The Digital Agricultural Revolution E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Fachliteratur
- Sprache: Englisch
THE DIGITAL AGRICULTURAL REVOLUTION The book integrates computational intelligence, applied artificial intelligence, and modern agricultural practices and will appeal to scientists, agriculturists, and those in plant and crop science management. There is a need for synergy between the application of modern scientific innovation in the area of artificial intelligence and agriculture, considering the major challenges from climate change consequences viz. rising temperatures, erratic rainfall patterns, the emergence of new crop pests, drought, flood, etc. This volume reports on high-quality research (theory and practice including prototype & conceptualization of ideas, frameworks, real-world applications, policy, standards, psychological concerns, case studies, and critical surveys) on recent advances toward the realization of the digital agriculture revolution as a result of the convergence of different disruptive technologies. The book touches upon the following topics which have contributed to revolutionizing agricultural practices. * Applications of Artificial Intelligence in Agriculture (AI models and architectures, system design, real-world applications of AI, machine learning and deep learning in the agriculture domain, integration & coordination of systems and issues & challenges). * IoT and Big Data Analytics Applications in Agriculture (theory & architecture and the use of various types of sensors in optimizing agriculture resources and final product, benefits in real-time for crop acreage estimation, monitoring & control of agricultural produce). * Robotics & Automation in Agriculture Systems (Automation challenges, need and recent developments and real case studies). * Intelligent and Innovative Smart Agriculture Applications (use of hybrid intelligence in better crop health and management). * Privacy, Security, and Trust in Digital Agriculture (government framework & policy papers). * Open Problems, Challenges, and Future Trends. Audience Researchers in computer science, artificial intelligence, electronics engineering, agriculture automation, crop management, and science.
