Emerging Smart Agricultural Practices Using Artificial Intelligence E-Book
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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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Veröffentlichungsjahr: 2025
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Table of Contents
Cover
Table of Contents
Series Page
Title Page
Copyright Page
About the Editors
List of Contributors
Preface
1 Agricultural Resilience: Water Quality and Human Well‐Being
1.1 Introduction
1.2 The Nexus of Water Quality and Agriculture
1.3 Impact of Contaminated Water on Crop Health
1.4 AI‐Driven Water Monitoring Systems
1.5 Research Gaps and Research Dimensions
1.6 Precision Irrigation Techniques
1.7 Waterborne Pathogens in Farming
1.8 Livestock Health and Water Safety
1.9 Sustainable Water Management Strategies
1.10 Human Health Implications
1.11 Significance of Research in Agricultural Water Quality
1.12 Conclusion
References
2 Precision Farming: A Technological Revolution for Sustainable Agriculture
2.1 Introduction
2.2 Principles of Precision Farming
2.3 Technologies in Precision Farming
2.4 Role of Drones in Precision Farming
2.5 Benefits of Precision Farming
2.6 Conclusion
References
3 Precision Farming and Smart Crop Management
3.1 Introduction
3.2 Related Work
3.3 Technologies in Precision Farming
3.4 Smart Crop Management Techniques
3.5 Mapping to Site‐Specific Applications
3.6 Challenges and Limitations
3.7 Conclusion
References
4 Empowering Smart Agriculture with Artificial Intelligence
4.1 Introduction
4.2 Benefits of AI in Agriculture
4.3 Applications of Artificial Intelligence in Agriculture
4.4 Part of AI Within the Farming Data Administration Cycle
4.5 Optimizing AI for Farming and Agrarian Forms
4.6 AI’s Limitations with Regard to Agriculture
4.7 Future of AI in Agriculture
4.8 The Future Research of AI in Small‐Scale Farming
References
5 Foundations of Agricultural AI
5.1 Introduction
5.2 Machine Learning
5.3 Deep Learning
5.4 Applications of AI in Agriculture
5.5 Challenges and Opportunities
5.6 Ethical and Social Implications
5.7 Current Trends and Future Directions
5.8 Conclusion
References
6 AI in Agriculture: A Comprehensive Exploration of Technological Transformation
6.1 Introduction
6.2 AI Integration in Agricultural Practices
6.3 AI‐Monitored Agricultural Parameters
6.4 Application Areas of AI in Agriculture
6.5 Limitations
6.6 Conclusion and Future Scope
References
7 Integrating AI and Climate‐Smart Agricultural Mechanization: Strategies for Enhancing Productivity and Sustainability in a Changing Climate
7.1 Introduction
7.2 Literature Review
7.3 Methodology
7.4 Analysis
7.5 Future Mechanization Pathways Through Climate‐Smart Technologies
7.6 Discussion
7.7 Conclusion
References
8 Harvesting Tomorrow: Exploring Real‐World Applications of AI in Agriculture
8.1 Introduction
8.2 Precision Agriculture: Transforming Farming Practices
8.3 Crop Monitoring and Management Techniques
8.4 Revolutionizing Livestock Management Through AI
8.5 Innovations in Food Supply Chains with AI
8.6 Addressing Ethical and Regulatory Considerations
8.7 Conclusion
8.8 Future Directions
References
9 Smart Agriculture: Predictive Modeling of Fertilizer Requirements Using Neural Networks
9.1 Introduction
9.2 Related Work
9.3 Proposed Research Work
9.4 Methodology and Concepts
9.5 Implementation and Execution flow
9.6 Results
9.7 Discussion
9.8 Conclusion
References
10 Reviewing Advances in Image‐Based Plant Disease Detection
10.1 Introduction
10.2 Literature Review
10.3 Imaging Techniques of Plant Disease
10.4 Critical Discussion
10.5 Conclusion
References
11 Leveraging ResNeXt50 and LSTM for Enhanced Plant Disease Detection: A Hybrid Model Proposal
11.1 Introduction
11.2 Literature Review
11.3 Research Methodology
11.4 A Proposed Hybrid Model Using ResNext50 & LSTM for Plant Disease Detection
11.5 Results and Implementation
11.6 Conclusion and Future Work
References
12 FarmTechAI: Artificial Intelligence‐Based Modern Farmer Management System
12.1 Introduction
12.2 Related Works
12.3 FarmTechAI: Proposed System
12.4 Performance Evaluation and Testing
12.5 Legal, Social, Ethical, and Sustainability Issues
12.6 Conclusions and Future Work
Acknowledgments
References
13 Livestock Monitoring and Welfare
13.1 Introduction
13.2 Benefits of Livestock Monitoring
13.3 Innovative Livestock Monitoring Technology Methods
13.4 Impact of Livestock Monitoring Methods on Welfare
13.5 Discussion
13.6 Conclusions
References
14 Smart Crop Management: Harnessing Green IoT Tomorrow
14.1 Introduction
14.2 Greening Agriculture: Advancing with IoT Technology
14.3 Green IoT Key Components
14.4 Future of AI in Agriculture
14.5 Conclusion and Future Aspects
References
15 Current Progress of Sustainable Smart Agriculture Using Internet of Things
15.1 Introduction
15.2 Literature Review
15.3 Methodology
15.4 Current Status of SDGs (Global and Local) in Ranking
15.5 Analysis
15.6 Conclusions
Funding
References
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Impact of water quality on agriculture.
Table 1.2 Impacts of pollutants on crop health and yield.
Table 1.3 Benefits of AI‐powered water monitoring in agriculture.
Table 1.4 Sustainable water conservation practices in agriculture.
