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Submit a ProposalMathematical Modeling in Agriculture
 | Edited by Sabyasachi Pramanik, Niranjanamurthy M., Ankur Gupta, and Ahmed J. Obaid Copyright: 2025 | Status: Published ISBN: 9781394233717 | Hardcover | 454 pages Price: $225 USD  |
One Line Description
The main goal of the book is to explore the idea behind data modeling in smart agriculture using information and communication technologies and tools to make agricultural practices more functional, fruitful and profitable.
Audience
Students, researchers, scientists, academics, policy makers from diverse disciplines including ecology, environment, agroforestry, forestry, agriculture, geology, soil science, plant science, climate change, sustainability, and related sciences
Students, researchers, scientists, academics, policy makers from diverse disciplines including ecology, environment, agroforestry, forestry, agriculture, geology, soil science, plant science, climate change, sustainability, and related sciences
Description
The research in the book looks at the likelihood and level of use of implemented technological components with regard to the adoption of different precision agricultural technologies. To identify the variables affecting farmers choices to embrace more precise technology, zero-inflated Poisson and negative binomial count data regression models were utilized. Outcomes from the count data analysis of a random sample of various farm operators show that various aspects, including farm dimension, farmer demographics, soil texture, urban impacts, farmer position of liabilities, and position of the farm in a state, were significantly associated with the approval severity and likelihood of precision farming technologies.
Farm management information systems (FMIS) have constantly advanced in complexity as they have incorporated new technology, the most recent of which is Internet connection. However, few FMIS have fully tapped into the internets possibilities, and the newly developing idea of precision agriculture receives little or no support in the FMIS that are now being sold. FMIS for precision agriculture must meet a few more criteria beyond those of regular FMIS, which increases the technological complexity of these systems deployment in a number of ways. In order to construct an FMIS that meet these extra needs, the authors here evaluated various cutting edge web based methods. The goal was to determine the requirements that precision agriculture placed on FMIS.
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The research in the book looks at the likelihood and level of use of implemented technological components with regard to the adoption of different precision agricultural technologies. To identify the variables affecting farmers choices to embrace more precise technology, zero-inflated Poisson and negative binomial count data regression models were utilized. Outcomes from the count data analysis of a random sample of various farm operators show that various aspects, including farm dimension, farmer demographics, soil texture, urban impacts, farmer position of liabilities, and position of the farm in a state, were significantly associated with the approval severity and likelihood of precision farming technologies.
Farm management information systems (FMIS) have constantly advanced in complexity as they have incorporated new technology, the most recent of which is Internet connection. However, few FMIS have fully tapped into the internets possibilities, and the newly developing idea of precision agriculture receives little or no support in the FMIS that are now being sold. FMIS for precision agriculture must meet a few more criteria beyond those of regular FMIS, which increases the technological complexity of these systems deployment in a number of ways. In order to construct an FMIS that meet these extra needs, the authors here evaluated various cutting edge web based methods. The goal was to determine the requirements that precision agriculture placed on FMIS.
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Author / Editor Details
Sabyasachi Pramanik, PhD, is an associate professor in the Department of Computer Science and Engineering, Haldia Institute of Technology, India. He has many publications in technical conferences and journals, as well as online book chapter contributions. He is also a reviewer for and on numerous editorial boards for technical journals. He has authored one book and edited nine books, including books for Scrivener Publishing.
Sabyasachi Pramanik, PhD, is an associate professor in the Department of Computer Science and Engineering, Haldia Institute of Technology, India. He has many publications in technical conferences and journals, as well as online book chapter contributions. He is also a reviewer for and on numerous editorial boards for technical journals. He has authored one book and edited nine books, including books for Scrivener Publishing.