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Table of Contents
Cover
Title Page
Copyright
Preface
1 Scope and Recent Trends of Artificial Intelligence in Indian Agriculture
1.1 Introduction
1.2 Different Forms of AI
1.3 Different Technologies in AI
1.4 AI With Big Data and Internet of Things
1.5 AI in the Lifecycle of the Agricultural Process
1.6 Indian Agriculture and Smart Farming
1.7 Advantages of Using AI in Agriculture
1.8 Role of AI in Indian Agriculture
1.9 Case Study in Plant Disease Identification Using AI Technology—Tomato and Potato Crops
1.10 Challenges in AI
1.11 Conclusion
References
2 Comparative Evaluation of Neural Networks in Crop Yield Prediction of Paddy and Sugarcane Crop
2.1 Introduction
2.2 Introduction to Artificial Neural Networks
2.3 Application of Neural Networks in Agriculture
2.4 Importance of Remote Sensing in Crop Yield Estimation
2.5 Derivation of Crop-Sensitive Parameters From Remote Sensing for Paddy and Sugarcane Crops
2.6 Neural Network Model Development, Calibration and Validation
2.7 Conclusion
References
3 Smart Irrigation Systems Using Machine Learning and Control Theory
3.1 Machine Learning for Irrigation Systems
3.2 Control Theory for Irrigation Systems
3.3 Conclusion and Future Directions
References
4 Enabling Technologies for Future Robotic Agriculture Systems: A Case Study in Indian Scenario
4.1 Need for Robotics in Agriculture
4.2 Different Types of Agricultural Bots
4.3 Existing Agricultural Robots
4.4 Precision Agriculture and Robotics
4.5 Technologies for Smart Farming
4.6 Impact of AI and Robotics in Agriculture
4.7 Unmanned Aerial Vehicles (UAV) in Agriculture
4.8 Agricultural Manipulators
4.9 Ethical Impact of Robotics and AI
4.10 Scope of Agribots in India
4.11 Challenges in the Deployment of Robots
4.12 Future Scope of Robotics in Agriculture
4.13 Conclusion
References
5 The Applications of Industry 4.0 (I4.0) Technologies in the Palm Oil Industry in Colombia (Latin America)
5.1 Introduction
5.2 Methodology
5.3 Results Analysis
5.4 Colombia PO Industry
5.5 The PO Industry and the Circular Economy
5.6 Conclusion
5.7 Further Recommendations for the Colombian PO Industry
Acknowledgments
References
6 Intelligent Multiagent System for Agricultural Management Processes (Case Study: Greenhouse)
6.1 Introduction
6.2 Modern Agricultural Methods
6.3 Internet of Things Applications in Smart Agriculture
6.4 Artificial Intelligence
6.5 MAS
6.6 Design and Implementation
6.7 Analysis and Discussion
6.8 Conclusion
References
7 Smart Irrigation System for Smart Agricultural Using IoT: Concepts, Architecture, and Applications
7.1 Introduction
7.2 Irrigation Systems
7.3 IoT
7.4 IoT Applications in Agriculture
7.5 IoT and Water Management
7.6 Introduction to the Implementation
7.7 Analysis and Discussion
7.8 Conclusion
References
8 The Internet of Things (IoT) for Sustainable Agriculture
8.1 Introduction
8.2 ICT in Agriculture
8.3 Internet of Things in Agriculture and Allied Sector
8.4 Geospatial Technology
8.5 Summary and Conclusion
References
9 Advances in Bionic Approaches for Agriculture and Forestry Development
9.1 Introduction
9.2 Precision Farming
9.3 Powerful Role of Drones in Agriculture
9.4 Nanobionics in Plants
9.5 Role of Nanotechnology in Forestry
9.6 Conclusion
References
10 Simulation of Water Management Processes of Distributed Irrigation Systems
10.1 Introduction
10.2 Modeling of Water Facilities
10.3 Processing and Conducting Experiments
10.4 Conclusion
References
11 Conceptual Principles of Reengineering of Agricultural Resources: Open Problems, Challenges and Future Trends
11.1 Introduction
11.2 Modern Agronomy and Approaches for Environment Sustenance
11.3 International Federation of Organic Agriculture Movements (IFOAM) and Significance
11.4 Low Cost versus Sustainable Agricultural Production
11.5 Change of Trends in Agriculture
References
12 Role of Agritech Start-Ups in Supply Chain—An Organizational Approach of Ninjacart
12.1 Introduction
12.2 How Does the Chain Work?
12.3 Undisrupted Chain of Ninjacart During Pandemic-19
12.4 Conclusion
References
13 Institutional Model of Integrating Agricultural Production Technologies with Accounting and Information Systems
13.1 Introduction
13.2 Research Methodology
13.3 The General Model of a New Informational Paradigm of Agricultural Activities’ Organization
13.4 The Model of Institutional Interaction of Information Agents in Agricultural Production
13.5 Conclusions
References
14 Relevance of Artificial Intelligence in Wastewater Management
14.1 Introduction
14.2 Digital Technologies and Industrial Sustainability
14.3 Artificial Neural Networks and Its Categories
14.4 AI in Technical Performance
14.5 AI in Economic Performance
14.6 AI in Management Performance
14.7 AI in Wastewater Reuse
14.8 Conclusion
References
15 Risks of Agrobusiness Digital Transformation
15.1 Modern Global Trends in Agriculture
15.2 The Global Innovative Differentiation
15.3 National Indicative Planning of Innovative Transformations
15.4 Key Myths and Risks of Digitalization of Agrobusiness
15.5 Examples of Use of Digital Technologies in Agriculture
15.6 Imperatives of Transforming the Region into a Cost-Effective Ecosystem of Digital Highly Productive and Risk-Free Agriculture
15.7 Conclusion
References
16 Water Resource Management in Distributed Irrigation Systems
16.1 Introduction
16.2 Types of Mathematical Models for Modeling the Process of Managing Irrigation Channels
16.3 Building a River Model
16.4 Spatial Hierarchy of River Terrain
16.5 Organizations in the Structure of Water Resources Management
16.6 Conclusion
References
17 Digital Transformation via Blockchain in the Agricultural Commodity Value Chain
17.1 Introduction
17.2 Precision Agriculture for Food Supply Security
17.3 Blockchain Technology Practices and Literature Reviews on Food Supply Chain
17.4 Agricultural Sector Value Chain Digitalization
17.5 Conclusion
References
18 Role of Start-Ups in Altering Agrimarket Channel (Input-Output)
18.1 Introduction
18.2 Agriculture Supply Chain Management
18.3 How Start-Ups Fill the Concerns and Gaps in Agri Input Supply Chain?
18.4 Output Supply Chain
18.5 How Start-Ups are Filling the Concerns and Gaps in Agri Output Supply Chain?
18.6 Conclusion
References
19 Development of Blockchain Agriculture Supply Chain Framework Using Social Network Theory: An Empirical Evidence Based on Malaysian Agriculture Firms
19.1 Introduction
19.2 Literature Review
19.3 Methodology
19.4 Results and Discussion
19.5 Conclusion
19.6 Acknowledgment
References
20 Potential Options and Applications of Machine Learning in Soil Science
20.1 Introduction: A Deep Insight on Machine Learning, Deep Learning and Artificial Intelligence
20.2 Application of ML in Soil Science
20.3 Classification of ML Techniques
20.4 Artificial Neural Network
20.5 Support Vector Machine
20.6 Conclusion
References
Index
Wiley End User License Agreement
List of Tables
Chapter 1
Table 1.1 Distinguishing feature of subfields of AI.
Chapter 2
Table 2.1 Land use of the study area in the year 2014–2015.
Table 2.2 Sample normalized input data of FFBPNN yield estimation model of paddy...
Table 2.3 Sample and training result of FFBPNN yield prediction model of paddy c...
Table 2.4 Statistical analysis of neural network training and testing of season ...
Chapter 3
Table 3.1 Abbreviations of machine learning techniques.
Table 3.2 Recent machine learning practices of the irrigation systems.
Table 3.3 Abbreviations of control theory techniques.
Table 3.4 Recent control theory applications of the irrigation systems.
Table 3.5 Aforementioned remote control extensions for irrigation systems.
Chapter 5
Table 5.1 Top 10 PO world production.
Table 5.2 Top 10 PO world exports 2020.
Table 5.3 Top 10 palm oil world imports 2020.
Table 5.4 Focal articles selected.
Table 5.5 Top 10 journals publishing the focal articles, citations, and impact f...
Table 5.6 Top 10 most-cited focal articles.
Table 5.7 Colombia’s palm oil plantations.
Table 5.8 Colombia’s palm oil technologies.
Chapter 6
Table 6.1 Extract from system agents.
Table 6.2 Extract agents from the solution.
Chapter 7
Table 7.1 Value in% of field efficiency [33].
Table 7.2 AT commands dedicated to the SMS service.
Chapter 9
Table 9.1 List of various application of UAV in agriculture sector.
Table 9.2 Agricultural and forestry applications of e-noses.
Chapter 10
Table 10.1 Average annual water consumption in Karasu, Parkent, Handam in 2011–2...
Chapter 14
Table 14.1 AI application in pollutant removal during wastewater treatment.
Chapter 15
Table 15.1 The main features of technological systems [20].
Chapter 16
Table 16.1 Spatial hierarchy of river terrain.
Chapter 17
Table 17.1 Comparison of ZIHA with alternative methods.
Table 17.2 DITAP phases.
Table 17.3 Food supply chain initiatives based on blockchain.
Chapter 19
Table 19.1 Firm profile (n=18).
Table 19.2 Respondent profile (n=18).
Table 19.3 Degree of centrality and betweenness report.
List of Illustrations
Chapter 1
Figure 1.1 Different forms of AI [3].
Figure 1.2 AI versus ML versus ANN versus DL.
Figure 1.3 Types of machine learning.
Figure 1.4 Generic methodology in building a model using machine learning algori...
Figure 1.5 Cotton leaf disease using DT algorithm.
Figure 1.6 Steps involved in advanced technologies.
Figure 1.7 The lifecycle of the agriculture process.
Figure 1.8 Various sensors for smart farming.
Figure 1.9 Late blight and leaf spot of tomato crop.
Figure 1.10 Early blight and stem rot of potato crop.
Chapter 2
Figure 2.1 Architecture of artificial neural network (original figure).
Figure 2.2 Location details of Krishna Central Delta (original figure).
Figure 2.3 Illustration of collection of ground sample points using EpiCollect a...
Figure 2.4 (a, b). APAR and CSWI maps of KCD on October 14, 2015 (original figur...
Figure 2.5 Synoptic view of spatial distribution of sample points of crop collec...
Figure 2.6 Exporting the attribute values of the points to excel (original figur...
Figure 2.7 Architecture of the proposed FFBPNN model (original figure).
Figure 2.8 Relative error between observed and predicted crop yields of training...