Chapter 2
Table 2.1 Salient features of recent techniques for disease prediction mode...
Chapter 3
Table 3.1 Summary of parameters and recent technologies covered in the lite...
Table 3.2 Analysis of various papers in the domain of precision farming bas...
Table 3.3 Summary of key enabling technologies integrated in precision farm...
Chapter 6
Table 6.1 Data collection indicator.
Table 6.2 Top institutions encouraging research in this field.
Table 6.3 Priority areas for application of research in AI and agriculture....
Table 6.4 AI in soil management.
Table 6.5 AI in crop management.
Table 6.6 AI in disease management.
Table 6.7 AI in weed management.
Table 6.8 Applications of AI in agriculture.
Table 6.9 Open‐source datasets.
Chapter 10
Table 10.1 A collection on plant disease segmentation techniques.
Table 10.2 Previous review versus proposed review (McCarthy, Hancock, & Rain...
Chapter 11
Table 11.1 Literature review table.
Chapter 12
Table 12.1 Comparison of the existing farming systems.
Table 12.2 Software requirements for the system.
Table 12.3 Functional requirements for the system.
Table 12.4 Nonfunctional requirements for the system.
Table 12.5 Table for user acceptance testing.
Table 12.6 Browser compatibility testing.
Chapter 13
Table 13.1 Indicative publications including innovative technologies in liv...
Table 13.2 Livestock monitoring methods that enhance the overall welfare.
Chapter 15
Table 15.1 List of articles studies with highlights on SDGs and focus.
Table 15.2 List of SDGs reports studies on published year and focus.
Table 15.3 List of analysis.
List of Illustrations
Chapter 1
Figure 1.1 Impacts of water pollution on human health and environment.
Chapter 2
Figure 2.1 Basic procedure steps for data analysis and monitoring in precisi...
Figure 2.2 Technology‐based disease prediction and monitoring in precision f...
Figure 2.3 Benefits of robots in precision farming.
Chapter 3
Figure 3.1 Possible technologies and its use cases for precision farming.
Figure 3.2 Application perspective of smart agriculture.
Figure 3.3 Challenges and limitations with precision agriculture and smart c...
Chapter 4
Figure 4.1 Benefits of artificial intelligence in agriculture.
Figure 4.2 Applications of artificial intelligence in agriculture.
Figure 4.3 Use of sensors and intelligent automation for plant growth.
Chapter 5
Figure 5.1 Growth of technology for the agriculture sector.
Figure 5.2 Uses of modern technology in agriculture.
Chapter 6
Figure 6.1 Implementation of AI techniques in different phases of agricultur...
Figure 6.2 Figure showing (a) Process of AI implementation in agriculture (b...
Figure 6.3 Top SCOPUS indexed journals publishing research on AI in agricult...
Chapter 7
Figure 7.1 Mechanization level for major crops in India.
Figure 7.2 Farm power availability in India.
Figure 7.3 Potential loss of yield due to weeds in various major crops in In...
Figure 7.4 Measurement and management of N (a) LCC, (b) Spad meter and N‐man...
Figure 7.5 Drone spraying for the field crop.
Figure 7.6 Fuel consumption and CO
2
emissions from thresher and combine harv...
Figure 7.7 Emission footprints during combined harvesting (for Kartar 4000)....
Figure 7.8 Burning of paddy crop residue and emission of GHGs.
Figure 7.9 Paddy residue management techniques.
Figure 7.10 Happy seeder with press wheel operating in the combined harveste...
Figure 7.11 Flow chart of an action plan for climate‐smart mechanization.
Chapter 8
Figure 8.1 Applications of AI in agriculture.
Figure 8.2 Techniques in precision farming.
Figure 8.3 Implications in soil analysis.
Figure 8.4 Strategies for monitoring and managing crops.
Chapter 10
Figure 10.1 Present concerns in plant illness diagnosis and crop administrat...
Figure 10.2 PRISMA model for study selection.
Figure 10.3 The electromagnetic spectrum's hyperspectral range.
Figure 10.4 (a) Standard thermal images of mock‐control, LD, and HD‐inoculat...
Figure 10.5 Thermal image technique.
Figure 10.6 Block diagram of 3D imaging.
Figure 10.7 Plant disease detection and classification.
Figure 10.8 The basic structure of CNN.
Chapter 11
Figure 11.1 Some diseased plant leaves [1].
Figure 11.2 Images of different varieties of plants from the dataset [1]....
Figure 11.3 Layer information of hybrid model(ResNeXt50 + LSTM).
Figure 11.4 Architecture of CNN.
Figure 11.5 Hybrid model(ResNeXt50 + LSTM) accuracy value for various epochs...
Figure 11.6 Hybrid model(ResNeXt50 + LSTM) loss value for various epochs.
Chapter 12
Figure 12.1 Data flow diagram for FarmTechAI.
Figure 12.2 Sign‐Up page in Farm TechAI.
Figure 12.3 Login page in FarmTechAI.
Figure 12.4 Main dashboard view in farm management system.
Figure 12.5 Weather page in the farm management system.
Figure 12.6 Employee page in the farm management system.
Figure 12.7 Process of adding employee page.
Figure 12.8 Financial management page in the farm management system.
Figure 12.9 Adding crop page.
Figure 12.10 Viewing crop page.
Figure 12.11 Adding machine page.
Figure 12.12 Adding livestock page.
Figure 12.13 Viewing machine page.
Figure 12.14 Picture of the view livestock page.
Figure 12.15 A snippet of the weather API code.
Figure 12.16 A snippet of the financial API code.
Figure 12.17 A snippet of the crop monitoring function.