Niranjanamurthy M, PhD, is an assistant professor in the Department of Artificial Intelligence and Machine Learning, BMS Institute of Technology and Management, Yelahanka, Bengalore, India. He has over ten years of teaching experience and two years of industry experience as a software engineer. He has published five books and is working on numerous books for Scrivener Publishing. He has published 54 research papers in various scientific refereed journals and filed ten patents, with two granted so far. He is a reviewer for more than 20 journals and has received numerous awards.
Ankur Gupta, MTech, is an assistant professor in the Department of Computer Science and Engineering at Vaish College of Engineering, Rohtak, India. He has many publications in scientific journals and conferences and online book chapter contributions.
Ahmed J. Obaid, PhD, is an assistant professor in the Department of Computer Science, Faculty of Computer Science and Mathematics, University of Kufa, Iraq. He has over 14 years of teaching experience and is a board member on numerous scientific journals. He has published over 75 journal research articles, five book chapters, 15 conference papers, 10 conference proceedings, and has edited eight books.
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Table of Contents
Preface
1. Analyzing the Impact of Food Safety Regulations on Agricultural Supply Chains: A Mathematical Modeling Perspective
Nimit Kumar, Shwetha M.S., Govind Shay Sharma, Nitin Ubale, Nuzhat Fatima Rizvi and Dharmesh Dhabliya
1.1 Introduction
1.2 Resources and Techniques
1.3 Results and Analysis
1.3.1 Knowledge, Application, and Obstacles to Food Modeling
1.3.2 Obstacles to our Company’s Use of Mathematical Modeling
1.4 Conclusion
References
2. Modeling the Effects of Land Degradation on Agricultural Productivity: Implications for Legal and Policy Interventions
Amit Verma, Istita Auddy, Murli Manohar Gour, Dhwani Bartwal, Sukhvinder Singh Dari and Ankur Gupta
2.1 Introduction
2.2 Materials and Procedures
2.2.1 Content of Minerals
2.3 Results and Analysis
2.4 Conclusion
References
3. Mathematical Modeling of Carbon Sequestration in Agricultural Soils: Implications for Climate Change Mitigation Policies
Kailash Malode, Brijpal Singh Rajawat, Amar Shankar S., Ravindra Kumar, Deepti Khubalkar and Sabyasachi Pramanik
3.1 Introduction
3.2 Resources and Techniques
3.2.1 Reference Trial
3.2.2 Interviews with Agriculturists in London Suburb and Liverpool
3.2.2.1 Overall Explanation of the Sampled Region and Organized Interviews
3.2.3 Online Tools for Calculating CF
3.3 Results
3.3.1 Agricultural Data as Model I/P
3.3.1.1 Case Study
3.3.1.2 From Discussions with Farmers
3.3.2 Farms’ Estimated GHG Emissions
3.3.3 Effects of Mitigating Measures
3.4. Discussion
3.4.1 Evaluating the Possible Effects of Mitigating Measures
3.5 Conclusions
References
4. Optimizing Livestock Feed Formulation for Sustainable Agriculture: A Mathematical Modeling Approach
Rutul Patel, Upasana, Ashutosh Pattanaik, Deepak Kumar, Ahmar Afaq and Soma Bag
4.1 Introduction
4.2 Managing Swine Herds Using Modeling
4.2.1 System of a Sow Herd
4.2.2 Major statistical Techniques Used in Modeling Cattle Herds
4.2.2.1 Literature Review on Herd Modeling for Cattle
4.2.2.2 Models for Simulation
4.2.2.3 Models for Optimization
4.2.2.4 The Integration of Simulation and Optimization