Figure 2.9 Relative error between observed and predicted crop yields of training...
Figure 2.10 Scatter plots of actual and FFBP NN model predicted yield of sugarca...
Figure 2.11 Scatter plots of actual and FFBP NN model predicted yield of sugarca...
Figure 2.12 Final predicted yield map of paddy during 2015 (original figure).
Figure 2.13 Final predicted yield map of sugarcane during 2015 (original figure)...
Chapter 3
Figure 3.1 The general learning structure of ML systems.
Figure 3.2 The control mechanisms of the irrigation systems.
Figure 3.3 Control diagram of the irrigation system.
Figure 3.4 Remote control diagram of the irrigation system.
Figure 3.5 Time-line of irrigation decision systems.
Chapter 4
Figure 4.1 Different types of robots in agriculture.
Figure 4.2 Various tasks of field robot.
Chapter 5
Figure 5.1 PRISMA analysis. Source: Original.
Figure 5.2 Publication years analysis. Source: Clarivate Analytics.
Figure 5.3 Top 10 research areas. Source: Clarivate Analytics.
Figure 5.4 Technologies used for PO management. Source: Original.
Figure 5.5 Word cooccurrence map (full counting). Source: VOSviewer.
Figure 5.6 Map of authors. Source: VOSviewer.
Figure 5.7 Citations. Source: VOSviewer.
Figure 5.8 Cocitations. Source: VOSviewer.
Figure 5.9 Colombia PO production. Source: U.S. Department of Agriculture.
Figure 5.10 Top 5 Colombian departments with the highest palm oil production. So...
Chapter 6
Figure 6.1 Geographical position and location of Adrar Wilaya.
Chapter 7
Figure 7.1 Runoff irrigation.
Figure 7.2 Sprinkler irrigation.
Figure 7.3 IoT system components diagram.
Figure 7.4 Presentation of the specifications.
Figure 7.5 Arduino board.
Figure 7.6 Arduino interface.
Chapter 8
Figure 8.1 Internet evolution. Source: [2].
Figure 8.2 IoTs and today’s innovative farming industry. Source: data-flair.trai...
Figure 8.3 IoT proliferation. source: [2].
Figure 8.4 People-to-IoT ratio. Sources: Cisco IBSG, Jim Cicconi, AT&T, Stev...
Figure 8.5 The future of agriculture with IoT and smart devices (a-c). Source: [...
Figure 8.6 The general system architecture. Source: [2].
Figure 8.7 IoT in smart farming. Source: [2].
Figure 8.8 Agricultural drones. Source: [2].
Figure 8.9 IoT in livestock management. Source: [2].
Figure 8.10 Smart greenhouse. Source: [2].
Figure 8.11 Remote sensing. Source: GrindGIS.com.
Figure 8.12 GIS and the agro-ecology. Source: GrindGIS.com.
Figure 8.13 GPS application in the farm field. Source: GrindGIS.com.
Figure 8.14 Drought monitoring. Source: [2].
Figure 8.15 Crop X is a hardware and software system that measures soil moisture...
Chapter 9
Figure 9.1 Precision agriculture cycle. Image source: Modified from Comparetti e...
Figure 9.2 Drones flying over the field and collecting images (source image: unm...
Figure 9.3 Data collection from agricultural drones occurs in the stages listed ...
Figure 9.4 Image captured by a drone give NDVI field map (Source image: unmanned...
Figure 9.5 Drones used for spraying pesticides (source image, www.Krishigaran.co...
Figure 9.6 Nanotechnology’s application in the wood product industry.
Chapter 10
Figure 10.1 Constant cross-section channel diagram.
Figure 10.2 Irrigation canal diagram.
Figure 10.3 Irrigation canal section diagram.
Figure 10.4 Dynamics of water consumption.
Chapter 11
Figure 11.1 Scheme for the formation of the rational use of resources.*
Figure 11.2 The system of internal interdependence and subordination of economic...
Chapter 12
Figure 12.1 Traditional model of SCM vs Start-up model of SCM.
Figure 12.2 Flow chart showing SCM of Ninjacart.
Figure 12.3 Flowchart showing operations involved in preprocurement stage.
Figure 12.4 Flowchart showing operations involved in postprocurement stage.
Figure 12.5 Mind map of Ninjacart.
Figure 12.6 Harvest the Farms model of Ninjacart.
Figure 12.7 Flowchart showing future goal of sustainability of Ninjacart.
Chapter 13
Figure 13.1 General model of the informational paradigm of organizing agricultur...
Figure 13.2 The model of institutional interaction of information agents in agri...
Chapter 14
Figure 14.1 Various AI techniques used in wastewater treatment (original diagram...
Chapter 15
Figure 15.1 Evolution of competitive development of the national economy at the ...
Figure 15.2 Competitiveness of the national economy based on innovation and inve...
Figure 15.3 Conceptual scheme of the national system of indicative planning of i...
Figure 15.4 Conceptual scheme of optimizing Ukrainian financial and credit syste...
Figure 15.5 Algorithm for using “limited experiments” to implement measures with...
Figure 15.6 Institutional Block “Indicative planning – innovative projecting” Or...
Chapter 16
Figure 16.1 Graphical relationship between strings.
Figure 16.2 Graphical links between the flow of two rivers.
Chapter 17
Figure 17.1 Doktar: Agricultural information services, Source: [8].
Figure 17.2 ImeceMobil: Solutions for herbal production and livestock-related pr...
Figure 17.3 Top 10 in Contract Farming in Turkey, Source: [10].
Figure 17.4 Sector-based blockchain technology usage, Source: [17].
Figure 17.5 Smart contract system, Source: [33].
Figure 17.6 Agricultural sector value chain digitalization phases, Source: Draw...
Figure 17.7 Smart contract: electronic warehouse receipts (EWR) trading ecosyste...
Figure 17.8 Digital Commodity Ecosystem, Source: Prepared by the authors.
Chapter 18
Figure 18.1 Flowchart of agrisupply chain.
Figure 18.2 Traditional input supply chain.
Figure 18.3 Existing models of supply chain.
Figure 18.4 Start-up input supply chain model.
Figure 18.5 e-commerce entry in agri-input sector. Source:/news.agropages.com/Ne...
Figure 18.6 Start-up input model in eliminating middlemen.
Figure 18.7 Traditional output supply chain.