Figure 12.18 A snippet of the livestock tracking function.
Figure 12.19 Snippet of the financial management function.
Figure 12.20 Code snippet of the city input for forecasting function.
Figure 12.21 The results of the survey for navigation.
Figure 12.22 The results of the survey for user interface and design.
Figure 12.23 Survey results regarding security.
Figure 12.24 Survey results regarding the usability of the current features....
Figure 12.25 Survey results regarding the system's performance in terms of s...
Figure 12.26 Survey results regarding the recommendation for the proposed sy...
Figure 12.27 Survey results regarding the proposed system's future usage.
Chapter 13
Figure 13.1 Livestock monitoring in a PLF system is empowered by sensors cap...
Figure 13.2 Number of papers per year on PLF, according to the Scopus databa...
Figure 13.3 Livestock monitoring market size.
Figure 13.4 Percentage of papers on livestock monitoring per animal type.
Figure 13.5 Percentage of papers per livestock monitoring technology method....
Figure 13.6 Number of papers on livestock monitoring per each defined welfar...
Chapter 14
Figure 14.1 Smart crop management using green IoT.
Figure 14.2 Key components of green IoT.
Chapter 15
Figure 15.1 Relevant goals of SDGs.
Figure 15.2 SDGs–IoT mapping.
Figure 15.3 Budget for the years (2018–2021) in the form of various schemes/...
Figure 15.4 Budget for the year (2018–2019) in the form of various schemes/p...
Figure 15.5 Budget for the year (2019–2020) in the form of various schemes/p...
Figure 15.6 Budget for the year (2020–2021) in the form of various schemes/p...
Figure 15.7 Score‐based category.
Figure 15.8 India SDGs city index and score.
Figure 15.9 Global SDGs index and score.
Guide
Cover Page
Table of Contents
Series Page
Title Page
Copyright Page
About the Editors
List of Contributors
Preface
Begin Reading
Index
Wiley End User License Agreement
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IEEE Press445 Hoes LanePiscataway, NJ 08854
IEEE Press Editorial BoardSarah Spurgeon, Editor‐in‐Chief
Moeness Amin
Ekram Hossain
Desineni Subbaram Naidu
Jón Atli Benediktsson
Brian Johnson
Yi Qian
Adam Drobot
Hai Li
Tony Quek
James Duncan
James Lyke
Behzad Razavi
Hugo Enrique Hernandez Figueroa
Joydeep Mitra
Thomas Robertazzi
Albert Wang
Patrick Chik Yue
Emerging Smart Agricultural Practices Using Artificial Intelligence
Edited by
Ashish Kumar
School of Computer Science Engineering and TechnologyBennett UniversityGreater NoidaUttar Pradesh, India
Jai Prakash Verma
Department of Computer Science and Engineering, Institute of TechnologyNirma UniversityAhmadabadGujarat, India
Rachna Jain
IT DepartmentBhagwan Parshuram Institute of TechnologyDelhiNew Delhi, India
Copyright © 2025 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.
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About the Editors
Dr. Ashish Kumar, PhD, has been working as an Associate Professor at Bennett University, Greater Noida, U.P., India, since 2022. Prior to this, he worked at Bharati Vidyapeeth’s College of Engineering (affiliated with GGS Indraprastha University) from August 2009 to July 2022. He obtained his PhD in Computer Science and Engineering from Delhi Technological University (formerly DCE), New Delhi, India, in 2020. He received the Best Researcher award from the Delhi Technological University for his contribution to the computer vision domain. He received his MTech with distinction in Computer Science and Engineering from GGS Indraprastha University, New Delhi. He has published more than 45 research papers in various reputed national and international journals and conferences. He has published 15+ book chapters in various Scopus‐indexed books and has authored/edited several books in the fields of AI, computer vision, and healthcare with reputed publishers. He is an active member of various international societies and clubs. He is a reviewer for many reputed journals, and he is on the technical program committee of various national/international conferences. Dr. Kumar also served as a session chair at many international and national conferences. His current research interests include object tracking, image processing, artificial intelligence, and medical imaging analysis.
Dr. Jai Prakash Verma has been working as an Associate Professor in the Computer Science and Engineering Department at Institute of Technology, Nirma University, and he has been associated with the department since July 2006. He received his BSc (PCM) and MCA degrees from the University of Rajasthan, Jaipur, and his PhD from Charusat University, Changa. His PhD subject area was Text Data Summarization and Big Data Analytics. His research interests include data mining, big data analytics, and machine learning. He has contributed to research in these domains through several international conferences and journal publications. He is a recognized PhD guide at Nirma University and currently guides four PhD scholars. He has also been serving as the IQAC Coordinator for the School of Technology at Nirma University and actively contributes to all ranking‐related tasks for the institute, such as NAAC, NBA, and NIRF. He is actively involved in various training programs, such as customized training on Big Data Analytics for naval officers at INS Valsura, Indian Navy, and SAC‐ISRO, Ahmedabad. He, with other faculty members of the department, coordinates activities of the Center of Excellence in Data Science, in collaboration with SUNY Binghamton, New York.
Dr. Rachna Jain has been working as an Associate Professor at Bhagwan Parshuram Institute of Technology (affiliated with GGSIPU) since August 2021. She has worked as an Assistant Professor in Computer Science Department at Bharati Vidyapeeth's College of Engineering (affiliated with GGSIPU) from August 2007 to August 2021. She completed her PhD in Computer Science from Banasthali Vidyapith in 2017. She received her ME degree in 2011 from the Delhi College of Engineering (Delhi University) with a specialization in Computer Technology and Applications. She did her BTech in Computer Science in 2006 from the N.C. College of Engineering, Kurukshetra University. Her current research interests include cloud computing, fuzzy logic, network and information security, swarm intelligence, big data and IoT, deep learning, and machine learning. She has contributed more than 10 book chapters to various books. She has also served as Session Chair in various international conferences. She was Co‐PI of a DST project titled “Design an Autonomous Intelligent Drone for City Surveillance.” With over 15 years of academic and research experience, Dr. Jian has authored more than 50 publications in various national and international conferences and high‐repute international journals such as Scopus, ISI, and SCI.