4.3 Models of a Sow Herd
4.3.1 Chosen Models
4.3.2 Input Criteria
4.3.2.1 Parameters Used as Inputs in Optimization Models
4.3.2.2 Parameters Used as Inputs in Simulation Techniques
4.3.3 Results from the Models
4.3.4 The Models’ Validation
4.3.5 Opportunities for Implementation and Integration
4.3.6 Management of Risk
4.3.7 Additional Submissions and Literature Review
4.4 Discussion
4.5 Conclusions
References
5. Modeling the Economic Impact of Agricultural Regulations: A Case Study on Environmental Compliance Costs
Vikesh Rami, Sunil Kumar, Gautham Krishna, Abhinav, Sukhvinder Singh Dari and Dharmesh Dhabliya
5.1 Introduction
5.2 Mechanisms Study Time and Location
5.3 Sampling
5.4 Analysis, Both Physical and Chemical
5.5 Module for Water Quality
5.6 Particulate Phosphorus and Suspended Solids
5.7 Calculation of PP
5.8 Model Caliphy
5.9 Scientifications Described by the Model
5.10 Simulation of Sediment Trap
5.11 Pumping Profile Modifications Simulation
5.12 Conclusion
References
6. Quantifying the Economic Benefits of Precision Agriculture Technologies: A Mathematical Modeling Study
Deepak Kumar, Apexaben Rathod, Sachchida Nand Singh, Meena Y. R., Rushil Chandra and Ankur Gupta
6.1 Introduction
6.2 Method and Materials
6.3 Conclusion and Results
6.4 Conclusions
References
7. Optimizing Resource Allocation in Agribusinesses: A Mathematical Modeling Approach Considering Legal Factors
Vishvendra Singh, Navghan Mahida, Anand Janardan Madane, Sudhakar Reddy, Parth Sharma and Sabyasachi Pramanik
Introduction
Methods
A Framework for the Transmission and Command of Brucellosis: A Case Study Overview
Brucellosis Nominal Transmission Modeling
Modeling Disease Costs and Control Capabilities
Creating a Cost Model and Confronting the Challenge of Control Design
Analysis, Design, and Parameterization Techniques
Overview of the Control and Surveillance Design
Network Model Identification and Validation for Zoonoses
Results
Indicative Model
Control Strategy Modeling
Optimized Approaches
Parameterization
Discussion
Wide-Ranging Perspectives on High-Performance Control
Talking About Parameterzing Models
Conclusion
References
8. Modeling the Dynamics of Agricultural Cooperatives and Legal Implications for Farmer Organizations
Shiv Shankar Shankar, Prashantkumar Zala, Ashutosh Awasthi, Ezhilarasan G., Sukhvinder Singh Dari and Soma Bag
8.1 Introduction
8.2 Resources and Techniques
8.3 Conclusion
References
9. Optimizing Agroforestry Systems for Sustainable Agriculture: A Mathematical Modeling Approach
Beemkumar Nagappan, Aakriti Chauhan, Chandni Mori, Praveen Kumar Singh, Shilpa Sharma and Sabyasachi Pramanik
9.1 Introduction
9.2 Relationships Between Structure and Activity (SAR) and the Level of Toxicological Involvement
9.3 Threshold Approaches
9.4 Reciprocal Analysis
9.5 Chemical-Specific Adjustments
Conclusion
References
10. Simulating the Effects of Climate-Smart Agriculture Practices on Farm Resilience: A Mathematical Modeling Approach
Kiran K. S., Meenakshi Dheer, Mukesh Laichattiwar, Devendra Pal Singh, Vaidehi Pareek and Soma Bag
10.1 Introduction
10.2 Definitions, Concepts, and Methods for the Analytical Framework
10.3 Results
10.4 Consequences for Political Implementations
10.5 Advanced Research
10.6 Conclusions
References
11. Modeling the Dynamics of Agrochemical Regulations and Impacts on Agricultural Productivity
