Contributors |
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SECTION 1 IoT and edge foundations and framework |
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Chapter 1 Internet of things (IoT) and data analytics in smart agriculture: Benefits and challenges |
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3 | (14) |
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3 | (2) |
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4 | (1) |
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2 IoT ecosystem in agriculture |
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5 | (2) |
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2.1 Management techniques/systems (IoT and big data) |
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5 | (1) |
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2.2 Smart information systems (SIS) in agriculture |
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6 | (1) |
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3 Benefits of IoT in agriculture |
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7 | (3) |
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3.1 Remote sensing as a major tool in agriculture |
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7 | (1) |
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3.2 Weather forecasting as a prime IoT in agriculture |
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8 | (1) |
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8 | (1) |
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3.6 Greenhouse monitoring and automation system |
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9 | (1) |
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4 Open issues and key challenges in the adoption of IoT in agriculture |
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10 | (2) |
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10 | (1) |
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4.2 Data privacy protection and issues of ownership |
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10 | (1) |
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4.3 Autonomy foreseeability and causation |
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11 | (1) |
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11 | (1) |
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4.5 Opaque research and development |
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12 | (1) |
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5 Legal issues in regulating AI in agriculture |
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12 | (2) |
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12 | (1) |
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13 | (1) |
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5.3 Law relating to accidents, health, and safety |
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13 | (1) |
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5.4 Accidents and negligence |
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13 | (1) |
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13 | (1) |
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14 | (3) |
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14 | (3) |
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Chapter 2 Edge computing-Foundations and applications |
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17 | (14) |
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17 | (1) |
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3 Applications of edge computing |
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22 | (4) |
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3.1 Future trends of edge computing |
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26 | (1) |
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26 | (5) |
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28 | (3) |
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Chapter 3 IoT-based fuzzy logic-controlled novel and multilingual mobile application for hydroponic farming |
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31 | (12) |
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31 | (1) |
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32 | (1) |
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33 | (1) |
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34 | (5) |
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39 | (2) |
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41 | (2) |
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41 | (2) |
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Chapter 4 Functional framework for loT-based agricultural system |
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43 | (28) |
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43 | (13) |
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1.1 Overview of the cases |
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44 | (1) |
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1.2 Challenges, opportunities, and use of IoT applications in agriculture |
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45 | (2) |
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1.3 Opportunities allied with the solicitation of IoT in agriculture |
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47 | (1) |
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1.4 The architecture of a smart farm monitoring system |
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48 | (4) |
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1.5 Energy-saving technologies |
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52 | (1) |
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52 | (1) |
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1.7 Advantages of IoT in agriculture system |
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53 | (1) |
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53 | (1) |
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54 | (1) |
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54 | (1) |
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55 | (1) |
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1.12 Limitations of the existing proposed model |
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56 | (1) |
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56 | (3) |
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2.1 Block diagram of proposed model |
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56 | (1) |
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2.2 Flow diagram of controlling process of motor using sensors |
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57 | (1) |
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2.3 IoT with transmitter and receiver wireless sensor model |
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58 | (1) |
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3 Experimental results and discussion |
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59 | (3) |
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59 | (2) |
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3.2 Thingspeak cloud server |
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61 | (1) |
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62 | (3) |
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4.1 Measurements at 14:30 when soil is dry |
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62 | (1) |
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4.2 Measurements on May 14, 2020; time varies when soil is wet |
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63 | (1) |
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4.3 Measurement in night, when soil is dry |
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64 | (1) |
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4.4 Measurement in night, when soil is wet |
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64 | (1) |
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65 | (1) |
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6 Conclusion and future scope |
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66 | (5) |
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66 | (1) |
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66 | (5) |
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Chapter 5 Functional framework for edge-based agricultural system |
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71 | (30) |
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71 | (2) |
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73 | (1) |
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3 Edge computing in agricultural sectors |
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73 | (5) |
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3.1 Role of edge computing in multiple facets of agriculture |
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76 | (2) |
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4 Edge computing framework design in agriculture |
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78 | (7) |
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80 | (1) |
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4.2 Processing/computation |
