
Emerging Smart Agricultural Practices Using Artificial Intelligence
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Bring the latest technology to bear in the fight for sustainable agriculture with this timely volume
Artificial intelligence (AI) has the potential to revolutionize virtually every area of research and scientific practice, including agriculture. With AI solutions emerging to drive higher yields, produce increased resource efficiency, and foster sustainability, there is an urgent need for a volume outlining this progress and charting its future course.
Emerging Smart Agricultural Practices Using Artificial Intelligence meets this need with a deep dive into the rapidly developing intersection of agriculture and artificial intelligence. Taking an interdisciplinary approach which applies data science, computer science, and engineering techniques, the book provides cutting-edge insights on the latest advancements in AI-driven agricultural practices. The result is an absolutely critical tool in the ongoing fight to develop sustainable world agriculture.
In addition, this book provides:
- Case studies and real-world applications of new techniques throughout
- Detailed discussion of agricultural applications for AI-driven technologies such as machine learning, computer vision, and data analytics
- A regional approach showcasing international best practices and addressing the varying needs of farmers worldwide
Emerging Smart Agricultural Practices Using Artificial Intelligence is ideal for agricultural professionals and scientists, as well as data scientists, technologists, and agricultural policymakers.
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Persons
Ashish Kumar, PhD, is an Associate Professor with Bennett University, Greater Noida, U.P. India. He has published widely on subjects including object tracking, image processing, artificial intelligence, and medical imaging analysis, and is a member of the IEEE.
Jai Prakash Verma, PhD, is an Associate Professor in the Department of Computer Science and Engineering, Nirma University, Ahmedabad, India. He offers customized training on big data analytics to the Indian Navy, SAC-ISRO scientists in Ahmedabad, and other experts from industry and academia.
Rachna Jain, PhD, is an Associate Professor in the Department of Information Technology Bhagwan Parshuram Institute of Technology. She has 18+ years of academic/research experience with more than 100+ publications in various international conferences and international journals (Scopus/ISI/SCI) of high repute.
Content
About the Editors xvii
List of Contributors xix
Preface xxiii
1 Agricultural Resilience: Water Quality and Human Well-Being 1
Tanu Taneja, B.S Bhatia, and Shalom Akhai
1.1 Introduction 1
1.2 The Nexus of Water Quality and Agriculture 2
1.3 Impact of Contaminated Water on Crop Health 2
1.4 AI-Driven Water Monitoring Systems 4
1.5 Research Gaps and Research Dimensions 6
1.6 Precision Irrigation Techniques 7
1.7 Waterborne Pathogens in Farming 8
1.8 Livestock Health and Water Safety 8
1.9 Sustainable Water Management Strategies 9
1.10 Human Health Implications 11
1.11 Significance of Research in Agricultural Water Quality 12
1.12 Conclusion 12
2 Precision Farming: A Technological Revolution for Sustainable Agriculture 25
Ashish Kumar, Prasoon Kumar Pandey, and Divya Singh
2.1 Introduction 25
2.2 Principles of Precision Farming 27
2.3 Technologies in Precision Farming 28
2.4 Role of Drones in Precision Farming 35
2.5 Benefits of Precision Farming 37
2.6 Conclusion 40
3 Precision Farming and Smart Crop Management 45
Bhavin Patel, Jitendra Bhatia, and Malaram Kumhar
3.1 Introduction 45
3.2 Related Work 46
3.3 Technologies in Precision Farming 47
3.4 Smart Crop Management Techniques 58
3.5 Mapping to Site-Specific Applications 59
3.6 Challenges and Limitations 62
3.7 Conclusion 66
4 Empowering Smart Agriculture with Artificial Intelligence 71
Shipra Raheja and Himanshi Bansal
4.1 Introduction 71
4.2 Benefits of AI in Agriculture 72
4.3 Applications of Artificial Intelligence in Agriculture 74