Figure 18.8 APMC model of output supply chain.
Figure 18.9 Hub & spoke model of supply chain.
Figure 18.10 Value chain model of supply chain.
Figure 18.11 Start-up output model in eliminating middlemen.
Chapter 19
Figure 19.1 Blockchain agriculture supply chain management framework. Source: Or...
Figure 19.2 Social network interactions of Malaysian agriculture supply chain (n...
Figure 19.3 Centrality of social network for Malaysian agriculture supply chain ...
Figure 19.4 Betweenness of social network for Malaysian agriculture supply chain...
Figure 19.5 Social network of Malaysian blockchain-based agriculture supply chai...
Figure 19.6 Social network degree of importance for Malaysian blockchain-based a...
Chapter 20
Figure 20.1 Prospects and applications of ML in soil science.
Figure 20.2 Schematic representation of Architecture of Artificial Neural Networ...
Figure 20.3 Scatter plot using soil microbial biomass and soil organic carbon co...
Guide
Cover
Table of Contents
Title Page
Copyright
Preface
1 Scope and Recent Trends of Artificial Intelligence in Indian Agriculture
Index
End User License Agreement
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Scrivener Publishing
100 Cummings Center, Suite 541J
Beverly, MA 01915-6106
Publishers at Scrivener
Martin Scrivener ([email protected])
Phillip Carmical ([email protected])
The Digital Agricultural Revolution
Innovations and Challenges in Agriculture through Technology Disruptions
Edited by
Roheet Bhatnagar
Nitin Kumar Tripathi
Nitu Bhatnagar
and
Chandan Kumar Panda
This edition first published 2022 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-119-82333-9
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10 9 8 7 6 5 4 3 2 1
Preface
The practice of agriculture began in ancient civilizations and farmers have always contributed to nation-building by growing produce to feed an ever-growing population. Moreover, agriculture is the foundation of an economy, providing livelihoods for millions of farmers. Therefore, there is a need for synergy between application of modern scientific innovation in the area of artificial intelligence (AI) and agriculture, while taking into consideration the major challenge brought on by climate change, viz., rising temperatures, erratic rainfall patterns, emergence of new crop pests, droughts, floods, etc. The intent of this edited volume is to report high-quality research (practical theory, including prototype and conceptualization of ideas, frameworks, real-world applications, policies, standards, psychological concerns, case studies and critical surveys) on recent advances toward the realization of a digital agricultural revolution resulting from the convergence of different disruptive technologies.
This book highlights the latest achievements in the field of modern agriculture, which is highly driven by technology and aimed at sustainable agriculture. In it is a collection of original contributions by researchers/ academicians from across the globe regarding state-of-the-art solutions using newer methods to enhance and improve crops for smart agriculture. These solutions are arrived at by various means, as indicated in the topics covered in the twenty chapters described below.
–
Chapter 1
presents an overview of how AI helps to increase socio-economic and environmental sustainability in the Indian agricultural sector. It also highlights the AI practices incorporated by farmers in India with small and medium-sized agricultural lands.
–
Chapter 2
describes the high efficacy of using remote sensing images and neural network models to generate accurate crop yield maps and offers proof of the significant superiority of neural network models over conventional methods.
–
Chapter 3
discusses the use of intelligent irrigation systems, which have recently gained importance in terms of efficient cultivation of plants and the correct use of Earth’s water. The use of machine learning and control methods in plant growth modeling and irrigation systems is also explained. The chapter ends with a discussion of current problems along with possible future implementation of new approaches to solve them.
–
Chapter 4
provides insight into the opportunities presented by the use of robots in agriculture, known as agribots, and focuses on the advancements in different types of agribots in terms of sensing, mobility, path planning, and manipulation. It also talks about the status and progress of robots in Indian agriculture, concentrating on Indian-based robotic startups and case studies involving the use of robots in harvesting crops along with the challenges faced when deploying robots in the field.
–
Chapter 5
delves into the Colombian palm oil (PO) industry. The contribution of this study is twofold: First, it provides a more comprehensive review of the PO industry technology literature based on Scopus and Clarivate Analytics, using the reporting checklist of preferred reporting items for systematic reviews and meta-analyses (PRISMA). Second, as far as the authors know, this is one of the first studies to address the technological solutions applied by Colombia’s PO producers and aims to help fill this research gap.
–
Chapter 6
presents a case on smart agriculture and discusses intelligent agriculture in a greenhouse-based multi-agent system (MAS), which is made up of several agents located in an environment that interact according to some defined relationships. In this work, each part of the greenhouse environment is represented by one or more agent, with each agent coordinating with other agents to achieve set goals. In addition, it discusses the society of agents in which two types of agents can be found: 1) reactive agents characterized by simple behavior, whose mission it is to perform tasks that do not require intelligent reasoning, and 2) cognitive or intelligent agents, which are tasked with performing more complex missions and require reasoning to make good decisions.
–
Chapter 7
is a study on the use of automatic and intelligent methods in the management of irrigation of agricultural land. Among these technologies are artificial intelligence and the Internet of Things (IoT), which are used to optimize the management of irrigation water in agricultural lands. The elements of the agricultural system and its environment are presented by things in direct contact with each other by relying on information and communication technology (ICT).
–
Chapter 8
discusses how modern agriculture has become knowledge intensive and how improved access to and availability of information and communication technologies (ICTs), especially cell phones, computers, radio, internet, and social media, has created many more opportunities for multi-format information gathering, processing, storage, retrieval, management and sharing.
–
Chapter 9
presents an overview of nanotechnology and nanosensors in forestry and agriculture, including its use in forest health protection, forest management, wood and paper processing, and chemotaxonomy. The nanotechnology sector has best applied this technology in precision farming by developing nanobionic plants by inserting nanosensors into living plants that can be utilized to communicate as infrared devices and for sensing objects in the plant’s environment. Therefore, the nanobionics approach has opened a new vista into plant nanomaterial research. Some nanobionics approaches for agriculture and forestry development are also briefly discussed.
–
Chapter 10
is all about mathematical models of the water resources management process of canals in the middle reaches of the Chirchik River, which were developed using simplified differential equations of Saint Venant in partial derivatives to model the necessary conditions for optimizing water distribution. An algorithm for solving the problem of optimal water resources management of distributed irrigation canals was also developed.
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Chapter 11
discusses various principles of reengineering of agricultural resources and throws light on open problems, challenges, and future trends.