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81 | (1) |
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82 | (1) |
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83 | (1) |
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84 | (1) |
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84 | (1) |
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5 Edge computing implementation |
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85 | (9) |
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5.1 Hardware implementation |
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87 | (1) |
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5.2 Data communication technologies |
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88 | (1) |
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5.3 Data processing implementation |
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89 | (3) |
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5.4 Experimental set-up of edge-based agricultural system |
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92 | (2) |
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94 | (7) |
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96 | (5) |
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Chapter 6 Precision agriculture: Weather forecasting for future farming |
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101 | (24) |
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Kingsley Eghonghon Ukhurebor |
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Charles Oluwaseun Adetunji |
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101 | (5) |
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1.1 Terminologies employed in precision agriculture |
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103 | (2) |
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1.2 Connection between precision agriculture and traditional agriculture |
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105 | (1) |
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106 | (3) |
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107 | (1) |
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108 | (1) |
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3 Agricultural implications of climate change |
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109 | (4) |
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3.1 Reducing the burden of agriculture on climate change |
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110 | (1) |
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3.2 Exploring the climate change influence as an influential element in agricultural productivity |
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111 | (2) |
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4 Modern tools and techniques for precision agriculture |
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113 | (3) |
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4.1 Internet of Things (IoT) |
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114 | (1) |
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114 | (1) |
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4.3 Unmanned aerial vehicles (UAVs) |
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114 | (1) |
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4.4 Unmanned ground vehicles (UGVs) |
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115 | (1) |
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115 | (1) |
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115 | (1) |
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4.7 Autoguidance equipment (AGE) |
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115 | (1) |
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4.8 Variable rate technology |
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116 | (1) |
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116 | (1) |
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116 | (9) |
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117 | (8) |
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SECTION 2 IoT use cases in smart farming and smart agriculture |
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Chapter 7 Crop management system using IoT |
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125 | (18) |
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125 | (2) |
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2 Background and related works |
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127 | (2) |
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129 | (3) |
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132 | (5) |
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137 | (1) |
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6 Future research direction |
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137 | (2) |
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139 | (4) |
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140 | (3) |
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Chapter 8 Smart irrigation and crop security in agriculture using IoT |
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143 | (14) |
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143 | (2) |
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144 | (1) |
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144 | (1) |
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145 | (1) |
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145 | (1) |
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145 | (4) |
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2.1 Basic building blocks of an IoT device |
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146 | (3) |
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149 | (2) |
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150 | (1) |
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151 | (3) |
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152 | (2) |
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154 | (1) |
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6 Conclusion and future scope |
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154 | (3) |
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155 | (2) |
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Chapter 9 The Internet of Things in agriculture for sustainable rural development |
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157 | (14) |
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157 | (3) |
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158 | (1) |
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158 | (2) |
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160 | (4) |
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160 | (2) |
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2.2 IoT in agriculture for rural development |
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162 | (2) |
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3 IoT in agriculture: Use cases |
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164 | (1) |
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4 Case studies: IoT-based agriculture for sustainable rural development |
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165 | (1) |
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4.1 Slashing water consumption in avocado |
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165 | (1) |
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166 | (1) |
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5 Impact of IoT on food sustainability and socioeconomic uplift |
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166 | (1) |
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166 | (1) |
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167 | (1) |
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6 Challenges and opportunities |
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167 | (1) |
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6.1 Unstable Internet connection in farms |
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167 | (1) |
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6.2 Disrupted connectivity to cloud servers |
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168 | (1) |
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168 | (1) |
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168 | (3) |
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169 | (2) |
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Chapter 10 Internet of Things (IoT) in agriculture toward urban greening |
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171 | (12) |
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171 | (1) |
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172 | (2) |
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172 | (1) |
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173 | (1) |
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173 | (1) |
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174 | (1) |
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174 | (1) |
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3.2 Green sensing networks |
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175 | (1) |