4.4 Part of AI Within the Farming Data Administration Cycle 76
4.5 Optimizing AI for Farming and Agrarian Forms 78
4.6 AI's Limitations with Regard to Agriculture 79
4.7 Future of AI in Agriculture 81
4.8 The Future Research of AI in Small-Scale Farming 82
5 Foundations of Agricultural AI 87
Divya Singh, Naman Agrawal, Jaya Saini, and Manoranjan Kumar
5.1 Introduction 87
5.2 Machine Learning 91
5.3 Deep Learning 91
5.4 Applications of AI in Agriculture 92
5.5 Challenges and Opportunities 94
5.6 Ethical and Social Implications 96
5.7 Current Trends and Future Directions 98
5.8 Conclusion 100
6 AI in Agriculture: A Comprehensive Exploration of Technological Transformation 105
Manya Gupta, Gargi Mishra, Supriya Bajpai, Abhinav Bhardwaj, and Milind Gautam
6.1 Introduction 105
6.2 AI Integration in Agricultural Practices 108
6.3 AI-Monitored Agricultural Parameters 110
6.4 Application Areas of AI in Agriculture 112
6.5 Limitations 120
6.6 Conclusion and Future Scope 124
7 Integrating AI and Climate-Smart Agricultural Mechanization: Strategies for Enhancing Productivity and Sustainability in a Changing Climate 133
Anil Kumar
7.1 Introduction 133
7.2 Literature Review 138
7.3 Methodology 140
7.4 Analysis 142
7.5 Future Mechanization Pathways Through Climate-Smart Technologies 153
7.6 Discussion 157
7.7 Conclusion 158
8 Harvesting Tomorrow: Exploring Real-World Applications of AI in Agriculture 163
Priya and Neha Gupta
8.1 Introduction 163
8.2 Precision Agriculture: Transforming Farming Practices 165
8.3 Crop Monitoring and Management Techniques 171
8.4 Revolutionizing Livestock Management Through AI 174
8.5 Innovations in Food Supply Chains with AI 179
8.6 Addressing Ethical and Regulatory Considerations 182
8.7 Conclusion 184
8.8 Future Directions 185
9 Smart Agriculture: Predictive Modeling of Fertilizer Requirements Using Neural Networks 189
Heet Dave and Jai Prakash Verma
9.1 Introduction 189
9.2 Related Work 190
9.3 Proposed Research Work 194
9.4 Methodology and Concepts 195
9.5 Implementation and Execution flow 197
9.6 Results 204
9.7 Discussion 206
9.8 Conclusion 206
10 Reviewing Advances in Image-Based Plant Disease Detection 209
Gautmi Tomar, Yuvraj Ahuja, Yogita Arora, and Neera Agarwal
10.1 Introduction 209
10.2 Literature Review 212
10.3 Imaging Techniques of Plant Disease 214
10.4 Critical Discussion 223
10.5 Conclusion 225
11 Leveraging ResNeXt50 and LSTM for Enhanced Plant Disease Detection: A Hybrid Model Proposal 231
Jaspreet Singh and Shashi Tanwar
11.1 Introduction 231
11.2 Literature Review 234
11.3 Research Methodology 236
11.4 A Proposed Hybrid Model Using ResNext50 & LSTM for Plant Disease Detection 240
11.5 Results and Implementation 241
11.6 Conclusion and Future Work 243
12 FarmTechAI: Artificial-Intelligence-Based Modern Farmer Management System 247
Murat Can Cardak, Muhammed Golec, and Sukhpal Singh Gill
12.1 Introduction 247
12.2 Related Works 248
12.3 FarmTechAI: Proposed System 251
12.4 Performance Evaluation and Testing 271
12.5 Legal, Social, Ethical, and Sustainability Issues 277
12.6 Conclusions and Future Work 278
13 Livestock Monitoring and Welfare 283
V. Kanakaris, E. Vrochidou, and G. A. Papakostas
13.1 Introduction 283
13.2 Benefits of Livestock Monitoring 287
13.3 Innovative Livestock Monitoring Technology Methods 288
13.4 Impact of Livestock Monitoring Methods on Welfare 298
13.5 Discussion 300
13.6 Conclusions 302
14 Smart Crop Management: Harnessing Green IoT Tomorrow 315
Shipra Raheja, Vimal Gaur, and Rachna Jain
14.1 Introduction 315
14.2 Greening Agriculture: Advancing with IoT Technology 316
14.3 Green IoT Key Components 318
14.4 Future of AI in Agriculture 324
14.5 Conclusion and Future Aspects 324
15 Current Progress of Sustainable Smart Agriculture Using Internet of Things 329
Savita Kumari Sheoran, Suraj Ranga, and Ghanapriya Singh
15.1 Introduction 329
15.2 Literature Review 331
15.3 Methodology 339
15.4 Current Status of SDGs (Global and Local) in Ranking 343
15.5 Analysis 344
15.6 Conclusions 346
Funding 346
References 346
Index 353
1
Agricultural Resilience: Water Quality and Human Well-Being