List of Contributors
Neera AgarwalDepartment of Electronics andCommunication, Bharati Vidyapeeth’sCollege of Engineering (BVCOE)GGSIPU, DelhiNew Delhi, India
Naman AgrawalSchool of Computer ScienceEngineering and Technology, BennettUniversity, Greater NoidaUttar Pradesh, India
Yuvraj AhujaDepartment of Electronics andCommunication, Bharati Vidyapeeth’sCollege of Engineering (BVCOE)GGSIPU, Delhi, New Delhi, India
Shalom AkhaiProfessor, Department ofMechanical Engineering, MaharishiMarkandeshwar (Deemed to beUniversity) Mullana, AmbalaHaryana, India
Yogita AroraDepartment of Electronics andCommunication, Bharati Vidyapeeth’sCollege of Engineering (BVCOE),GGSIPU, Delhi, New Delhi, India
Supriya BajpaiIIT Bombay‐Monash ResearchAcademy, IIT Bombay, MumbaiMaharashtra, India
Himanshi BansalIT Department, Guru Tegh BahadurInstitute of TechnologyNew Delhi, India
Abhinav BhardwajDepartment of Computer Scienceand Engineering, Bharati VidyapeethCollege of Engineering, DelhiNew Delhi, India
B.S BhatiaPro Vice Chancellor, ManagementDepartment, RIMT UniversityFatehgarhSahib, Punjab, India
Jitendra BhatiaDepartment of Computer Science andEngineering, Institute of TechnologyNirma University, Ahmedabad,Gujarat, India
Murat Can CardakSchool of Electronic Engineeringand Computer Science, Queen MaryUniversity of London, London, UK
Heet DaveDepartment of Computer Scienceand Engineering, Institute ofTechnology, Nirma UniversityAhmadabad, Gujarat, India
Vimal GaurCSE Department, Maharaja SurajmalInstitute of Technology, DelhiNew Delhi, India
Milind GautamDepartment of Computer Scienceand Engineering, Bharati VidyapeethCollege of Engineering, DelhiNew Delhi, India
Sukhpal Singh GillSchool of Electronic Engineering andComputer ScienceQueen Mary University of LondonLondon, UK
Muhammed GolecSchool of Electronic Engineeringand Computer Science, Queen MaryUniversity of LondonLondon, UK
Manya GuptaDepartment of Computer Scienceand Engineering, Bharati VidyapeethCollege of EngineeringDelhi, New Delhi, India
Neha GuptaSchool of Computer Applications,Manav Rachna International Instituteof Research & StudiesFaridabad, Haryana, India
Rachna JainIT Department, Bhagwan ParshuramInstitute of Technology, DelhiNew Delhi, India
V. KanakarisMLV Research Group, Department ofInformatics, Democritus Universityof Thrace, Kavala, GreeceDepartment of Economics,Democritus University of ThraceKomotini, Greece
Anil KumarBharati Vidyapeeth’s College ofEngineering, DelhiNew Delhi 110063 India
Ashish KumarSchool of Computer ScienceEngineering and TechnologyBennett University, Greater NoidaUttar Pradesh, India
Manoranjan KumarSchool of Computer ScienceEngineering and TechnologyBennett University, Greater NoidaUttar Pradesh, India
Malaram KumharDepartment of Computer Science andEngineering, Institute of TechnologyNirma University, AhmedabadGujarat, India
Gargi MishraDepartment of Computer Scienceand Engineering, Bharati VidyapeethCollege of Engineering, DelhiNew Delhi, India
G. A. PapakostasMLV Research Group, Department ofInformatics, Democritus University ofThrace, Kavala, Greece
Prasoon Kumar PandeySchool of Computer ScienceEngineering and TechnologyBennett University, Greater NoidaUttar Pradesh, India
Bhavin PatelResearch Scholar, GujaratTechnological UniversityAhmedabad, Gujarat, IndiaDepartment of Computer EngineeringVishwakarma GovernmentEngineering College, AhmedabadGujarat, India
PriyaSchool of Computer Applications,Manav Rachna International Instituteof Research & Studies, FaridabadHaryana, India
Shipra RahejaIT Department, Guru Tegh BahadurInstitute of Technology, New DelhiIndia
Suraj RangaDepartment of Computer Science &Engineering, Indira Gandhi UniversityMeerpur, Rewari, Haryana, India
Jaya SainiSchool of Computer ScienceEngineering and TechnologyBennett UniversityGreater NoidaUttar Pradesh, India
Savita Kumari SheoranDepartment of Computer Science& Engineering, Indira GandhiUniversity Meerpur, RewariHaryana, India
Divya SinghSchool of Computer ScienceEngineering and TechnologyBennett University, Greater NoidaUttar Pradesh, India
Ghanapriya SinghDepartment of Electronics &Communication EngineeringNIT, KurukshetraHaryana, India
Jaspreet SinghProfessor, Computer ScienceDepartment, Christ UniversityGhaziabad, Uttar Pradesh, India
Tanu TanejaResearch Scholar, Department of CivilEngineering, RIMT UniversityFatehgarhSahibPunjab, India
Shashi TanwarProfessor, Computer Science &Engineering, Anangpuria Schoolof Management & TechnologyFaridabad, Haryana, India
Gautmi TomarDepartment of Electronics andCommunication, Bharati Vidyapeeth’sCollege of Engineering (BVCOE),GGSIPU, Delhi, New Delhi, India
Jai Prakash VermaDepartment of Computer Science andEngineering, Institute of Technology,Nirma University, AhmadabadGujarat, India
E. VrochidouMLV Research Group, Department ofInformatics, Democritus University ofThrace, Kavala, Greece
Preface
In today’s rapidly evolving world, agriculture stands at a crossroads, facing the dual challenge of feeding a growing global population while mitigating the environmental impact of farming. The integration of Artificial Intelligence (AI) into agriculture has emerged as a pivotal solution, promising increased efficiency, sustainability, and productivity.