Hannah Jessie Rani, Akanchha Singh, Aishwary Awasthi, Ashwani Rawat, Nuvita Kalra and Ankur Gupta
11.1 Introduction
11.2 Resources and Techniques
11.3 Results
11.4 Discussion
11.5 Conclusion
References
12. Optimizing Energy Consumption in Greenhouse Production: A Mathematical Modeling Approach
Beemkumar Nagappan, Arun Gupta, Sachin Gupta, Diksha Nautiyal, Aarti Kalnawat and Dharmesh Dhabliya
12.1 Introduction
12.2 Literature Review
12.3 The Creation of Mathematical Models a Range of Models
12.4 Formulation of a Model
12.5 Modeling of Groundwater Quality
12.6 Conclusion
References
13. Analyzing the Economic and Legal Impacts of Intellectual Property Rights on Plant Breeding Innovations: A Mathematical Modeling Study
Gopalakrishna K., Bhirgu Raj Maurya, Rajeev Kumar, Sushila Arya, Himanshi Bhatia and Ankur Gupta
13.1 Introduction
13.2 Competition Postulates
13.3 Transparent Competition
13.3.1 Effect of Competitiveness-Density
13.3.2 Changes to the Population’s Size Structure
13.4 Concurrence Inter-Specific
13.4.1 Adding Damage
13.4.2 Neighborhood Function
13.4.3 Innovative Design and Analysis
13.5 Dynamic Plant Growth and Competition Models
13.5.1 Dynamic Population
13.6 Aspects Impacting the Result of Competitiveness
13.7 Crop-Weed Competition Models Applied in Practical Situations
13.8 Conclusion
References
14. Simulating the Effects of Land Use Regulations on Agricultural Land Values: A Mathematical Modeling Study
Ashwani Rawat, Ramachandran T., Yogesh Chandra Gupta, Manoj Kumar Mishra, Gabriela Michael and Sabyasachi Pramanik
14.1 Introduction
14.2 Models of Component Agricultural Systems
14.3 Present-Day Farming System Frameworks in Relation to Certain Application Situations
14.4 Discussion
References
15. Simulating the Effects of Agricultural Land Fragmentation on Farm Efficiency: A Mathematical Modeling Analysis
Diksha Nautiyal, Manjunath H. R., Praveen Kumar Singh, Umesh Kumar Tripathi, Saurabh Raj and Soma Bag
15.1 Introduction
15.2 Conceptual Foundation
15.3 Resources and Techniques Household Polls
15.4 Results
15.5 Discussion
15.6 Conclusions
References
16. Simulating the Effects of Land Use Policies on Agricultural Productivity: A Mathematical Modeling Perspective
Vinaya Kumar Yadav, Sushila Arya, Asha Rajiv R., Devendra Pal Singh, Siddharth Ranka and Dharmesh Dhabliya
16.1 Introduction
16.2 Upcoming Applications of NextGen Farming Frameworks
16.3 Envisioned Consumers of the Application Chain Beneficiaries
16.4 Conclusion and Research Plan
References
17. Quantifying the Economic Benefits of Agricultural Extension Services: A Mathematical Modeling Analysis
Rajeev Kumar, Satendra Kumar, Pradeepa P., Akanchha Singh, Karun Sanjaya and Ankur Gupta
17.1 Introduction
17.2 Creating New Models for the Future: A Demand-Driven, Prospective Strategy
17.3 Potential Improvements to Model Elements
17.4 Conclusions
References
18. Modeling the Impact of Agricultural Investment Incentives on Rural Development: Legal and Economic Perspectives
Dal Chandra, Manoj Kumar Mishra, Ankit Pant, Ahmadi Begum, Sukhvinder Singh Dari and Dharmesh Dhabliya
18.1 Introduction
18.2 Approach
18.3 Conversation
18.4 Conclusion
References
19. Optimizing Harvest Scheduling in Agriculture: A Mathematical Modeling Approach Considering Legal Restrictions