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Chapter 12
shows how the supply chain management method is used for planning maintenance strategy, storing products, moving material through the organization and its distribution channel, which leads directly to maximum profits through cost-saving fulfilment of orders. A simple supply chain acts as a bridge between demand and supply. Startups are bringing a new shape to the agri-supply chain by using new-age technologies like AI, machine learning, IoT and blockchain management, that procure directly from farm gates and supply to retailers.
–
Chapter 13
discusses the need for an institutional approach of using digital techniques in modern agrarian production. This approach is illustrative of the synergy of economic, ecological, and social effectiveness as a progressive direction in which the development of a global economic system can be worked out. A general model was used to determine a new organization of the informational paradigm of agricultural activities based on the agility of the knowledge and analytical data being transferred into the value of information.
–
Chapter 14
provides a comprehensive analysis of four aspects of AI implementation in treatment of wastewater: management, technology, reuse and economics of wastewater. It also provides an insight into the future prospects of the use of AI in the treatment of wastewater, which, in complex practical applications, simultaneously addresses pollutant removal, water reuse and management and cost-efficient challenges.
–
Chapter 15
presents methods for assessing the impact of digital transformation risks on the business model of agricultural enterprises. Industry 4.0 is accompanied by the rapid transformation of several sectors under the influence of “breakthrough” digital innovations such as blockchain, IoT, AI, and augmented reality.
–
Chapter 16
presents a unified systematic approach to the issue of modeling the dynamics of water management facilities. There is a wide range of mathematical models of individual objects of different complexity, which is why the choice of mathematical models that will describe the complex processes of water distribution in water management systems with the required degree of accuracy is a very problematic task.
–
Chapter 17
showcases the use of blockchain technology that has become a phenomenon in recent years and is evolving into a form that institutionalized organizations can benefit from. The IoT integrates blockchain technology into the agricultural sector and provides the automation of the control mechanisms in the agricultural food supply chain. The study evaluated in this chapter utilizes technology in various forms, from farm to fork. Furthermore, a Fintech solution framework via blockchain created for digitalization of the agricultural commodity value chain is presented that secures the contract creation, transfer, and redemption (burn) processes.
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Chapter 18
discusses how new-age entrepreneurs are using technological innovations to address supply chain challenges and unlock value across it. India’s startup agricultural ecosystem is mushrooming, with over 450 startups that are currently operational, over 50% of which are focused on making the supply chain more efficient by improving market linkages. Inputs play a crucial role in extracting higher yields. The existing delivery system is not appropriate due to poor supply, lack of subsidies, improper infrastructure, lack of farm credit, and poor delivery systems.
–
Chapter 19
is about the adoption of blockchain technology in the Malaysian agriculture sector and proposes a framework of blockchain agriculture supply chain management. As the blockchain supply chain framework in the agriculture sector is still limited, social network theory tends to be used in the development of the framework. This chapter has collected quantitative survey and social network data from firms registered in the Federation of Malaysian Manufacturers that operate in the agriculture sector. The demographic profiles were analyzed through IBM SPSS 26 and the social network data was analyzed through Social Network Visualizer.
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Chapter 20
discusses the use of machine learning algorithms to study soil fertility, salinity, dynamics, and the relationship of soil organic carbon with the environment, spatial and temporal variation of soil water content, soil and water pollution, soil formation processes, soil classification, prediction, nutrient availability, etc.
Our intent in writing this book was to provide a foundation of comprehensive knowledge for others to build on; therefore, it is our sincerest hope that it will prove to be beneficial to people from different domains. We hope that you find it useful and engaging as you continue your journey to expand the sphere of human knowledge, if only by an inch.
We are thankful to all the authors and co-authors of every chapter who have contributed their knowledge in the form of quality manuscripts for the benefit of others.
The editorsDr. Roheet BhatnagarDr. Nitin Kumar TripathiDr. Chandan Kumar PandaDr. Nitu BhatnagarApril 2022
1Scope and Recent Trends of Artificial Intelligence in Indian Agriculture
X. Anitha Mary1, Vladimir Popov2,3, Kumudha Raimond4, I. Johnson5 and S. J. Vijay6*
1Department of Robotics Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India
2Additive Manufacturing, Technion-Israel Institute of Technology, Haifa, Israel
3Ural Federal University, Ekaterinburg, Russia
4Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India
5Department of Plant Pathology, TamilNadu Agricultural University, Coimbatore, India
6Department of Mechanical Engineering, Karunya Institute of Technology and Sciences, Coimbatore, Tamil Nadu, India
Abstract
Agriculture is the economic backbone of India. About 6.4% of the total world’s economy relies on agriculture [1]. Automation in agriculture is the emerging sector as there is an increase in food demand and employment. The traditional ways used by farmers are not sufficient to fulfill the demands and it is high time that newer technologies are implementing in the agricultural sector. Artificial Intelligence (AI) is one of the emerging and promising technologies where intelligence refers to developing and utilizing human-level thinking machines through learning algorithms programmed to solve critical problems. Artificial Intelligence plays an important role in supporting agriculture sectors with the objectives of boosting productivity, efficiency, and farmers’ income. This chapter focuses on how AI helps in increasing the socioeconomic and environmental sustainability in the Indian agricultural sector. Also, it highlights the AI practices in India incorporated by farmers having small and medium-size agricultural lands.
Keywords: Indian agriculture, Artificial Intelligence, farmers
1.1 Introduction
Artificial Intelligence (AI) is a broad field of computer science that focuses on creating intelligent machines that can accomplish activities that would normally need human intelligence. Although AI is a multidisciplinary field with many methodologies, advances in machine learning (ML) and deep learning (DL) [4] are causing a paradigm shift in nearly every sector of the IT industry.
One of the oldest occupations in the world is farming and agriculture. It has a significant impact on the economy. Climate variations also play an influence in the agriculture lifecycle. Climate change is a result of increasing deforestation and pollution, making it difficult for farmers to make judgments about which crop to harvest. Nutrient insufficiency can also cause crops to be of poor quality [37]. Weed control has a significant impact and can lead to greater production costs. The above traditional farming can be replaced by using modern technology with AI.