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3.3 Green Internet technologies |
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175 | (1) |
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175 | (4) |
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4.1 Smart industrial plants and machine-to-machine communications |
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177 | (1) |
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4.2 Smart plant monitoring |
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177 | (1) |
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4.3 Smart data collection |
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177 | (1) |
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178 | (1) |
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178 | (1) |
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4.6 Smart social networks |
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178 | (1) |
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178 | (1) |
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178 | (1) |
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179 | (1) |
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179 | (1) |
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5 G-IOT challenges and opportunities |
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179 | (1) |
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179 | (1) |
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5.2 Green spectrum management |
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179 | (1) |
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180 | (1) |
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5.4 Green security and management |
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180 | (1) |
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180 | (3) |
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181 | (2) |
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Chapter 11 Smart e-agriculture monitoring systems |
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183 | (22) |
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183 | (1) |
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2 Need for smart e-monitoring system for agriculture |
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184 | (1) |
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184 | (6) |
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3.1 WSN-based architecture |
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185 | (1) |
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3.2 IoT-Cloud based architecture |
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186 | (4) |
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4 IoT and data analytics in agriculture |
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190 | (3) |
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191 | (1) |
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191 | (1) |
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191 | (1) |
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191 | (2) |
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5 Different types of solutions available |
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193 | (2) |
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193 | (1) |
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193 | (1) |
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194 | (1) |
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194 | (1) |
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5.5 Automated hydroponics: Bitponics |
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194 | (1) |
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194 | (1) |
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195 | (1) |
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195 | (1) |
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7 Case study on IoT-based monitoring systems |
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196 | (3) |
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7.1 Case study 1: IoT-based greenhouse crop production |
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196 | (1) |
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7.2 Case study 2: IoT-based plant disease prediction |
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197 | (1) |
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7.3 Case study 3: IoT-based vineyard monitoring |
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198 | (1) |
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7.4 Case study 4: IoT-based irrigation management |
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198 | (1) |
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199 | (1) |
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199 | (6) |
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200 | (5) |
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Chapter 12 Smart agriculture using renewable energy and AI-powered IoT |
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205 | (22) |
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205 | (1) |
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2 Background and related work |
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206 | (3) |
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206 | (1) |
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2.2 Data analytics platform |
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207 | (2) |
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209 | (1) |
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209 | (3) |
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210 | (1) |
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211 | (1) |
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4 Architecture and system design |
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212 | (8) |
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4.1 Components of the system |
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212 | (5) |
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217 | (3) |
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220 | (1) |
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220 | (1) |
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221 | (1) |
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222 | (1) |
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222 | (1) |
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222 | (1) |
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222 | (1) |
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7.4 Efficiency of crop growth |
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223 | (1) |
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7.5 Holistic supply chain management |
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223 | (1) |
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223 | (4) |
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8.1 Structural implementation |
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223 | (1) |
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223 | (1) |
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223 | (1) |
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224 | (1) |
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8.5 Maintenance requirement |
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224 | (1) |
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224 | (3) |
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Chapter 13 Smart irrigation-based behavioral study of Moringa plant for growth monitoring in subtropical desert climatic condition |
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227 | (14) |
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227 | (1) |
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2 Moringa oleifera as a miracle plant |
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228 | (3) |
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2.1 Properties of Moringa oleifera |
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229 | (1) |
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2.2 Medicinal value and health benefits |
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229 | (2) |
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3 Favorable climatic condition |
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231 | (1) |
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3.1 Growth pattern in arid and semiarid areas |
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231 | (1) |
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3.2 Challenges in subtropical climatic conditions |
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231 | (1) |
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4 Motivation and challenges |
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231 | (1) |
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4.1 Plant and smart technology |
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232 | (1) |
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232 | (2) |
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232 | (1) |
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232 | (1) |
