11.1 Introduction

The word “Internet of Things” (IoT) was first proposed by Kevin Ashton, a British visionary, in 1999. Internet of Things has been broadly applied in different fields for quite some time like home, industry, and many other applications acquiring attention all over the world. It is very clear from the term itself that the IoT network will provide us with a technological world in which day-to-day tools, sensors, equipment enhanced by computing power and network functionalities, in short, physical objects that can be classified as “Things” will be able to play a role as individual components or as distributed collaborations of interconnected devices [1]. The computer, the Internet and mobile Internet technology have brought a lot of great changes in human society in the last few decades.

In this present technical era, it is quite impossible to think of a single moment in our daily lives without any device and the Internet. IoT has been changing the meaning and dimensions of our lives for quite some time by connecting different parts of our lives. To put simply, IoT is ruling the technical world and hence enables us to connect with anything at anywhere and at any time. The interconnectivity, ubiquitous, heterogenic, dynamic changes, interoperability, sensing and actuation, and enormous scalability characteristics of IoT make it very powerful to integrate everyday objects through the Internet [1]. The present and upcoming technology of IoT have the huge potentiality of connecting machine-to-machine, human-to-human making it a global smart communication through smart technologies like wireless sensor networks, mobile computing, etc. IoT is serving worldwide smart environments in everyday life domains such as the farming system, transport system, agriculture, healthcare system, traffic system, security system, and many more [1].

Agriculture in basic terms is a science of soil cultivation, crop production, animal husbandry, and, ultimately, the marketing of the resulting products [2]. The increasing demand for food in quantitative and qualitative terms can be met by fusing technology with farming practices. The global population has grown dramatically, and so there needs to be such a rise in crop production to satisfy world demand for food and nutrition. With the integration of IoT with agriculture, it will not only be beneficial to the farmers but also the development of the nation. Thus, the development of efficient smart agricultural systems will revolutionize the agricultural industry.

Integrating agriculture along with IoT presumes that the connections will be wireless classified based on energy consumption, the uplink and downlink data rate, and device per success point, topology, packet size, and channel width and frequency bandwidth. IoT-enabled agricultural systems help in finding accurate details about the environmental conditions, soil conditions, etc. to get better productivity. The sensors, when properly installed and maintained, save water while also increasing production output. At the same time, Cloud Computing offers ample tools and solutions for managing, storing, and analyzing the vast amount of data produced by devices that can be used to automate tasks, predict scenarios, and enhance several real-time activities that allow farmers to decide on different factors for crop improvement. For example, diseases of plants are conventionally detected manually by agriculture experts, which can be inaccurate at times and is also very time-consuming. To avoid such inaccurate results and time-consuming processes, automatic and remote identification of crop diseases can be applied through Wireless Sensor Networks (WSN) and IoT [2]. At the same time, agriculture is accompanied by a lot of other problems like climate changes, environmental changes, increment in urbanization, water scarcity, soil moisture factors, shortage of arable land for growing crops [3, 4]. Given the problems faced in agriculture mentioned above, it can be understood that there is a need for a sufficient range of crop varieties, a proper adaptation of agricultural practices, and sustainability to conserve scarce resources [4]. Such factors affect agricultural growth, such as crop productivity, diseases, and yield generation. Hence, there is a need to incorporate technology with agriculture to increase production.

Modern techniques can be used to grow crops in a controlled environment [5] which can help humans to lead wise and smart agriculture. Since, Cloud Computing also provides sufficient resources and facilities for handling, processing, and evaluating the vast volume of data collected from sensors; so it can be used to optimize operations, forecast scenarios, and improve broad-based real-time activities for smart farming. IoT will be able to help farmers get a timely cultivating guideline related to the parameters like the seasonal plant diseases, pesticide usage, pest control, crop treatment, irrigation management, natural disasters affecting crops and the recovery methods [4, 5]. Other existing technologies along with WSNs that are feasible with IoT are radio frequency identification (RFID), middleware technologies, and cloud computing and end-user applications [6]. Hence, these technologies have a great impact on smart farming or agriculture along with a strong impact on the global economy.

This chapter of the book focuses mainly on the implementation of the IoT-enabled agricultural system, that is, which modern technologies are integrated with the agricultural sector that are benefitting the farmers and increasing the quality of the products, productivity’s volume, and the profitability of a business. The chapter also aims at the challenges and the security issues along with the scopes and trends faced by these systems as many of the current surveys at present focuses on the constraints, pitfalls, and benefits of the agricultural food sector on large scale.