Tanu Taneja1, B.S Bhatia2, and Shalom Akhai3
1 Research Scholar, Department of Civil Engineering, RIMT University, FatehgarhSahib, Punjab, India
2 Pro Vice Chancellor, Management Department, RIMT University, FatehgarhSahib, Punjab, India
3 Professor, Department of Mechanical Engineering, Maharishi Markandeshwar (Deemed to be University) Mullana, Ambala, Haryana, India
1.1 Introduction
The water consumed by humans directly or in the form of food products harvested or prepared using water directly affects human health, depending on the quality of the water being used [1]. When the water used for agriculture is contaminated/polluted, it reduces crop yields and lowers the nutritional value due to unacceptable levels of pollutants such as heavy metals, herbicides, and pathogens [2]. This long-term contamination in soil due to harmful elements that may come into it by polluted water harms ecosystems [3]. When using sensor-based and artificial intelligence (AI)-driven water monitoring precision devices, farmers have access to real-time irrigation water quality data, which aids to address and prevent harmful contamination levels of pollutants [4]. Farmers can also save water by avoiding over-irrigation based on data analysis of the data collected using sensors, drones, and AI-based systems [5]. It is also useful for disease prevention by identification of waterborne pathogens at early stages [6]. Good water quality is required not only by humans and food crops but also by livestock for consumption [7]. Water safety in animal health is crucial for agricultural resilience because livestock health affects sustainable and profitable farming [8]. Agricultural resilience requires water management that goes beyond conservation, efficiency, and ecological harmony [9]. Rainwater harvesting, efficient irrigation, and agroecology reduce farmers' dependency on conventional water sources and optimize water application and resource utilization [10]. Heavy metals, pesticides, and aquatic illnesses may enter the food chain from unclean water-irrigated crops [11]. Community-protecting programs need to understand how water quality affects health [12]. Sustainable and cost-effective water filtration techniques solve agricultural water quality challenges by filtering and cleansing water naturally [13]. Farmers may improve water quality, agricultural profitability, and environmental and community well-being by using AI, precision irrigation, and sustainable water management [14].
1.2 The Nexus of Water Quality and Agriculture
Crop growth, production, and nutrition depend on water quality. Due to its complex chemical and biological makeup, its relationship to agriculture is elaborated [15]. Pollination, flower loss, and yield are affected by contaminated water [16]. Lack of moisture delays seed germination, whereas enough water stimulates flowering and fruiting for a rich harvest. Water quality is important during irrigation because pollutants may affect soil structure and plant health, reducing agricultural yield. Water quality affects nutrition because plant roots absorb nutrients and contaminants [17]. Polluted water harms soil fertility's diverse microbial communities by spreading illnesses and poisons. Diseases, economic hardship, and agricultural instability may result from contaminants [18]. Technology like AI-driven water monitoring systems, precision agriculture, and waterborne disease diagnosis in farming are improving water quality and agriculture [20]. Farmers may decrease over-irrigation, pollutant leaching, and pollution using these technologies [20]. Water quality agriculture management also involves waterborne pathogen identification, which helps farmers identify harmful bacteria [21]. Ethical, animal welfare, and agricultural profitability depend on livestock water safety [22]. Sustainable water management technologies help farmers gather and store rain, control water application, and optimize resource usage. Water is purified chemical-free by phytoremediation and wetland filtration [23]. Table 1.1 briefs the impact of water quality on agriculture.