Heejeebu Shanmukha Viswanath, Umesh Kumar Tripathi, Minnu Sasi, Kishore Kumar Pedapenki, Prashant Dhage and Ankur Gupta
19.1 Initialization
19.2 Structure of the System
19.3 Irrigation Community Event
19.4 Assessment and Authentication
19.5 Conclusions
References
20. Quantifying the Economic Benefits of Agricultural Data Sharing: A Mathematical Modeling Perspective
Aruno Raj Singh, Vinaya Kumar Yadav, Laishram Zurika, Dasarathy A. K., Abhishekh Benedict and Dharmesh Dhabliya
20.1 Introduction
20.2 Model for Data Mining Process
20.3 Techniques for Machine Learning
20.4 Website Tools
20.5 Case Study: Grading of Mushrooms
20.6 Conclusion
References
Index
Back to Top
Preface
1. Analyzing the Impact of Food Safety Regulations on Agricultural Supply Chains: A Mathematical Modeling Perspective
Nimit Kumar, Shwetha M.S., Govind Shay Sharma, Nitin Ubale, Nuzhat Fatima Rizvi and Dharmesh Dhabliya
1.1 Introduction
1.2 Resources and Techniques
1.3 Results and Analysis
1.3.1 Knowledge, Application, and Obstacles to Food Modeling
1.3.2 Obstacles to our Company’s Use of Mathematical Modeling
1.4 Conclusion
References
2. Modeling the Effects of Land Degradation on Agricultural Productivity: Implications for Legal and Policy Interventions
Amit Verma, Istita Auddy, Murli Manohar Gour, Dhwani Bartwal, Sukhvinder Singh Dari and Ankur Gupta
2.1 Introduction
2.2 Materials and Procedures
2.2.1 Content of Minerals
2.3 Results and Analysis
2.4 Conclusion
References
3. Mathematical Modeling of Carbon Sequestration in Agricultural Soils: Implications for Climate Change Mitigation Policies
Kailash Malode, Brijpal Singh Rajawat, Amar Shankar S., Ravindra Kumar, Deepti Khubalkar and Sabyasachi Pramanik
3.1 Introduction
3.2 Resources and Techniques
3.2.1 Reference Trial
3.2.2 Interviews with Agriculturists in London Suburb and Liverpool
3.2.2.1 Overall Explanation of the Sampled Region and Organized Interviews
3.2.3 Online Tools for Calculating CF
3.3 Results
3.3.1 Agricultural Data as Model I/P
3.3.1.1 Case Study
3.3.1.2 From Discussions with Farmers
3.3.2 Farms’ Estimated GHG Emissions
3.3.3 Effects of Mitigating Measures
3.4. Discussion
3.4.1 Evaluating the Possible Effects of Mitigating Measures
3.5 Conclusions
References
4. Optimizing Livestock Feed Formulation for Sustainable Agriculture: A Mathematical Modeling Approach
Rutul Patel, Upasana, Ashutosh Pattanaik, Deepak Kumar, Ahmar Afaq and Soma Bag
4.1 Introduction
4.2 Managing Swine Herds Using Modeling
4.2.1 System of a Sow Herd
4.2.2 Major statistical Techniques Used in Modeling Cattle Herds
4.2.2.1 Literature Review on Herd Modeling for Cattle
4.2.2.2 Models for Simulation
4.2.2.3 Models for Optimization
4.2.2.4 The Integration of Simulation and Optimization
4.3 Models of a Sow Herd
4.3.1 Chosen Models
4.3.2 Input Criteria
4.3.2.1 Parameters Used as Inputs in Optimization Models
4.3.2.2 Parameters Used as Inputs in Simulation Techniques
4.3.3 Results from the Models
4.3.4 The Models’ Validation
4.3.5 Opportunities for Implementation and Integration
4.3.6 Management of Risk
4.3.7 Additional Submissions and Literature Review
4.4 Discussion
4.5 Conclusions
References
5. Modeling the Economic Impact of Agricultural Regulations: A Case Study on Environmental Compliance Costs
Vikesh Rami, Sunil Kumar, Gautham Krishna, Abhinav, Sukhvinder Singh Dari and Dharmesh Dhabliya
5.1 Introduction