Section 11.2 consists of a literature survey done on various existing systems mentioning the results, pros, and cons of these systems along with a table showing the survey analysis. In Section 11.3, the challenges to implement IoT-enabled systems have been elaborated. Section 11.4 has an explanation of the security issues of IoT systems and measures that can be taken. In Section 11.5 the future research direction of the IoT-enabled systems are explained. Section 11.6 has a short conclusion of this book chapter.

11.2 Background & Related Works

Agriculture is the primary source of food grains and other natural resources, and food is the basis of human life. This area, therefore, plays a very key role in the rate of economic growth of the country. Enhancing the agricultural sector with the implementation of new technology is also one of the major challenges faced by developing countries. Unfortunately, several farmers in developing countries still use conventional farming methods, resulting in low yields of crops and fruit. Some of the characteristics of agriculture that affect the quality and production of the crops and are different for every crop are enormous, dynamics, complexity, and spatiality. IoT-enabled agriculture system application enables the farmers to not only monitor the cultivating crops better but also to scan the soil along with access to the GPS. Access to remote locations can be established to track and manage agricultural fields. Plant disease is a very big threat to cultivation. Conventionally, these diseases are detected manually by experts, which other than being time-consuming are also inaccurate at times. The identification of plant diseases is possible through WSN and IoT. Raspberry Pi3 hardware interfaced with wireless sensors when deployed in the agricultural fields can measure the parameters of the environment and also monitor the agriculture field.

A Random Forest Classifier technique is used to classify the diseases in a system proposed by Devi et al. [2]. Plant images are collected repeatedly and continuously via a camera sensor that is integrated with Raspberry Pi3 hardware and Gray Level Co-occurrence Matrix (GLCM) features are extracted to be transmitted to the cloud via Wi-Fi. In this system, plant disease is identified at an early stage so that the spread of such disease can be avoided. The system consists of five phases to detect plant diseases using image processing techniques and IoT-image acquisition (captured using sensor cameras which are interfaced with Raspberry Pi3 hardware model), image pre-processing (resizing captured image to 256 × 256 size and converting color image to gray image to calculate the histogram equalization), image segmentation (segmentation is done on the obtained equalized image using k-means clustering technique), feature extraction (Texture features such as energy, dissimilarity, contrast, correlation are retrieved from the segmented image and the features are determined from the GLCM matrix) and finally image classification (Random Forest Classification (RFC) technique is used to classify and analyze finally). The proposed system is capable of identifying diseases with increased accuracy of 24% compared to the current K-NN classifier.

In some other recent studies have been done on the growth of the monitoring system developed for the crops and the real-world deployments [4], and the main focus has been kept on the leaf area index (LAI) in this study which is a specific crop parameter. Bauer et al. [4] proposed a system that focuses on the LAI, a well-known key parameter that provides details about the performance of the photosynthesis and the plants’ vital conditions. The architecture [4] developed mainly comprises of a WSN based monitoring system which is tailored for in-situ LAI assessment. The key approaches of the architecture are simplicity of the software, redundancy of the hardware, and control of the entire system. A holistic IoT-based agricultural framework to track crops continuously and evaluate the LAI appropriate for the accurate monitoring of crop growth based on an in-situ WSN that is optimized to collect sensor information is presented in Ref. [4]. This sensor network is linked to the central server using an IoT-based MQTT infrastructure and the networking used is the Public Land Mobile Network (PLMN).

Some systems propose that real-time notifications can be delivered to orient smart irrigation based on analyzing information and without the intervention of humans, for example, systems proposed by Balaji et al. [7]. Hartung et al. [8] explained that WSNs have been deployed in the agricultural sector for some time to enhance the remote control of agricultural products and resources. The capability of WSNs to improve efficiency and reduce waste has improved remarkably. More recently, with the increase in the potentiality of WSNs, these deployments have been integrated with IoT successfully as shown by Vasist et al. [9].

Mat et al. [10] presented a system of Green House Management System (GHMS) in which Wireless Sensor Networks (WSNs) have been used. The system will measure soil moisture, the wetness of air, the temperature of air by using a soil moisture sensor, a humidity sensor, and a temperature sensor respectively. The XBee technology [10] has been selected for WSN. A GUI is connected by XBee which can be a computer or a mobile phone. The GreenHouse Management System (GHMS) uses various sensors to collect environmental data like temperature, humidity, and soil moisture. The readings of the sensors will decide which device (fan or water pump) will be switched ON or OFF. Here, the moisture was being used to monitor the input measured in Volumetric Water Content (VWC). The data values decide which device should be ON or OFF. The system uses wireless technology.