1.2 Different Forms of AI
Agriculture is extremely important, and it is the primary source of income for almost 58% of India’s population [2]. However, it lacks support and suffers from a variety of factors, such as groundwater depletion, erratic monsoons, droughts, plant diseases, and so on. To detect the relationship between influencing factors with crop yield and quality, a variety of tools and approaches have been identified. The impact of recent technological advancements in the field of AI is significant. Recently, large investors have begun to capitalize on the promise of these technologies for the benefit of Indian agriculture. Smart farming and precision agriculture (PA) are ground-breaking science and technological applications for agriculture growth. Farmers and other agricultural decision makers are increasingly using AI-based modeling as a decision tool to increase production efficiency.
Artificial Intelligence is silently entering Indian agriculture and impacting society to a greater extent. There are three forms of AI, namely Artificial Narrow Intelligence (ANI), Artificial General Intelligence (AGI), and Artificial Super Intelligence (ASI) [3] as shown in Figure 1.1 Artificial Narrow Intelligence as the name suggests uses computer programming to do a specific task. Artificial General Intelligence refers to a machine that can think like a human and perform huge tasks. Artificial Super Intelligence is designed to think beyond humans. Artificial Narrow Intelligence is mainly used in agriculture to do some specific tasks, such as identification of diseases in leaf, optimization in irrigation, the optimal moisture content in crops, and so on, using AI techniques. The different forms of AI are shown in Figure 1.1.
Figure 1.1 Different forms of AI [3].
1.3 Different Technologies in AI
There are many subfields, such as ML, Artificial Neural Network (ANN), and DL, as shown in Figure 1.2. The distinguishing features of these subfields are shown in Table 1.1.
Figure 1.2 AI versus ML versus ANN versus DL.
Table 1.1 Distinguishing feature of subfields of AI.
AI
AI is a technology that allows us to build intelligent systems that mimic human intelligence.
ML
ML is an AI discipline that allows machines to learn from previous data or experiences without having to be explicitly programmed.
ANN
ANN depends on algorithms resembling the human brain.
DL
DL algorithms automatically build a hierarchy of data representations using the low- and high-level features.
1.3.1 Machine Learning
A subset of AI focuses on algorithm development by learning from experience and helps in the improvement of decision making with greater accuracy. The categories and the corresponding tasks are shown in Figure 1.3. Supervised, unsupervised, and reinforcement are the three main learning paradigms. Supervised is the most prevalent training paradigm for developing ML models for both classification and regression tasks [27]. It finds the relationship between the input and target variables. Some of the supervised learning algorithms are support vector machine (SVM), logistic regression, Decision Tree (DT), random forest, and so on. Unsupervised learning is often used for clustering and segmentation tasks. This method does not require any target variable to group the input data sets. Some of the examples are K-means, hierarchical, density, grid clustering, and so on. Reinforcement learning corresponds to responding to the environment and deciding the right action to complete the assignment with maximum reward in a given application. It finds its applications in a real-time environment.
Figure 1.3 Types of machine learning.
Figure 1.4 Generic methodology in building a model using machine learning algorithms.
In ML, training is performed with a huge amount of data to get accurate decisions or predictions. The general steps involved in building an ML model are shown in Figure 1.4.
1.3.1.1 Data Pre-processing
It is a process of converting raw data into a usable and efficient format.
1.3.1.2 Feature Extraction
Before training a model, most applications need first transforming the data into a new representation. Applying pre-processing modifications to input data before presenting it to a network is almost always helpful, and the choice of pre-processing will be one of the most important variables in determining the final system’s performance. The reduction of the dimensionality of the input data is another key method in which network performance can be enhanced, sometimes dramatically. To produce inputs for the network, dimensionality reductions entail creating linear or nonlinear combinations of the original variables. Feature extraction is the process of creating such input combinations, which are frequently referred to as features. The main motivation for dimensionality reduction is to help mitigate the worst impacts of high dimensionality.
1.3.1.3 Working With Data Sets
The most popular method is to split the original data into two or more data sets at random or using statistical approaches. A portion of the data is used to train the model, whereas a second subset is used to assess the model’s accuracy. It is vital to remember that while in training mode, the model never sees the test data. That is, it never uses the test data to learn or alter its weights. The training data is a set of data that represent the data that the ML will consume to answer the problem it was created to tackle. In certain circumstances, the training data have been labeled—that is, it has been “tagged” with features and classification labels that the model will need to recognize. The model will have to extract such features and group them based on their similarity if the data is unlabeled. To improve the generalization capability of the model, the data set can be divided into three sets according to their standard deviation: training sets, validation sets, and testing sets. The validation set is used to verify the network’s performance during the training phase, which in turn is useful to determine the best network setup and related parameters. Furthermore, a validation error is useful to avoid overfitting by determining the ideal point to stop the learning process.
1.3.1.4 Model Development
The ultimate goal of this stage is to create, train, and test the ML model. The learning process is continued until it provides an appropriate degree of accuracy on the training data. A set of statistical processing processes is referred to as an algorithm. The type of algorithm used is determined by the kind (labeled or unlabeled) and quantity of data in the training data set, as well as the problem to be solved. Different ML algorithms are used concerning labeled data. The ML algorithm adjusts weights and biases to give accurate results.
i. Support Vector Machine
Support vector machine finds out an optimum decision boundary to divide the linear data into different classes. It is also useful to classify nonlinear data by employing the concept of kernels to transform the input data into higher dimension data. The nonlinear data will be categorized into different classes in the new higher-dimensional space by finding out an optimum decision surface.
ii. Regression Algorithm
Regression methods, such as linear and logistic regression, are used to understand data relationships. Independent variables are used to predict the value of a dependent variable using linear regression. When the dependent variable is binary, such as x or y, logistic regression can be employed. The dependency of crop yield overirrigation and fertilization is an example of linear regression. Using temperature, nitrogen, phosphorous, and potassium content in the soil, rainfall, pH of the soil as independent variables; yield can be forecasted using multiple regression.
iii. Decision Tree
The most powerful and widely used tool for classification and prediction is the DT algorithm. A DT is a tree structure that resembles a flowchart, with each leaf node representing the outcome, an inside node indicating a feature (or attribute), and a branch representing a decision rule. In a DT, the root node is the uppermost node. A Top-Down technique is used to classify the instances by sorting them down the tree from the root to a leaf node, with the leaf node provides the classification label to the given data set. This process is called recursive partitioning. Figure 1.5 shows an example of the application of the DT algorithm for the identification of leaf disease in cotton crops.
iv. K-means Clustering
It uses categorization to determine the likelihood of a data point belonging to one of two groups based on its proximity to other data points. The first stage in the k-means clustering algorithm is to determine the number of clusters (K) that will be obtained as a final result. The cluster’s centroids are then chosen at random from a set of k items or objects. Based on a distance metric, all remaining items (objects) are assigned to their nearest centroid (mostly Euclidean Distance Metric). The algorithm then calculates the new mean value of each cluster. The term “centroid update” cluster is used to build this stage. Now that the centers have been recalculated, each observation is evaluated once more to see if it is closer to a different cluster. The cluster updated means are used to reassign all of the objects. The cluster assignment and centroid update processes are done iteratively until the cluster assignments do not change anymore (until a convergence criterion is met). That is, the clusters created in the current iteration are identical to those obtained in the prior iteration. Generally, K-means clustering is used in predicting crop yields.