This book is a comprehensive exploration of the transformative power of AI in agriculture. It delves deep into the innovative practices and technologies that are reshaping the way we cultivate crops, raise livestock, and manage agricultural resources. It provides a roadmap for harnessing the full potential of AI to optimize farming operations, minimize waste, and adapt to the dynamic demands of the modern agricultural landscape. “Emerging Smart Agricultural Practices Using Artificial Intelligence” envisions a future where AI contributes to a more sustainable, high‐yield, and environmentally responsible agricultural sector. By embracing these emerging practices, one can pave the way for a smarter, more efficient, and more resilient future of farming.
The book comprises 15 chapters contributing to diverse aspects of AI for reshaping traditional farming and providing resolution to contemporary agricultural challenges. The first chapter titled “Agricultural Resilience: Water Quality and Human Well‐Being” emphasizes the need of sustainable water management strategies for producing healthy crops for improved human health. The second chapter titled “Precision Farming: A Technological Revolution for Sustainable Agriculture” highlights the technological advancement for precision farming along with livestock monitoring and tracking. The third chapter titled “Precision Farming and Smart Crop Management” investigates the industrial advancements in precision farming and smart crop management techniques for sustainable practices in agriculture. The fourth chapter titled “Empowering Smart Agriculture with Artificial Intelligence” highlights the benefits of AI in agriculture for resource optimization and executing data‐driven decisions. The fifth chapter titled “Foundations of Agricultural AI” discusses the fundamentals of artificial intelligence for monitoring and managing crop, data quality, and their ethical and social implications. The sixth chapter titled “AI in Agriculture: A Comprehensive Exploration of Technological Transformation” explores the role of AI in crop, disease, and weed management. The seventh chapter titled “Integrating AI and Climate‐Smart Agricultural Mechanization: Strategies for Enhancing Productivity and Sustainability in a Changing Climate” discusses the government’s efforts for automating farming processes to enhance productivity without contributing to the adverse effects of climate change.
The eighth chapter titled “Harvesting Tomorrow: Exploring Real‐World Applications of AI in Agriculture” explores the innovations in supply chain, revolution in livestock monitoring, soil analysis, and management for improving future prospects of contemporary farming using AI. The ninth chapter titled “Smart Agriculture: Predictive Modeling of Fertilizer Requirements Using Neural Networks” analyzes the role of neural networks in identifying the requirement of fertilizer to enhance productivity. The tenth chapter titled “Reviewing Advances in Image‐Based Plant Disease Detection” reviews the imaging techniques for predicting plant diseases using AI. The eleventh chapter titled “Leveraging ResNeXt50 and LSTM for Enhanced Plant Disease Detection: A Hybrid Model Proposal” examines plant diseases and automates the detection process using deep learning models to prevent severe plant damage at a later stage. The twelfth chapter titled “FarmTechAI: Artificial‐Intelligence‐Based Modern Farmer Management System” highlights the functional and non‐functional requirements in AI‐based modern farming along with legal, social, and ethical issues for global sustainability. The thirteenth chapter titled “Livestock Monitoring and Welfare” explores the innovation in livestock monitoring using AI and other recent technologies. The fourteenth chapter titled “Smart Crop Management: Harnessing Green IoT Tomorrow” discusses the green agriculture techniques using IoT for remote monitoring of crops and precision agriculture. The last chapter titled “Current Progress of Sustainable Smart Agriculture Using Internet of Things” elaborates on the progress of AI in agriculture to ensure sustainability by analyzing the current status of various sustainable development goals worldwide.
Throughout these pages, the reader discovers real‐world case studies, expert insights, and practical guidance on how AI can revolutionize various facets of agriculture, from precision farming and livestock monitoring to sustainable resource management. We aim to bridge the gap between agricultural experts, data scientists, technology enthusiasts, and policymakers, ensuring that the knowledge shared here is accessible and beneficial to a diverse audience.
This book provides advanced learners with limitless possibilities to explore what AI offers to the world of agriculture. This is suitable for a farmer seeking ways to optimize crop yields, a researcher on the cutting edge of AI innovation, or a policymaker shaping the future of food production. This book aims to equip them with the knowledge and insights needed to navigate the exciting intersection of AI and agriculture.
1Agricultural 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
Nutrition [
27
,
28
]
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
Soil health [
3
,
29
,
30
]
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
Economic impacts [
31
,
32
]
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 water management is needed.
