the concept and application of 4IR in driving the sector into the new era of development.

Farming Automation

Farming automation requires a whole lot of new skills and use of technology for achieving a substantial increase in the crop yield. Smart farming enables the use of technology in tracking, monitoring, and analyzing various farming operations. Internet of things (IoT) platform is formed with sensors and actuators, cameras and drones, telecommunication technologies, edge devices, cloud servers, and specialized hardware and software. This chapter will discuss the available hardware and software technology elements that can be used in farm automation. The chapter is comprised of four sections. The first section provides an overview of precision agriculture and smart farming. The second section provides the literature review of existing research. The third section describes IoT techniques, sensors, and cloud and edge computing solutions for the implementation of smart farming. The fourth section provides a few case studies of the application of IoT in smart farming. Specifically, the chapter will describe the IoT platform solution for complete farm automation.

Digital Technologies for Smart Agriculture

This chapter will provide the reader with an introduction to the modern emerging technologies like cloud computing, machine learning, and artificial intelligence used in agriculture. Then a glimpse of complete crop cycle follows, including seven steps, namely crop selection, soil preparation, seed selection, seed sowing, irrigation, crop growth, fertilizing and harvesting; and how these digital technologies are helpful for the crop cycle is also explained in this chapter. The rest of the chapter will explain the merger of the modern digital technologies with the agricultural crop cycle and how the future farming will work.

Development of a Solar-Powered Greenhouse Integrated With SMS and Web Notification Systems

Energy for heating and cooling is among the biggest costs in greenhouse crop production. This has led to a rethink on energy-saving strategies, including the demand for solar energy as a viable renewable and sustainable choice for greenhouse farming. This chapter presents the development of a solar-powered system leveraging on internet of things and GSM technologies for sensing, controlling, and maintaining optimal climatic parameters inside a greenhouse. The proposed system is designed to automatically measure and monitor changes in temperature, humidity, soil moisture, and the light intensity. The strategy utilized in the design framework provides the user with the information of the measured parameters online and via SMS regardless of their geographical location. The chapter also incorporates a mechanism to self-regulate the climatic condition inside the greenhouse, suitable for the plant growth. Such a system can help improve the quantity and quality of crops grown in a greenhouse. Tests carried out on the system prove its effectiveness according to the design considerations.

WSN, APSim, and Communication Model-Based Irrigation Optimization for Horticulture Crops in Real Time

The use of wireless sensor networks, the internet of things, and advanced technologies lead to new direction of research in the agriculture domain called prescriptive agriculture. Prescriptive agriculture is the enforcement of precision agriculture, which is observing, measuring, and responding to inter and intra field variability of farm field. In this chapter, the advent of wireless sensor network, APSim, and communication model spurred a new direction in the farming domain at optimizing irrigation. Sensors are programmed to collect the datasets of climatic parameters such as relative humidity and temperature, where the datasets were forwarded to the server through a GSM module. Datasets collected were analyzed through statistical software for grown crops by considering inter and intra farm field conditions. Finally, information on irrigation is decimated through an algorithm designed by way2SMS and WebHost server.