Suma et al. [11] proposed a system that uses a microcontroller connected with various sensors (temperature, moisture, and PIR sensor) through RS232. The system receives data and it contains a buzzer and LED light which is set ON and the LED starts to blink if the value received exceeds a threshold value. This system uses sensors, a microcontroller, and RS232 to check incoming sensor values with threshold values. If the sensor value exceeds a certain threshold, the buzzer is switched on and the LED immediately begins to blink. This sends an alarm to the farmer and the power is switched off immediately. The system contains two modes manual and automatic. The system also contains an android application. In the manual mode, the user must manually turn on and off using the built android program. In automatic mode, it is done automatically. In both modes, the implementation is done using a GSM module.

Drought is a major serious problem that limits crop yield productive capacity. Many areas of the world face this problem with varying degrees of severity. To address this problem, remote sensing, especially in very rural areas, is used to acquire frequent soil moisture data that help to assess agricultural drought in remote regions. For this reason, the Soil Moisture and Ocean Salinity (SMOS) satellite was launched in 2009, which offers global soil moisture maps every one to two days. SMOS L2 was used in the framework proposed by Martínez-Fernández et al. [12] for the measurement of the Soil Water Deficit Index (SWDI) in Spain. They used various methods and compared the data derived from in situ data on soil water metrics with the SWDI. An autonomous, sensor-based, and vision-based robot named Agribot was developed for seed sowing by Santhi et al. [13]. The robot can execute on any crop yields as long as the robot’s self-awareness is verified via the global and local maps created by the Global Positioning System (GPS) and the on-board vision system is equipped with a personal computer.

On the other hand, more data collection functions, such as environmental monitoring, plant health, and pest problems, can be provided by field sensors in every corner of the crop cycle. Approaches such as vehicle precision spraying and automated VRT chemigation was proposed by Oberti et al. [14], which are widely used in smart fertilization, can also be used to treat diseases and other pesticide concerns. Some IoT-based automated traps are advantageous over remote sensing as they can count, track, identify insect types, and export data to the cloud for further evaluation. New approaches are also provided through the advancement of robotic technology. An agricultural robot with multi-spectral sensing systems, under the guidance of a remote IoT disease management system, configured with accurate spray nozzles, can locate and deal with pest challenges more precisely. This type of IoT-based pest management system hence supports the restoration of the natural climate along with reducing the overall expenditures.

Some crops that are grown indoors are less likely to be affected by the climate and the environment. This is called Greenhouse farming in which crops are not limited to receive lights only during day time. Such farming allows crops to grow under suitable conditions in any part of the world and at any time. Greenhouse farming proposed by Akkas et al. [15] has also gained well acknowledgment as it allows the successful production of various crops under a controlled environment like ventilation system, the accuracy of monitoring parameters, covering materials to control the wind, etc. An original routing protocol was developed and integrated into an IoT-based internally accessible agriculture application using MicaZ offthe-shelf wireless nodes [15] programmed in nesC. XServe is the primary gateway for wireless networks and other applications. The data obtained can also be processed on a server and day- and time-based reports are available for analysis. The specific data collected using the adjustable sensor nodes allows the farmers to study intricate trends of the farming process. The long term collected data allows agriculture specialists to make timetables that can grow specific crops in any controlled conditions.

From the literature review above, we can draw the conclusion that certain systems have been developed that identify environmental conditions such as crop temperature and soil conditions. Many systems are designed to keep inspection of the irrigation system. Many existing networks often lack the ability to find out whether any node is being hacked, or whether any cable is being cut by a human knowingly, or by animal scratching. Some systems not only keep a record of soil content but also use sensors to keep track of crop field safety. Many other systems are designed to see field protection.

The book chapter focuses on the challenges and security problems along with the scopes and developments these systems face, as many of the existing studies are currently centered on the large-scale limitations, risks, and benefits of the agricultural food field. The chapter focuses on those aspects of IoT-enabled agricultural systems that need to be considered very keenly while implementing these systems. This chapter of the book also emphasizes how the IoT-enabled agricultural system is applied, that is, what technological innovations are incorporated with the agricultural industry that favors the farmers and increases the quality of the goods, the volume of production and the profitability of a business. Table 11.1 shows the survey analysis of the existing systems in smart agriculture.