5.2 Mechanisms Study Time and Location
5.3 Sampling
5.4 Analysis, Both Physical and Chemical
5.5 Module for Water Quality
5.6 Particulate Phosphorus and Suspended Solids
5.7 Calculation of PP
5.8 Model Caliphy
5.9 Scientifications Described by the Model
5.10 Simulation of Sediment Trap
5.11 Pumping Profile Modifications Simulation
5.12 Conclusion
References
6. Quantifying the Economic Benefits of Precision Agriculture Technologies: A Mathematical Modeling Study
Deepak Kumar, Apexaben Rathod, Sachchida Nand Singh, Meena Y. R., Rushil Chandra and Ankur Gupta
6.1 Introduction
6.2 Method and Materials
6.3 Conclusion and Results
6.4 Conclusions
References
7. Optimizing Resource Allocation in Agribusinesses: A Mathematical Modeling Approach Considering Legal Factors
Vishvendra Singh, Navghan Mahida, Anand Janardan Madane, Sudhakar Reddy, Parth Sharma and Sabyasachi Pramanik
Introduction
Methods
A Framework for the Transmission and Command of Brucellosis: A Case Study Overview
Brucellosis Nominal Transmission Modeling
Modeling Disease Costs and Control Capabilities
Creating a Cost Model and Confronting the Challenge of Control Design
Analysis, Design, and Parameterization Techniques
Overview of the Control and Surveillance Design
Network Model Identification and Validation for Zoonoses
Results
Indicative Model
Control Strategy Modeling
Optimized Approaches
Parameterization
Discussion
Wide-Ranging Perspectives on High-Performance Control
Talking About Parameterzing Models
Conclusion
References
8. Modeling the Dynamics of Agricultural Cooperatives and Legal Implications for Farmer Organizations
Shiv Shankar Shankar, Prashantkumar Zala, Ashutosh Awasthi, Ezhilarasan G., Sukhvinder Singh Dari and Soma Bag
8.1 Introduction
8.2 Resources and Techniques
8.3 Conclusion
References
9. Optimizing Agroforestry Systems for Sustainable Agriculture: A Mathematical Modeling Approach
Beemkumar Nagappan, Aakriti Chauhan, Chandni Mori, Praveen Kumar Singh, Shilpa Sharma and Sabyasachi Pramanik
9.1 Introduction
9.2 Relationships Between Structure and Activity (SAR) and the Level of Toxicological Involvement
9.3 Threshold Approaches
9.4 Reciprocal Analysis
9.5 Chemical-Specific Adjustments
Conclusion
References
10. Simulating the Effects of Climate-Smart Agriculture Practices on Farm Resilience: A Mathematical Modeling Approach
Kiran K. S., Meenakshi Dheer, Mukesh Laichattiwar, Devendra Pal Singh, Vaidehi Pareek and Soma Bag
10.1 Introduction
10.2 Definitions, Concepts, and Methods for the Analytical Framework
10.3 Results
10.4 Consequences for Political Implementations
10.5 Advanced Research
10.6 Conclusions
References
11. Modeling the Dynamics of Agrochemical Regulations and Impacts on Agricultural Productivity
Hannah Jessie Rani, Akanchha Singh, Aishwary Awasthi, Ashwani Rawat, Nuvita Kalra and Ankur Gupta
11.1 Introduction
11.2 Resources and Techniques
11.3 Results
11.4 Discussion
11.5 Conclusion
References
12. Optimizing Energy Consumption in Greenhouse Production: A Mathematical Modeling Approach
Beemkumar Nagappan, Arun Gupta, Sachin Gupta, Diksha Nautiyal, Aarti Kalnawat and Dharmesh Dhabliya
12.1 Introduction
12.2 Literature Review
12.3 The Creation of Mathematical Models a Range of Models
12.4 Formulation of a Model
12.5 Modeling of Groundwater Quality
12.6 Conclusion
References