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233 | (1) |
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233 | (1) |
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234 | (2) |
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236 | (1) |
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8 Limitations and area of improvement |
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237 | (1) |
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237 | (4) |
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237 | (4) |
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Chapter 14 Surveying smart farming for smart cities |
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241 | (24) |
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Jose Alberto Hernandez-Aguilar |
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241 | (2) |
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243 | (3) |
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3 Smart farming and future trends |
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246 | (11) |
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257 | (8) |
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258 | (7) |
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SECTION 3 Edge computing use cases in smart farming and smart agriculture |
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Chapter 15 Farm automation |
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265 | (22) |
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265 | (2) |
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2 Current trends in smart farming automation systems |
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267 | (3) |
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3 Architecture of edge computing and IoT (E-IoT) platform |
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270 | (5) |
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271 | (1) |
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3.2 Edge computing reference architecture 2.0 |
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271 | (1) |
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3.3 Industrial Internet Consortium reference architecture |
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272 | (1) |
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3.4 INTELSAP reference architecture |
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273 | (1) |
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3.5 Global edge computing reference architecture |
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274 | (1) |
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4 Applications of E-IoT in farm automation |
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275 | (7) |
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4.1 E-IoT in weed detection |
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275 | (2) |
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4.2 Smart irrigation system with E-IoT |
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277 | (1) |
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4.3 E-IoT in livestock management |
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278 | (1) |
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4.4 Farm security solution with E-IoT |
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279 | (2) |
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281 | (1) |
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282 | (1) |
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282 | (1) |
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282 | (5) |
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283 | (4) |
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Chapter 16 A fog computing-based IoT framework for prediction of crop disease using big data analytics |
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287 | (14) |
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287 | (4) |
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287 | (1) |
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288 | (1) |
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1.3 Smart crop disease prediction |
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288 | (3) |
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1.4 The role of fog computing in IoT |
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291 | (1) |
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2 IoT-fog integration in crop disease prediction |
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291 | (6) |
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292 | (2) |
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2.2 IoT agricultural framework |
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294 | (1) |
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2.3 A fog computing-based IOT framework for predicting crop disease |
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295 | (2) |
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297 | (1) |
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3.1 Information needed to predict disease accurately |
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297 | (1) |
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3.2 Map-reduce based prediction model |
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298 | (1) |
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4 Conclusion and future work |
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298 | (3) |
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300 | (1) |
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Chapter 17 Agribots: A gateway to the next revolution in agriculture |
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301 | (14) |
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Charles Oluwaseun Adetunji |
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301 | (1) |
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2 Specific examples of how agribots could be integrated into a regional IoT-enabled single window for improving collective subsistence agricultural production in rural communities |
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302 | (6) |
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308 | (7) |
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309 | (6) |
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SECTION 4 Sensor network use cases in smart farming and smart agriculture |
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Chapter 18 SAW: A real-time surveillance system at an agricultural warehouse using IoT |
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315 | (14) |
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315 | (1) |
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2 Issues and challenges with a traditional monitoring system in agriculture |
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316 | (1) |
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3 The possibilities of IoT as an alternate to conventional agriculture |
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316 | (1) |
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4 Components used with specifications and applications for IoT-enabled agriculture system |
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317 | (5) |
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4.1 Temperature sensor and humidity sensor |
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317 | (1) |
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4.2 Flame sensor and smoke sensor |
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318 | (1) |
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318 | (1) |
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4.4 NRF24L01 transceiver module |
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319 | (1) |
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4.5 GSM communication module |
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319 | (2) |
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321 | (1) |
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322 | (1) |
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5 IoT-enabled autonomous agriculture model (SAW) |
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322 | (4) |
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323 | (3) |
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326 | (3) |
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326 | (3) |
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Chapter 19 The predictive model to maintain pH levels in hydroponic systems |
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329 | (16) |
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329 | (1) |
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2 Hydroponics system discussion |
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330 | (3) |
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330 | (1) |
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331 | (2) |
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333 | (1) |
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334 | (1) |
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334 | (1) |