11.3 Challenges to Implement IoT-Enabled Systems

All technologies have their boon and their curse. Though smart farming is very effective and helps in the increase of productivity of the crops for the farmers, IoT-enabled agricultural systems do have their challenges and drawbacks. Some of the challenges in IoT-enabled agricultural system are discussed below.

11.4 Security Issues and Measures

Security has always been an essential component and a challenge in the implementation of IoT solutions. Improper access and privacy are the main issues on the list. With the advent of new technology, the IoT world has become more challenging and complex, making problems of privacy and protection more complicated.

Some of the security threats in IoT are the modification of data, data leakage, device modification, protocol hijacking, software vulnerabilities data gathering, and many more. Other factors affecting the security measures of IoT are network structure, terminal equipment, etc. The implementation of mobile nodes and wireless sensor networks enhances the role of wireless infrastructure in IoT applications. Wireless sensor networks become vulnerable to hackers from all over the world if they are left open to internet connectivity. Several reports have shown that IoT systems and users are seriously impacted by various security threats. Many of the major security concerns in IoT include an unsafe mobile interface, unprotected web interface, unsafe network infrastructure, inadequate authorization and authentication, insecure cloud interface, lack of security specifications, etc.

The security threats listed above are as general in the field of smart farming as in other IoT fields. Therefore, as with any other sector, farmers need to think about safety and security to use technology in the right way. The agricultural sector involves the challenges of hacking and theft of data brought about by the concept of precision farming. Organized crime or cyber terrorism can cause huge damage to the system, infrastructure, and business. Malware attacks may cause huge data loss. Less expensive machines in compromised locations are much more susceptible to cyber-attacks. Also, other modern and complex higher-level security threats are triggered by middleware integration, several software layers, APIs, machine-to-machine communication, etc.

Front-end sensors and devices, network, and back-end IT services are referred to as security threats of IoT. A few of the aspects associated with the back end of IT systems are the replacement of an operator and management of the safety of code resources. Since data is the brain of IoTbased smart agricultural systems, they are extremely valuable. Somebody might have unauthorized entry to the IoT provider database and can hence steal and manipulate the information. If anyone alters the data then even the smallest alterations may have disastrous consequences like if someone alters the data about the precise use of fertilizers or pesticides on plants then a huge amount of crops will be damaged and the farmer may incur huge losses. If the plants survive, excess amounts of fertilizers, pesticides, or chemicals may be consumed by humans and animals leading to more fatal outcomes. Thus data security is a top priority in IoT-based agricultural systems. There has been a lack of information significantly about data protection in IoT-enabled agricultural systems additionally. To make farming more efficient, IoT devices and data analysis practices are used by farm equipment providers.

Traditional agriculture with old technologies still prevails which often does not include a robust data backup along with a proper security concept. Let us take an example, farms equipment is connected via some field monitoring drones which are often linked to the internet and general channels. However, these types of equipment do not include basic security mechanisms such as employee logins monitoring or two-factor verification or authentication for remote access sessions. Nevertheless, these types of equipment do not have specific security features, such as employee logins monitoring or two-factor remote access session authentication. Major security standards must therefore be developed, such as network access control, identity authentication, 2-way verification, the confidentiality of data to improve protection. One of such systems developed to secure agricultural data from hackers uses AES 128 cryptography method [22]. The data obtained by the sensors is stored safely in the cloud in this system. The device which gathers the sensor data from the cloud gets the information framed into the JSON format at the encryption side in the device system. An extension of the AES 128 cryptography method has been proposed which gets the data encrypted. The reverse order is followed at the decryption side. The JSON parser is used to obtain the raw data at the decryption side of the server system. Other systems that define precision agriculture technologies, uncover security threats, devises lightweight encryption, and decryption method to facilitate a robust authentication solution in P2P communications of smart farming have also been proposed. A system that constructs the Common Vulnerability Scoring System (CVSS) score has also been predicted which calculates the score based on the technologies that are used in the farm for smart farming. Such a system can be used to focus on assessing the cyber-attack vulnerabilities in the technologies along with the smart farming environment.

The security issue is not only confined to the resources but also the agricultural products. For example, crops are often damaged due to rodents or insects as mentioned before. To detect the motion of insects, pests, or rodents, an Infrared (IR) Sensor can be used which can calculate the distance of the pests or insects and capture their images. These data can be stored in the websites so that the farmers can retrieve them and can get an idea as to how to protect the crops. Such systems can use web cameras to take the images, hence reducing manpower and usage of pesticides.