13. Analyzing the Economic and Legal Impacts of Intellectual Property Rights on Plant Breeding Innovations: A Mathematical Modeling Study
Gopalakrishna K., Bhirgu Raj Maurya, Rajeev Kumar, Sushila Arya, Himanshi Bhatia and Ankur Gupta
13.1 Introduction
13.2 Competition Postulates
13.3 Transparent Competition
13.3.1 Effect of Competitiveness-Density
13.3.2 Changes to the Population’s Size Structure
13.4 Concurrence Inter-Specific
13.4.1 Adding Damage
13.4.2 Neighborhood Function
13.4.3 Innovative Design and Analysis
13.5 Dynamic Plant Growth and Competition Models
13.5.1 Dynamic Population
13.6 Aspects Impacting the Result of Competitiveness
13.7 Crop-Weed Competition Models Applied in Practical Situations
13.8 Conclusion
References
14. Simulating the Effects of Land Use Regulations on Agricultural Land Values: A Mathematical Modeling Study
Ashwani Rawat, Ramachandran T., Yogesh Chandra Gupta, Manoj Kumar Mishra, Gabriela Michael and Sabyasachi Pramanik
14.1 Introduction
14.2 Models of Component Agricultural Systems
14.3 Present-Day Farming System Frameworks in Relation to Certain Application Situations
14.4 Discussion
References
15. Simulating the Effects of Agricultural Land Fragmentation on Farm Efficiency: A Mathematical Modeling Analysis
Diksha Nautiyal, Manjunath H. R., Praveen Kumar Singh, Umesh Kumar Tripathi, Saurabh Raj and Soma Bag
15.1 Introduction
15.2 Conceptual Foundation
15.3 Resources and Techniques Household Polls
15.4 Results
15.5 Discussion
15.6 Conclusions
References
16. Simulating the Effects of Land Use Policies on Agricultural Productivity: A Mathematical Modeling Perspective
Vinaya Kumar Yadav, Sushila Arya, Asha Rajiv R., Devendra Pal Singh, Siddharth Ranka and Dharmesh Dhabliya
16.1 Introduction
16.2 Upcoming Applications of NextGen Farming Frameworks
16.3 Envisioned Consumers of the Application Chain Beneficiaries
16.4 Conclusion and Research Plan
References
17. Quantifying the Economic Benefits of Agricultural Extension Services: A Mathematical Modeling Analysis
Rajeev Kumar, Satendra Kumar, Pradeepa P., Akanchha Singh, Karun Sanjaya and Ankur Gupta
17.1 Introduction
17.2 Creating New Models for the Future: A Demand-Driven, Prospective Strategy
17.3 Potential Improvements to Model Elements
17.4 Conclusions
References
18. Modeling the Impact of Agricultural Investment Incentives on Rural Development: Legal and Economic Perspectives
Dal Chandra, Manoj Kumar Mishra, Ankit Pant, Ahmadi Begum, Sukhvinder Singh Dari and Dharmesh Dhabliya
18.1 Introduction
18.2 Approach
18.3 Conversation
18.4 Conclusion
References
19. Optimizing Harvest Scheduling in Agriculture: A Mathematical Modeling Approach Considering Legal Restrictions
Heejeebu Shanmukha Viswanath, Umesh Kumar Tripathi, Minnu Sasi, Kishore Kumar Pedapenki, Prashant Dhage and Ankur Gupta
19.1 Initialization
19.2 Structure of the System
19.3 Irrigation Community Event
19.4 Assessment and Authentication
19.5 Conclusions
References
20. Quantifying the Economic Benefits of Agricultural Data Sharing: A Mathematical Modeling Perspective
Aruno Raj Singh, Vinaya Kumar Yadav, Laishram Zurika, Dasarathy A. K., Abhishekh Benedict and Dharmesh Dhabliya
20.1 Introduction
20.2 Model for Data Mining Process
20.3 Techniques for Machine Learning
20.4 Website Tools
20.5 Case Study: Grading of Mushrooms
20.6 Conclusion
References
Index
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