Figure 1.5 Cotton leaf disease using DT algorithm.
v. Association Algorithm
Association algorithms look for patterns and links in data, as well as frequently occurring “if-then” correlations known as association rules. These restrictions are comparable to data mining rules.
1.3.1.5 Improving the Model With New Data
The final stage is to apply the model to new data and, in the best-case scenario, see how accurate and effective it becomes over time. The source of the new data will be determined by the problem to be solved.
1.3.2 Artificial Neural Network
ANNs resembles the human brain based on the principle that:
Information is processed by basic units known as neurons.
Signals are transmitted from one neuron to the next via connecting links.
Each connecting link has a weight associated with it, which amplifies the signal transmitted in a conventional neural network.
To determine its output signal, each neuron’s net input passes through the activation function.
One of the popular architectures of ANN is a Multiple-layer perceptron (MLP) which consists of input, hidden, and output layers. Multiple-layer perceptrons have been successfully trained in a supervised manner utilizing a widely used method known as the Error Back Propagation Algorithm to solve a variety of complex and diverse tasks. The input layer consists of nodes that receive information from external sources and passes this information to one or more hidden layers of computation nodes and an output layer of computation nodes. During the training phase, the output is calculated for every given input and compared with the desired output. Based on the error, the network is updated. During the testing phase, the network will calculate the output for any new input data. Each conclusion has a probability assigned to it. For the most part, ANN is thought to be a good answer to difficult situations. They solve intricate relationships between crop production and interconnected characteristics that linear systems can’t solve. Artificial Neural Networks are computer programs that simulate the functioning of the human brain. Artificial Neural Network is a task-based strategy that instructs the system to work based on an internal task rather than a computationally programmed task.
1.3.2.1 ANN in Agriculture
The major advantage of neural networks is their ability to predict and anticipate via parallel thinking. Artificial Neural Network can be taught instead of being extensively programmed. Artificial Neural Network was employed by Gliever and Slaughter [30] to distinguish weeds from crops. Maier and Dandy [31] used ANNs to forecast water resources factors. Song and He [32] combined expert systems and ANNs to forecast crop nutrient levels. Comax (COtton Management eXpert), an expert system, was effectively integrated with Gossym, a computer model, and cotton crop growth was simulated. This expert system was created to work continuously in cotton crop fields throughout the year. Comax considers three field parameters: irrigation timing, nitrogen content in the field, and cotton crop development.
1.3.3 Deep Learning for Smart Agriculture
Images make up a significant portion of the data collected by remote sensing. Images can provide a complete view of agricultural landscapes in many circumstances, and they can help with a range of problems. As a result, imaging analysis is an important research field in the agricultural realm, and picture identification/classification is done using intelligent data analysis approaches [33]. One such approach is DL. A deep neural network is a network that has numerous hidden layers, each of which refines the preceding layer’s output. Feature learning, or the automatic extraction of features from raw data, is a key advantage of DL. This architecture finds its applications in the computer vision field for image classification, object identification, picture segmentation, and so on.
Because of the more complicated models utilized in DL, which allow huge parallelization, it can tackle more complicated problems exceptionally well and quickly [34]. Many researchers used DL for fruit counting, predicting future parameters, such as yield production, soil moisture content, evapotranspiration, weed detection, weather prediction, and so on.
1.3.3.1 Data Pre-processing
A commonly used pre-processing step is image resizing to 60 × 60, 256 × 256, 128 × 128, and 96 × 96 pixels. Image pre-processing is also used to identify the region of interest through segmentation, background removal, conversion to grayscale, and so on.
1.3.3.2 Data Augmentation
Many computer vision tasks have shown that deep neural networks perform exceptionally well. However, to avoid overfitting, huge data have to be provided to perfectly model the training data. The goal of data augmentation techniques is to artificially increase the quantity of training images. By providing the model with a variety of data, it helps to improve the overall learning method and performance, as well as generalization capability. For small data sets, this augmentation method is critical for training DL models. Some of the popular data augmentation techniques are flipping, rotating, cropping, scaling, translation, Gaussian noise, color casting, and so on.
1.3.3.3 Different DL Models
Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are two types of DL models that are driving development in different areas including the agricultural field. Many other increasingly sophisticated architectures, such as AlexNet, VGG-16, VGG-19, ResNet, Inception-V3, DenseNet, and so on, have been developed. To apply such architectures to smaller data sets, some regularization techniques, like data augmentation, dropout, batch normalization, transfer learning, and pretraining, are implemented.
1.4 AI With Big Data and Internet of Things
Policymakers and industry leaders are turning to technology factors like Internet of Things (IoT), big data, analytics, and cloud computing to help them deal with the demands of rising food demand and climate change. Farmers can use big data to get detailed information on patterns of rainfall, fertilizer requirements, and more. This allows them to make decisions on which crops to sow for maximum profit and when to harvest. Farm yields are improved when the appropriate selections are made. Sensors have been integrated into farming equipment by companies like John Deere. This kind of monitoring can be lifesaving for big farms, as it notifies users of tractor availability, service due dates, and fuel refill warnings. In essence, this maximizes the efficiency of farm equipment while also ensuring its long-term health [38].
IoT is truly a ground-breaking modern technology due to its dynamic nature. When AI is integrated with the IoT, predictive intelligence emerges. According to some experts, IoT and AI technologies can dramatically boost crop yields and maybe the only option to reach a better system. The technology has the potential to pave the way for ecologically friendly practices as well. Figure 1.6 shows the steps involved in advanced technologies.