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334 | (1) |
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7 Ph management automation |
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335 | (3) |
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336 | (1) |
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337 | (1) |
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338 | (1) |
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338 | (2) |
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339 | (1) |
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10 Hydroponics automation |
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340 | (2) |
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340 | (2) |
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10.2 Major advantages of hydroponics |
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342 | (1) |
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10.3 Major disadvantages of hydroponics |
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342 | (1) |
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11 Conclusion and future scope |
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342 | (3) |
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343 | (2) |
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Chapter 20 A crop-monitoring system using wireless sensor networking |
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345 | (16) |
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345 | (1) |
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2 Background and related works |
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346 | (3) |
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349 | (4) |
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3.1 NodeMCU (node microcontroller unit) |
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350 | (1) |
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3.2 Passive infrared sensor (PIR sensor) |
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351 | (1) |
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3.3 Temperature and humidity sensor |
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351 | (1) |
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351 | (1) |
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352 | (1) |
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352 | (1) |
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352 | (1) |
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353 | (2) |
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355 | (2) |
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6 Future research direction |
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357 | (1) |
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358 | (3) |
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359 | (2) |
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Chapter 21 Integration of RFID and sensors in agriculture using IOT |
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361 | (14) |
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361 | (2) |
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2 Background and related works |
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363 | (2) |
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3 System design and architecture |
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365 | (3) |
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368 | (2) |
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5 Future research direction |
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370 | (1) |
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370 | (5) |
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371 | (4) |
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SECTION 5 AI and data analytics in agriculture |
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Chapter 22 Prediction of crop yield and pest-disease infestation |
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375 | (20) |
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375 | (1) |
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2 Crop yield forecasting models |
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376 | (9) |
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376 | (6) |
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2.2 Bayesian forecasting approach |
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382 | (1) |
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2.3 Weather-based crop yield forecasting models |
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382 | (3) |
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3 Pest and disease forewarning systems |
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385 | (4) |
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385 | (3) |
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388 | (1) |
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3.3 Loss of crop yield due to pest and disease outbreak |
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388 | (1) |
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389 | (6) |
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389 | (6) |
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Chapter 23 Machine learning-based remote monitoring and predictive analytics system for crop and livestock |
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395 | (14) |
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395 | (1) |
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396 | (4) |
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2.1 Framework for remote monitoring and predictive analysis using ML |
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397 | (1) |
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397 | (2) |
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399 | (1) |
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2.4 Role of AI and machine learning for crop monitoring |
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400 | (1) |
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400 | (3) |
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400 | (1) |
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401 | (1) |
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402 | (1) |
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402 | (1) |
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403 | (2) |
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405 | (4) |
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405 | (4) |
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Chapter 24 Exploring performance and predictive analytics of agriculture data |
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409 | (28) |
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409 | (1) |
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410 | (1) |
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3 The need for data processing |
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410 | (2) |
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4 Big data characteristics |
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412 | (2) |
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5 Techniques and tools for big data processing |
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414 | (2) |
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6 Advantages of data analysis in agriculture |
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416 | (1) |
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7 Classical approach of farming process |
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417 | (1) |
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417 | (1) |
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9 Advantages of smart farming |
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418 | (1) |
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10 Analysis of usefulness of various smart farming techniques |
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418 | (1) |
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11 Agricultural big data mining |
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419 | (1) |
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12 Study of agricultural sector using mobile apps |
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419 | (1) |
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13 Study of vegetable production using hydroponics |
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420 | (1) |
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14 Comparative approach of implementation mechanisms |
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420 | (1) |
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15 Literature survey on algorithms |
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421 | (1) |
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16 Comparative approach of the various techniques |
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421 | (2) |
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422 | (1) |
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423 | (1) |
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17.1 Crop production dataset overview |