Drones or UAVs (Unmanned Aerial Vehicles) with equipped cameras, integrating modules, and sensors can be deployed in various sections of the field which can capture real-time images and contribute towards precision agriculture and also towards security issues of the fields. Some proposed solutions [23] which can be incorporated into Unmanned Aerial Vehicles (UAV) or drones have been discussed such as hyperspectral imaging is much better than multispectral imaging. RGB-D camera can be installed in drones to obtain real-time images that can be processed further using Support Vector Machine (SVM). These systems can have Global Positioning System (GPS) [24] built in it so that it can carry the right instruction by the operator across any location. The use of thermal or heat-seeking cameras can aid agriculture in a significant way by tracking the thermal properties of plants and crops and also by identifying the presence of harmful wildlife in agricultural fields. Thermal imaging also allows us to track plant pests, lack of water, and other physiological processes.

11.5 Future Research Direction

The future of IoT-based agricultural farming is the interconnected farm known as smart farming. Precision agriculture applications are usually targeted at large and conventional farming, but in the future, there will also be new levers to enhance the changing trends in agricultural investments, such as organic farming, family farming, etc. The coming years of agriculture lie in linking, gathering, and analyzing the vast majority of data to maximize efficiency and productivity. Licensed low-power wireless access (LPWA) is believed to be the new era of sustainable agriculture because of its good geographical coverage and also because it is cost-effective.

The use of drones can help to keep track of rainfall and environmental information, as well as the crop species that can be harvested in a particular environment. The historical information can be found out about an area and sent to agricultural experts to make better decisions as to which crop suits which environment. The identification of the type of fertilizers which is suitable for a specific crop is an important challenge that can be overcome if a device can say the change in soil type or weather change through intelligent systems.

Protection of the deployed devices from environmental damages and wildlife damages is a big challenge. Along with the sensor networks, the connectivity of the entire network is affected by adverse weather effects. Ground-level sensors when deployed in rural areas get moved sometimes due to wildlife, sometimes the cables get nibbles or even bit marks on sensors are seen. Sensors on higher levels are sometimes disturbed by birds. These challenges could be mitigated by the usage of solar energy equipment, more robust and shielded cables, and professional uninterrupted power supply (UPS) systems or by electric fences.

As data security is one of the critical problems in this field, local networks should be protected from interference by other networks. If the server is infected once, it will be very difficult to debug it. Reinstalling the software or shutting down a compromised system is not a feasible option and takes a major toll on the entire process. The devices can be authorized and authenticated if the middleware of IoT can manage a trust relationship with these devices. Middleware helps in promoting data interoperability (technical interoperability) and becomes the bridge between the heterogeneous applications in the cloud and the things.

Autonomous systems will always be more efficient according to the market situation, minimizing the cost and maximizing the profits. But in some cases, many small individual users can use the same IoT integrated agriculture process. In such cases, the technologies and the specifications of the sensors used by the different users may be different. So we need local network security along with making difference in the interoperability, the semantic annotation, and filtering of data coming from each user. Security, command over access rights to information, anonymity is hence important and should be maintained in such a system as many of these data stored by these users can be related to commercial enterprise, strategic thinking and are not to be disclosed to non-authorized entities.

11.6 Conclusion

In today’s world, every aspect of human life is connected by IoT devices such as automotive and logistics, remote monitoring of health and fitness, smart homes, industrial IoT, smart cities, alarm systems, etc. [25]. In the last decades, there has been a major technological transformation in the domain of agriculture and farming and this field has become more industrialized and technology-driven. IoT-enabled agricultural system applications have made farmers gain better control over growing crops by saving scarce resources like water, increasing scale efficiencies, decreasing production risks, and cutting down costs. The weather patterns, crop production, soil conditions, and all other data can be managed using smart sensors from remote locations, i.e. without the presence of humans. Climate-based controllers alter climatic factors to facilitate plant growth. This critical data collected can be recorded and stored in the cloud and later retrieved to monitor equipment output and staff results. Automation is an important IoT approach to achieve greater control over the production cycle while retaining higher growth potential and higher crop quality standards.

Hence we see that crop management devices make farming more precise by tracking factors like precipitation, temperature, leaf water capacity, and overall crop safety, which as a result makes it possible to avoid diseases or infestations that could adversely affect crop yields. In this book chapter, we see that there are quite a several challenges that are faced to implement such IoT-based agricultural systems. With the future moving towards and farming being the major source of survival in this world, a continuous study and research are going on to make the IoT-based agricultural system more and more feasible to all sectors of the farmers.

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