1.4 AI‐Driven Water Monitoring Systems
AI‐driven water monitoring systems are a crucial tool in agriculture, providing real‐time insights, predictive capabilities, and proactive water quality solutions [52]. With modern sensors, these systems gather and analyze vast amounts of data in real time to help farmers manage water, irrigation, and soil [19, 53]. These sensors monitor water pH, nutrient content, temperature, and contaminants while powerful data analytics and machine learning algorithms detect patterns, irregularities, and contamination events. Farmers can immediately identify water quality issues and take preventative measures using real‐time data [54]. These systems' sensor networks encompass the agricultural area to increase water quality information and enable personalized treatments [55]. Farmers may reduce pollution by changing irrigation, target treatments, or water sources using machine learning [56]. New data may teach AI‐driven systems to change their models, making flexibility crucial in changing agricultural environments [57, 58]. In climate change, temperature, precipitation, and severe weather may alter water quality, making adaptability essential [59]. AI systems with anomaly detection can quickly discover water quality abnormalities, minimizing economic losses and ensuring agricultural sustainability. AI‐driven water monitoring is proactive, like precision agriculture, allowing farmers to optimize irrigation operations using real‐time data to ensure crops receive the proper quantity of water at the right time [60]. This saves water and prevents over‐irrigation, which may cause waterlogging, nutrient leaching, and soil contamination [61]. AI‐driven water monitoring systems improve agriculture's resilience and sustainability while upgrading technology. However, installation costs, training requirements, and data privacy concerns must be addressed. As technology evolves and becomes more accessible, AI‐driven water monitoring becomes an essential part of contemporary agriculture [53, 62]. Table 1.3 presents AI‐driven water monitoring systems that are revolutionizing water management in agriculture.
Table 1.2 Impacts of pollutants on crop health and yield.
Pollutant category
Specific pollutant examples
Uptake pathway
Effects on plants
Effects on yield andquality
Additional notes
Heavy metals [
42
,
43
]
Lead (Pb), cadmium (Cd), and mercury (Hg)
Root uptake
Disrupt enzyme activity and nutrient uptake
Damage cell membranes and organelles
Inhibit photosynthesis and respiration
Stunted growth
Reduced seed production
Deformed plant organs
Accumulate in edible parts, posing health risks to consumers
Herbicides [
44
,
45
]
Glyphosate, atrazine, and 2,4‐
D
Root uptake and foliar absorption
Disrupt plant growth hormones
Inhibit photosynthesis and protein synthesis
Cause leaf chlorosis and necrosis
Reduced growth and development
Lower crop yield
Decreased crop quality
Can persist in soil and water for extended periods
Waterborne pathogens [
46
,
47
]
Bacteria:
Escherichiacoli
and
Pseudomonas
Viruses:
tomato mosaic virus and cucumber mosaic virus
Fungi:
Fusarium wilt and Phytophthora blight
Stomata, wounds, and lenticels
Cause tissue death and necrosis
Disrupt water and nutrient transport
Interfere with plant defense mechanisms
Wilting, stunting, and death of plants
Reduced crop yield
Decreased fruit quality
Some pathogens can be transmitted to humans through contaminated food or water
Salinity (high levels of dissolved salts) [
48
,
49
]
Sodium (Na+), chloride (Cl−), and sulfates (SO₄
2−
)
Root uptake
Disrupts water uptake by plants
Creates osmotic stress
Damages cell membranes and organelles
Reduced plant growth
Leaf scorching and wilting
Decreased crop yield and quality
Can render some land unsuitable for agriculture
Industrial pollutants [
50
,
51
]
Polycyclic aromatic hydrocarbons (PAHs), pharmaceuticals, and personal care products (PPCPs)
Root uptake and foliar absorption
Can disrupt various plant physiological processes
May mimic hormones and affect plant growth
Can accumulate in plant tissues
Reduced plant growth and development
Potential negative impacts on human health through food consumption
Emerging contaminants with limited research on long‐term effects
These smart systems enhance agricultural productivity while promoting efficient water usage and environmental sustainability. Thus, the extensive sensor network monitors water supplies, irrigation systems, and soil, providing real‐time analytics and machine learning for predictive risk management.
1.5 Research Gaps and Research Dimensions
The study highlights the need for interdisciplinary research, advanced detection technologies, and innovative crop production methods to sustain agricultural water quality and protect water resources for future generations. Tables 1.2 and 1.3 indicate valuable research areas in agricultural water management. Research on the impact of pollutants on crop health and the benefits of AI‐powered water monitoring systems in agriculture has identified gaps. These include understanding the long‐term effects of contaminants, integrating emerging contaminants, and holistically assessing water quality monitoring systems. Sustainable agriculture requires studying contaminants' long‐term effects on soil health, crop productivity, and food safety. Future research should focus on the detection, accumulation, and ecological effects of contaminants by data analysis as it is yet understudied. Encourage adoption of smart monitoring technologies, and high installation costs, data privacy concerns, and technological barriers must be addressed.
The management of agricultural water quality requires interdisciplinary techniques, longitudinal research, sophisticated detection technologies, policy and governance, capacity development, and environmental issues. This study explores the link between water quality, soil health, and agricultural yield through longitudinal research across multiple growing seasons. It uses sensor platforms, molecular diagnostic tools, and remote sensing to improve water quality monitoring systems. Policy and governance are crucial for effective water quality management and regulatory compliance.
Table 1.3 Benefits of AI‐powered water monitoring in agriculture.