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423 | (1) |
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17.2 Technique used in data mining |
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423 | (1) |
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423 | (4) |
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18.1 Fertilizers datasets overview |
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425 | (2) |
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427 | (4) |
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20 Concerns and conclusions |
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431 | (6) |
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434 | (2) |
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436 | (1) |
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Chapter 25 Climate condition monitoring and automated systems |
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437 | (12) |
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Kingsley Eghonghon Ukhurebor |
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Charles Oluwaseun Adetunji |
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437 | (1) |
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438 | (5) |
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2.1 Climate impacts on environment |
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439 | (1) |
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2.2 Climate impacts on health |
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440 | (1) |
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2.3 Climate impacts on agriculture |
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440 | (3) |
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3 Climate monitoring systems and automation |
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443 | (1) |
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4 Recent developments on applications of climate monitoring systems in environment, health, and agriculture |
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443 | (2) |
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5 Conclusion and future work |
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445 | (4) |
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445 | (4) |
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Chapter 26 Decision-making system for crop selection based on soil |
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449 | (28) |
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449 | (2) |
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2 Machine learning role in agriculture: A review |
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451 | (1) |
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3 Soil health and crop production |
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452 | (7) |
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4 Experiment and analysis |
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459 | (9) |
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459 | (9) |
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468 | (3) |
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5.1 Prediction algorithm implementation and performance outcomes |
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469 | (1) |
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5.2 Prediction comparative analysis |
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469 | (2) |
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6 Crop selection and soil health recommendation system |
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471 | (1) |
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7 Conclusion and challenges |
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472 | (5) |
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473 | (4) |
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Chapter 27 Cyberespionage: Socioeconomic implications on sustainable food security |
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477 | (10) |
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Charles Oluwaseun Adetunji |
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Kingsley Eghonghon Ukhurebor |
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477 | (7) |
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484 | (3) |
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485 | (2) |
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Chapter 28 Internet of Things on sustainable aquaculture system |
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487 | (18) |
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Ricardo A. Barrera-Camara |
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Francisco R. Trejo-Macotela |
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487 | (3) |
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2 Internet of Farming Things |
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490 | (3) |
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3 Internet of Things on sustainable aquaculture system |
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493 | (8) |
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501 | (4) |
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501 | (4) |
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Chapter 29 IoT-based monitoring system for freshwater fish farming: Analysis and design |
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505 | (12) |
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Charles Oluwaseun Adetunji |
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505 | (1) |
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2 Relevance of monitoring devices in freshwater fish farming (including IoT and smart monitoring systems) |
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506 | (3) |
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3 IoT-based monitoring production: Feasibility, requirement planning, analysis, and design |
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509 | (2) |
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4 Building IoT infrastructure for monitoring production: Feasibility, requirement planning, analysis, and design |
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511 | (1) |
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5 Conclusions and recommendations |
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512 | (5) |
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513 | (4) |
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Chapter 30 Transforming IoT in aquaculture: A cloud solution |
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517 | (16) |
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517 | (2) |
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519 | (1) |
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519 | (1) |
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4 Benefits of cloud platform in IoT |
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520 | (1) |
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5 Integration of cloud computing and IoT |
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520 | (2) |
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521 | (1) |
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522 | (1) |
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7 Cloud-based IoT monitoring aquaculture system |
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523 | (1) |
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524 | (1) |
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9 Cloud-IoT architecture in shrimp aquaculture |
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524 | (2) |
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10 Introduction of wireless sensor networks (WSN) |
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526 | (3) |
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10.1 WSN architecture for aquaculture |
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526 | (1) |
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10.2 Monitoring of water quality with the help of WSN |
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527 | (1) |
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528 | (1) |
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11 Future trends and conclusion |
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529 | (4) |
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530 | (3) |
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Chapter 31 Toward the design of an intelligent system for enhancing salt water shrimp production using fuzzy logic |
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533 | (10) |
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Charles Oluwaseun Adetunji |
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533 | (1) |
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2 Specific examples of intelligent systems for enhancing salt water shrimp production using fuzzy logic |
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534 | (5) |
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539 | (4) |
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540 | (3) |
Index |
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543 | |