In conclusion, water quality and soil health are interrelated, and innovative water filtration technology can help address these issues.
1.3 Impact of Contaminated Water on Crop Health
Water is crucial for crop health, transporting nutrients needed for development [31]. However, when polluted with heavy metals, pesticides, and viruses, it becomes a silent enemy, stunting growth, reducing yields, and lowering nutrition [33]. Heavy metals like lead, cadmium, and mercury disrupt plant physiological systems, impairing nutrition uptake and transport, leading to stunted development [34, 35]. Pesticides, used in agriculture to control pests, can also pollute water, causing stunted growth, leaf discoloration, and lower photosynthetic efficiency [36]. They can also build up in the soil, threatening agricultural land [37]. Waterborne pathogens, including bacteria, viruses, and fungus, can enter plants, producing wilting, lesions, and rotting, causing farmers significant economic losses [38]. Water contamination impacts crops via root absorption since soil pollutants circulate in their vascular systems and inhibit nutrient synthesis. This stunts growth, lowers yields, and may impact farmers, downstream consumers, and agriculture-dependent firms [39]. Heavy metals and pesticides in contaminated water limit nutrient absorption and plant synthesis, lowering agricultural nutrition and possibly affecting human health [2, 11]. Soil contaminants worsen the issue, diminishing the land's ability to support many crop cycles. Water quality's various impacts must be understood to build resilient agricultural systems [33]. Monitoring water sources and soil quality, precision irrigation utilizing real-time data, and investments in water treatment equipment, including filtration and purification systems, may prevent agricultural contamination [40, 41]. Table 1.2 summarizes contaminants and their effects.
Table 1.1 Impact of water quality on agriculture.
Aspect of agriculture Effects of poor water quality Effects of good water quality Research needs Crop development and productivity [24-26]- Delays or prevents seed germination. Affects pollination, leading to flower loss and reduced yield
- Contaminants can damage plant health, leading to lower yield
- Promotes seed germination and healthy growth
- Encourages pollination and blooming, resulting in higher yields
- Supports strong plant development, leading to healthy and abundant crops
- Develop cost-effective methods for on-farm water quality testing
- Research on improving seed tolerance to contaminants
- Develop crop varieties resilient to poor water quality
- Plants absorb pollutants alongside minerals, affecting the nutritional value of crops
- Contaminated water can introduce diseases into crops, impacting food safety
- Plants absorb essential minerals, contributing to the nutritional value of crops
- Promotes healthy crop growth, leading to safe and nutritious food
- Develop strategies to reduce contaminant uptake by plants
- Research on biofortification techniques to enhance crop nutrient content
- Contaminated water damages soil structure, hindering water drainage and root growth
- Disrupts the delicate balance of beneficial microbes, affecting soil fertility
- Supports healthy soil structure, allowing for proper water drainage and root growth
- Creates an environment for diverse microbial populations, contributing to soil fertility
- Research on soil remediation techniques to mitigate the effects of contaminants
- Develop methods to improve soil organic matter content for better water retention
- Can lead to crop loss, requiring replanting and increasing economic burden
- May contribute to the spread of diseases, impacting marketability and causing financial losses
- Supports healthy crop growth, reducing the need for replanting and associated costs
- Contributes to higher yields and improved crop quality, leading to increased profitability
- Develop economic models to assess the financial impact of water quality on agriculture
- Research on cost-effective water treatment solutions for farms
Polluted water slows crop growth, lowers yields, and reduces crop nutrition and land sustainability. To enhance agricultural systems and feed rising populations, comprehensive agricultural...
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