Farmers must keep a sharp eye on their crops for symptoms of sickness and pollution in most farming operations. The procedure is simple at a macro level, but the eyes cannot see everything. Farmers can use modern IoT solutions, as well as AI and mobile computing, to automate the entire process, leaving the review to technology. Farmers can keep track of their crops. With the help of microsensors, farmers can keep an eye on an individual plant for signs of illness or disease. Furthermore, the system can display statistics remotely via a smartphone or similar device, allowing farmers to receive real-time notifications about the condition of their fields, presence of pests, diseases, and so on.
Farmers can better safeguard their crops from pests by combining AI control technologies and IoT sensors. Farmers can use a spot treatment to treat individual plants and keep insects at bay. Simultaneously, fewer pollutants are released into the environment, particularly the soil beneath.
Figure 1.6 Steps involved in advanced technologies.
Aerial drones can evaluate and monitor crops in addition to—or perhaps instead of—IoT monitoring. The drones collect information about plants down to a single leaf using cameras and sensors placed inside. All of the acquired data, when fed into an ANN or ML solution, can produce a detailed image of a farmer’s herd.
Earth observation satellites have recently made high spatiotemporal remote sensing data available. Satellite and aerial imaging technologies are particularly valuable for capturing effective sensory images to monitor the environment, floods, fires, droughts, and other natural disasters, as well as agricultural applications like mapping, crop evaluation, crop health, and drought prediction. It offers high-speed spatial data at the global level. Numerous agricultural and hydrological indices have been created from this distant data to define the state of the land surface, primarily vegetation, groundwater level, soil moisture, and so on, to monitor and detect the beginning, duration, and severity of drought.
Managing cattle and livestock is no easy task. Farmers must not only keep track of each animal’s whereabouts, but they must also stay updated about their health. Farmers tie their cows with Fitbit-like IoT wristbands that monitor data in real-time to relieve some of the burdens. Animals will be benefitted from such wearable devices.
Experts can utilize the data acquired to develop predictive models and compare the performance to gain insights. The sharing of thousands of setups and pertinent facts in the farming world can lead to more efficient operations across the board. Agricultural specialists can exchange and consume a large quantity of knowledge, which includes anything from soil and seed tests to yield large production.
1.5 AI in the Lifecycle of the Agricultural Process
Despite modern technologies, instability in climate and unsustainable agriculture practices cause agricultural distress. The AI technology can help agriculture in the sectors [5] as shown in Figure 1.7.
1.5.1 Improving Crop Sowing and Productivity
Artificial Intelligence helps the farmers to determine the appropriate crop production in a favorable climate. An AI-based machinery helps in sowing crops at equidistant intervals and optimal depths. For example, in Andhra Pradesh, AI-powered sowing mobile application helps the farmers to increase the yield by about 30% per hectare [39]. The pilot farming was launched with the combined effort of Microsoft and ICRISAT (International Crops Research Institute for the Semi-Arid Tropics) and was implemented in the Kurnool district in 2016. Machine learning with business intelligence tools helped the farmers and Government to use digital technologies with the dashboard providing SMS for seed sowing, optimal seed depth, land preparation, and weed management [6].
Figure 1.7 The lifecycle of the agriculture process.
1.5.2 Soil Health Monitoring
Adequate amounts of moisture and nutrient content in the soil also contribute to the best yield. Soil health can be effectively monitored using distributed technology with DL and image recognition approach. Remote sensing techniques along with hyperspectral imaging and 3D laser scanning are also used for constructing crop matrices for better yield. The Indian Government introduced schemes like Soil Health Management (SHM) and Soil Health Card (SHC). The SHM scheme promotes judicious usage of chemical fertilizers, soil test recommendations, ensuring quality fertilizers, and so on. Each farmer is given SHC to make sure that a good harvest is possible by analyzing the soil quality. According to this scheme, the states like Madhya Pradesh, Rajasthan, Karnataka, and Uttar Pradesh [7] and nearly 45 million farmers got benefitted.
1.5.3 Weed and Pest Control
India needs 400 million tons of food to feed nearly 1.7 billion people by 2050 [12]. The food production decreases due to irregular climate which favors weed growth and thereby reduces the yield and quality of production. Many researchers in India studied the economic loss due to the presence of weeds. According to Sahoo and Saraswat, the loss was estimated to be INR 28 billion in the last two decades [8]. Bhan et al. [9] estimated that the 31.5% of reduction is mainly due to weeds. Varshney and Babu [10] estimated an economic loss of INR 1050 billion/year. Yogita et al. [11] estimated about 11 billion dollars lost due to weeds. The major crop which estimated economic losses is groundnuts, maize, soybean, wheat, rice, and so on [11, 28]. It is reported that about one third of total losses are because of weeds [13]. Despite efforts taken by weed management, weeds are considered to be a serious issue for different crops and other ecosystems. The main challenges faced by Indian farmers are as follows [36]:
(i) managing weeds in small area cultivation,
(ii) inadequate labor and modern tools,
(iii) less information about weed biology,
(iv) impact of climate change on growth of weeds,
(v) lack of knowledge in usage of herbicide which kills the weeds.
Various weed managements are prevailing, namely chemical, mechanical, biological, and cultural control. It is difficult to manage the weed effectively using single weed management. The use of integrated weed practices is suggested by many researchers [14–20] for major crops like rice, wheat, finger millet, maize, cotton, groundnut, and so on [29]. In a nutshell, it is proven that the herbicides combined with hand weeding help in removing weeds and increase crop production [21]. However, location-specific weed management with AI technology is necessary for Indian crops.
1.5.4 Water Management
In India, because of diverse climatic conditions, water management is not effective. Modern technologies are being used—thermal imaging camera, which monitors the crop determines whether it is getting adequate water. It is reported that water scarcity in India can lead to about 6% of the Gross Domestic Product (GDP) by 2030. The researchers also predicted that about 70% of groundwater is being pumped faster than estimated [23]. It is high time to look into the overpumping of groundwater. Artificial Intelligence coupled with image processing techniques helps in proper water management thereby increasing yield.
1.5.5 Crop Harvesting
It is reported that about 40% of the annual agricultural cost is being spent on labor employment. Nowadays, AI-based robots are being deployed to reduce labor costs. Artificial Intelligence also finds application in supply chain management [22] for crops.
