Emerging Trends in Disruptive Technology Management for Sustainable Development

Rik Das, Mahua Banerjee, Sourav De

• Publisher: Chapman and Hall/CRC
• Year: 2019
• ISBN:
  9781000712056
• Edition: 1
• Topic: Computer Science
• Subtopic: Computer Science General
• Index: Computer Science

Interdisciplinary approaches using Machine Learning and Deep Learning techniques are smartly addressing real life challenges and have emerged as an inseparable element of disruption in current times. Applications of Disruptive Technology in Management practices are an ever interesting domain for researchers and professionals. This volume entitled Emerging Trends in Disruptive Technology Management for Sustainable Development has attempted to collate five different interesting research approaches that have innovatively reflected diverse potential of disruptive trends in the era of 4 th. Industrial Revolution. The uniqueness of the volume is going to cater the entrepreneurs and professionals in the domain of artificial intelligence, machine learning, deep learning etc. with its unique propositions in each of the chapters. The volume is surely going to be a significant source of knowledge and inspiration to those aspiring minds endeavouring to shape their futures in the area of applied research in machine learning and computer vision.

The expertise and experiences of the contributing authors to this volume is encompassing different fields of proficiencies. This has set an excellent prelude to discover the correlation among multidisciplinary approaches of innovation. Covering a broad range of topics initiating from IoT based sustainable development to crowd sourcing concepts with a blend of applied machine learning approaches has made this volume a must read to inquisitive wits.

Features

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• Assorted approaches to interdisciplinary research using disruptive trends

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• Focus on application of disruptive technology in technology management

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• Focus on role of disruptive technology on sustainable development

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• Promoting green IT with disruptive technology

The book is meant to benefit several categories of students and researchers. At the students' level, this book can serve as a treatise/reference book for the special papers at the masters level aimed at inspiring possibly future researchers. Newly inducted PhD aspirants would also find the contents of this book useful as far as their compulsory course-works are concerned. At the researchers' level, those interested in interdisciplinary research would also be benefited from the book. After all, the enriched interdisciplinary contents of the book would always be a subject of interest to the faculties, existing research communities and new research aspirants from diverse disciplines of the concerned departments of premier institutes across the globe. This is expected to bring different research backgrounds (due to its cross platform characteristics) close to one another to form effective research groups all over the world. Above all, availability of the book should be ensured to as much universities and research institutes as possible through whatever graceful means it may be.

Hope this volume will cater as a ready reference to your quest for diving deep into the ocean of technology management for 4 th. Industrial Revolution.

1

IoT-Based Intelligent System for Identification of Plant Stress in Sustainable Agriculture

Debarshi Mazumder , Sudarshan Nandy, and Sudip Chatterjee

CONTENTS

1.1 Introduction

1.2 Organization of IoT

1.2.1 An Organized View of the Overall System

1.3 Methodology

1.3.1 Data Collection

1.3.2 Data Validation and Pre-Processing

1.3.3 Object Detection and Segmentation

1.3.4 Feature Extraction

1.3.5 Machine Learning Methods

1.4 Discussion

1.5 Conclusion

References

1.1 Introduction

The increasing global population demands quality food while maintaining the food quantity and the environment. It is estimated that the global population will be approximately 9 billion by 2050 and hence food production should increase by 70 per cent [1]. In developed and underdeveloped countries, the demand for the food is completed from agriculture-based products. Sustainable agriculture has a remarkable prospect in the field of agricultural production to fulfil the food requirements of the global population by maintaining the environmental ecology. In sustainable agriculture, advanced technologies are incorporated to detect plant stress, which has a direct impact on the quality and quantity of the agriculture product [2]. The growth of a plant in an unsatisfactory environment may be the cause behind the plant stresses. The effects of stresses can lead to deficiencies in growth, permanent damage, or death of plant; this reduces the quality as well as the quantity of the food production [3]. Plant stress factors are generally divided into two sets: biotic (includes living biological factors such as fungi, bacteria, virus, insects, parasites, weeds, etc.) and abiotic (includes non-living environmental factors such as light, water, temperature, drought, flood, nutrient deficiency, and other environmental factors) [4, 5]. In agriculture, environmental and biological factors play an important role in the growth, development, and productivity of the plant. Every plant gives good productivity if stress factors are within appropriate limits. Any unexpected changes in environmental and biological factors can cause deficiencies or damages in plant productivity and these changes may affect several parts of the plant, such as root, leaf, etc. [6]. It is observed that every biotic and abiotic stress consists of threshold values for the plant, and under this threshold value, good productivity can be expected [3, 7]. These unexpected or critical changes in the environmental or biological factors can exceed the threshold levels and it is then the main cause behind the deficiencies in productivity [8]. Usually, the identification and monitoring of the plant stresses are done by farmers with the naked eye, but nowadays detection of plant stresses due to biotic factors is also done using an Internet of Things (IoT)-based intelligent system [9, 10]. In this respect, IoT-based intelligent systems in sustainable agriculture is the most significant way to identify plant stresses as quickly as possible, and it is possible to decide correct and accurate amount of pesticide or another remedy [11–13].