Feature
Description
Benefits
Data collection and analysis [
63
,
64
]
Extensive sensor network monitors water supplies, irrigation systems, and soil
Sensors track parameters such as pH, nutrient content, temperature, and pollutants
Advanced analytics and machine learning process data in real time
Identifies trends, abnormalities, and contamination events
Allows quick recognition of deviations from ideal water quality
Predictive capabilities [
65
–
67
]
Machine learning predicts risks based on historical data and current trends
Enables proactive risk management by adjusting irrigation practices, targeting treatments, and shifting water sources to avoid contamination
Adaptability and flexibility [
68
–
70
]
Systems learn and modify models with new data, adapting to changing conditions
Crucial for responding to climate change and unforeseen circumstances
Anomaly detection [
71
–
73
]
Quickly detects water quality abnormalities
Minimizes economic losses and ensures agricultural sustainability
Precision irrigation [
74
,
75
]
Real‐time data optimizes irrigation, ensuring proper water delivery
Minimizes water loss and avoids logging off water nutrient loss and soil adulteration
1.6 Precision Irrigation Techniques
Automation in agriculture is enhancing food production and reducing environmental impact by optimizing precise irrigation for diverse crops. Using sensors, drones, and automated systems, agriculture addresses rising food demand, population growth, and water shortages by adjusting water consumption to meet crop needs at various stages, thereby conserving water and reducing over‐irrigation. Precision irrigation relies on real‐time monitoring and data‐driven decision‐making. Field sensors measure soil moisture, temperature, and crop health to help farmers save resources. Soil moisture sensors let farmers adjust irrigation schedules to match crop and soil demands in real time. Drones help farmers see water shortages, crop health concerns, and irrigation system efficiency from above. Automated irrigation systems improve water efficiency and uniformity by watering precisely and adaptably without operator intervention. Environmental advantages of precision irrigation include reduced water stress during development, plant growth, and environmental resilience. Reducing over‐irrigation minimizes soggy roots and nutrient loss [76–78].
1.7 Waterborne Pathogens in Farming
Waterborne pathogens pose a significant threat to agriculture, as water supplies from natural or artificial sources can harbor bacteria, viruses, and parasites that threaten crops and cattle. These infections must be detected quickly and accurately to minimize disease spread, protect agricultural systems, and ensure food supply chain safety. Traditional techniques for detecting waterborne infections are laborious and slow, delaying hazard detection. New molecular methods and diagnostic tools aid farmers in detecting hazardous bacteria, ensuring crop health, and reducing waterborne illness risks [78, 79]. Molecular methods like Polymerase Chain Reaction (PCR) and Next‐Generation Sequencing (NGS) have revolutionized pathogen identification in water sources, allowing for high sensitivity and specificity in identifying harmful bacteria.[80]. Rapid diagnostic technologies speed up identification and reveal water source microbial composition in realtime, allowing for real‐time monitoring to prevent disease spread and safeguard crops [81]. Waterborne diseases affect cattle as well as crops, and farmers can avoid cattle infections by quickly identifying pathogens in water sources. The importance of aquatic pathogen detection extends beyond agricultural production and animal health, as it also impacts human health [82]. A comprehensive water monitoring and pathogen detection system requires strategic sampling and continuous water source surveillance, with automated sensors and remote monitoring systems improving this procedure [83]. Farmers must be aware of waterborne pathogen detection and best practices shared by agricultural extension services, research institutions, and government agencies to integrate advanced detection technologies into agricultural practices.
1.8 Livestock Health and Water Safety
Water quality is crucial for livestock health, causing infections, reproductive problems, and economic losses. Farmers must implement comprehensive management techniques and sustainable water management and educate farmers about water safety to maintain agricultural systems' resilience and prevent waterborne illnesses [83–85]. The following key points emphasize the importance of livestock health, water safety, and water quality in agricultural settings:
Water quality is crucial for cattle health and well‐being in agricultural settings.
Contaminated water can lead to infections, impaired development, reproductive issues, and producer losses.
Waterborne diseases can harm cattle's development, weight gain, and reproduction.
Farmer management should include periodic testing for water safety.
Livestock farms can reduce dependence on polluting external water sources and maintain water supply systems.
Regular cleaning and maintenance of water storage facilities protect water quality and livestock health.
Farmers should be educated on livestock water safety and equipped with tools to prevent animal health concerns.
1.9 Sustainable Water Management Strategies
Water management in agricultural systems must be sustainable to maintain water supplies and promote environmental sustainability [86]. Rainwater collection, irrigation, soil moisture management, and agroecology are essential [87]. Modern drip irrigation and sprinkler systems decrease runoff and evaporation, while rainwater collecting lowers water dependence [88]. Real‐time soil moisture management ensures crops receive the right amount of water at the right time [89]. Environmental balance and natural resource conservation support sustainable water management in agroecological systems. Cover cropping, agroforestry, and rotational grazing improve soil structure, erosion, and water retention [90]. Riparian buffer zones and wetlands in agricultural landscapes remove sediments and contaminants before reaching water sources [91]. Crop rotation and diversity optimize water use and limit resource depletion risk. Deep root systems in certain crops improve soil structure and water retention [92]. Water‐efficient crop selection, such as drought‐tolerant or water‐efficient cultivars, connects agricultural operations with water availability, boosting sustainability [93]. Education and outreach are essential for implementing sustainable water management strategies. Table 1.4 presents a comprehensive list of agricultural practices that focus on water management and soil health, each offering unique benefits for sustainable agriculture.
Table 1.4 Sustainable water conservation practices in agriculture.
Practice
Description
Benefits
Rainwater harvesting [
94
,
95
]
Capturing and storing rainwater for later use in irrigation
Reduces reliance on conventional water sources.
Modern irrigation systems (drip, sprinkler) [
95
–
97
]
Targeted and efficient application of water directly to crops.