Currently, IoT intelligent systems are used widely in the agricultural domain. Advancement and existing challenges present in IoT will make it more popular in the future [1]. An IoT-based intelligent system performs the commendable job of continuous monitoring in the concerned agriculture field and making predictions and decisions regarding the plant stresses of the field [14]. This advanced system has collected the raw data or information from numerous disparate systems. As per system requirements, these data are processed, segmented, and systematized by some smart algorithms [15]. In another process, some advanced algorithms are applied in agriculture for analyzing the data to identify and detect the early threats or warnings that are produced by the IoT-based prediction system [16]. If a system generates any threat or warning, then an alert is created automatically and an appropriate solution or remedy is suggested to the agriculture field-monitoring objects such as farmer, agriculture robot, agriculture vehicle, etc [17]. An IoT-based intelligent system is cost-effective because it works on low power and stores the data into distributed databases. Further, analyzed or processed overheads by these systems are effortlessly transferred to the cloud, where more devices are connected in a distributed manner, and, thus, computational speed is increased [1, 18].

In the agriculture field, identification and monitoring of plant stresses with the naked eye is very tedious and leads to low-quality, insufficient production. For this reason, the IoT-based intelligent system is introduced to sustainable agriculture to detect or identify the plant stress. The survey of this chapter covers the IoT-based hybrid intelligent techniques which help to identify, detect, or predict the plant stress in agriculture production. The other objective of this survey is to perform a comparative study on different machine learning (ML) methods which helps to observe and analyze different plant stress parameters. The rest of this survey is organized through the discussion of IOT and intelligent system techniques for processing an object. First, the survey concentrates on the IoT structure and its detail layer-based discussion. In Section 1.3, methodology to perform this survey is described. The sub-section consists of the machine learning process for agriculture with a detailed view of each phase of data processing. Section 1.4 depicts the importance of IoT based intelligent system for plant stress detection. Finally, the survey concludes with the future scope.

1.2 Organization of IoT

The Internet of Things is an extremely favourable hybrid technological system in the present world that provides various activities like sensing, monitoring, controlling, actuating, etc [19–21]. It is represented through physical and virtual systems, which are interconnected with the internet. Data are collected in the IoT systems from numerous objects like physical devices and transferred to the server instances through high-speed internet. The virtual system or instances of cloud performs various computational analysis on these data to create appropriate decisions. In IoT, each device either physically or virtually is connected to the system is known as “Things.” Examples of these physical devices are digital cameras, numerous sensors, etc. The virtual system or resources in the cloud service help us with its machine learning services. These services are basically associated with a user application from which the user may get the required information [22–25]. In this study, Figure 1.1 represents a layer structure of IoT for agriculture systems. The layers are perception or sensing layer, data transfer or network layer, and data storage and manipulation layer or application layer. In the perception layer, data are collected from various near-field sources like cameras and sensors or sensor-based devices. Image-based data are also collected or fed into the perception layer like weather station data, historical data, statistical reports, etc. In the network layer, these data are transferred to the database instance of the cloud system via numerous network devices and gateways. In the final layer, all system intelligence techniques are applied, with high computation and decision-making taking place [1, 15, 17, 19, 23].

Figure 1.1 Layered structure of IoT.

• Layer 1: Perception Layer—In the agriculture sector, this layer consists of numerous sensors and cameras. Different types of sensors and cameras are collecting various types of raw data from the agriculture field. Agriculture field sensors are collecting data related to humidity, ...

Table of contents

• Cover
• Half-Title
• Series
• Title
• Copyright
• Dedication
• Contents
• Preface
• Editors
• Contributors
• 1 IoT-Based Intelligent System for Identification of Plant Stress in Sustainable Agriculture
• 2 IoT: A step Towards Sustainability
• 3 Smartphone Crowd Computing: A Rational Approach for Sustainable Computing by Curbing the Environmental Externalities of the Growing Computing Demands
• 4 CFD-Based Flow Analysis around the Multi-Body Segments Optimized Using Rigid Body Fitting Method for Robotic Fish Design
• Index