Reduces water runoff and evaporation
Real‐time soil moisture management [
98
,
99
]
Utilizing sensors and data to monitor soil moisture and adjust irrigation accordingly
Ensures crops receive the right amount of water at the right time
Agroecology: cover cropping [
100
,
101
]
Planting additional crops between main crops to improve soil health and water retention
Promotes environmental balance and natural resource conservation
Agroforestry: integrating trees and shrubs within agricultural landscapes [
102
,
103
]
Improving soil structure and water infiltration
Enhances soil quality and water storage capacity
Rotational grazing [
104
,
105
]
Rotating livestock grazing patterns to prevent overgrazing and improve soil health
Promotes healthy soil ecosystems
Riparian buffer zones and wetlands [
106
,
107
]
Creating buffer areas between agricultural land and water bodies to filter sediments and pollutants
Protects water quality and aquatic ecosystems
Crop rotation and diversity [
108
,
109
]
Planting different crops in sequence or together to improve soil health, nutrient availability, and water use efficiency
Reduces the risk of resource depletion and promotes healthy soil ecosystems
Deep‐rooted crops [
110
,
111
]
Selecting crops with deep root systems to improve soil structure and water retention
Adapts agricultural practices to local water conditions and promotes sustainable use
Water‐efficient crop varieties [
101
,
112
]
Selecting drought‐tolerant or water‐efficient cultivars based on local water availability
Adapts agricultural practices to local water conditions and promotes sustainable use
1.10 Human Health Implications
Clean air, water, and nutritious food are essential requirements for all living beings, including humans. Both indoor and outdoor air quality significantly impact human health, similar to the importance of clean water [113–118]. Water quality and agriculture are interconnected, affecting human health in various ways [119, 120]. Contaminated crops and livestock products can introduce harmful pollutants into the food chain, leading to infections, heavy metals, pesticides, and other contaminants [121]. To assess food safety and reduce health hazards, it is crucial to understand water pollutants and their effects on crops. Livestock may also absorb toxins from polluted water, increasing health hazards in meat, milk, and eggs [122, 123]. Infected water sources can transmit illnesses via agricultural goods, with pathogenic bacteria, viruses, and parasites infecting crops through irrigation or contact with polluted surfaces [124]. Consuming contaminated agricultural goods can cause diarrhea, gastrointestinal infections, and other waterborne disorders [125]. Bioaccumulation/toxin exposure is essential for understanding the health effects of agricultural water contamination, as waterborne heavy metals, herbicides, and other pollutants accumulate in crops and animals, potentially causing long‐term health problems due to toxin buildup [126]. Figure 1.1 illustrates the significant effects of water pollution on both human health and the environment.
Community health and clean water are crucial for agriculture‐dependent populations, as contaminated water sources increase waterborne illness and toxin exposure [127]. Safe and clean water for agriculture and homes is essential for community well‐being [128]. Pregnant women and small children are especially vulnerable to agricultural water contamination, as toxin exposure may cause birth abnormalities or developmental difficulties. Protecting mother and child health requires specific measures to protect agricultural water quality [129]. E‐waste is a major environmental hazard, including its contribution to water pollution [130]. Agriculture pollution concerns include cancer, neurological issues, and endocrine disruption. Monitoring and control are crucial to prevent chronic health impacts from agricultural pesticides while addressing human health consequences of water quality in agriculture is essential for a resilient and sustainable system [131].
Figure 1.1 Impacts of water pollution on human health and environment.
1.11 Significance of Research in Agricultural Water Quality
AI‐powered water monitoring systems are a boon for agricultural communities, as they monitor the water quality via sensors and may even actuate alarming signals based on the data collected and thus, they provide quality water and food security, help in the direction toward better human health, environmental sustainability, agricultural resilience practices, and economic prosperity [132]. AI provides reliable, real‐time information [133, 134]. This information can be further studied to predict trends, allowing legislators, water resource managers, and farmers to make educated decisions. AI systems thus act as early warning systems for contamination levels and pollution, accordingly, directing data‐driven decision‐making and supporting environmentally conscious efforts [135, 136]. Modern agriculture is therefore undergoing a transition shift by employing smart sensors, IoT devices, and machine learning algorithms to improve water efficiency, minimize waste, and raise crop yields [137–140]. So, understanding the link between water pollution and crop vitality is crucial as it allows these AI systems to avoid hazards, assuring a prosperous future for agricultural enterprises.
1.12 Conclusion
Data‐centric techniques in modern agriculture have many advantages/benefits as well as challenges to overcome. The practice in use includes data monitoring and decision‐making for precise water quality control and management, proactive crop health monitoring, data‐driven sustainability, reduced environmental footprint building agricultural resilience for sustainable future.
Modern water management uses AI‐powered sensors to monitor water quality in real time, identifying salinity, pH, and nutrient levels. This aids to protect food and agriculture crops from deterioration/damage due to the water pollution levels. Practical crop health monitoring by using precise smart sensors and data analysis using machine learning algorithms aid to predict nutrient deficiencies, detect disease by enabling early detection, and thereby provide instant treatment to agriculture crops by early detecting issues.
By making decisions using practical data‐driven information, i.e. data analysis for implementing data‐driven practices from various sources thereby optimizing resource use, taking decisions on fertilization, planting practices, pest control strategies, and crop rotation, we step toward sustainable development practices as this improves farm efficiency and minimizes environmental impact.
Data analysis‐based knowledge of water quality related to agriculture facilitates the information sharing of data among farmers, researchers, and agribusinesses promoting cooperation among them for accelerating agriculture and better practices. Secure open‐source data sharing drives improvement in agriculture technology practices and promotes cooperation among farmers, researchers, and agribusinesses by promoting digital literacy.
Agricultural firms can greatly benefit from data‐driven insights, which are essential for building long‐term agricultural resilience. These insights enable informed planning and risk management, helping firms to mitigate the impacts of shifting weather patterns, water and air pollution, and other environmental challenges. The collection of data and its advanced data analytics for predictive modeling must be prioritized. In the long run, focusing on data collected from sensor networks can help food production systems become more resilient to environmental change and resource scarcity.
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