This book was created with the intention of informing an international audience about the latest technological aspects for developing smart agricultural applications. As artificial intelligence (AI) takes the main role in this, the majority of the chapters are associated with the role of AI and data analytics components for better agricultural applications.

The first two chapters provide alternative, wide reviews of the use of AI, robotics, and the Internet of Things as effective solutions to agricultural problems. The third chapter looks at the use of blockchain technology in smart agricultural scenarios. In the fourth chapter, a future view is provided of an Internet of Things-oriented sustainable agriculture. Next, the fifth chapter provides a governmental evaluation of advanced farming technologies, and the sixth chapter discusses the role of big data in smart agricultural applications. The role of the blockchain is evaluated in terms of an industrial view under the seventh chapter, and the eighth chapter provides a discussion of data mining and data extraction, which is essential for better further analysis by smart tools. The ninth chapter evaluates the use of machine learning in food processing and preservation, which is a critical issue for dealing with issues concerns regarding insufficient foud sources. The tenth chapter also discusses sustainability, and the eleventh chapter focuses on the problem of plant disease prediction, which is among the critical agricultural issues. Similarly, the twelfth chapter considers the use of deep learning for classifying plant diseases. Finally, the book ends with a look at cyber threats to farming automation in the thirteenth chapter and a case study of India for a better, smart, and sustainable agriculture in the fourteenth chapter.

This book presents the most critical research topics of today’s smart agricultural applications and provides a valuable view for both technological knowledge and ability that will be helpful to academicians, scientists, students who are the future of science, and industrial practitioners who collaborate with academia.

1. Smart Farming Using Artificial Intelligence, Internet of Things and Robotics: A Comprehensive Review

M.J. Mathushika, R. Vinushayini, and C. Gomes

2. Towards theTechnological Adaptation of Advanced Farming Through Artificial Intelligence, the Internet of Things, and Robotics: A Comprehensive Overview

MD. Mahadi Hasan, Muhammad Usama Islam, and Muhammad Jafar Sadeq

3. Artificial Intelligence and the Blockchain in Smart Agriculture: Emergence, Opportunities, and Challenges

Anoop V. S., Adarsh S. , and Asharaf S.

4. Artificial Intelligence and Internet of Things Enabled Smart Farming for Sustainable Development: The Future of Agriculture

M.Thilagu and J. Jayasudha

5. A Science, Technology, and Society Approach to Studying the Cumin Revolution in Western India

Diwakar Kumar

6. Role of Big Data in Agriculture

C.T. Ashita and T. Sree Kala

7. Blockchain-Based Agri Manufacture Industry

Mahadi Hasan Miraz, Mohammad Tariq Hasan, Farhana Rahman Sumi, Shumi Sarkar, Mohammad Amzad Hossain, and Subrato Bharati

8. Agricultural Data Mining and Information Extraction

K. Aditya Shastry and Sanjay H. A.

9. Machine Learning and Its Application in Food Processing and Preservation

Babatunde Olawoye, Oyekemi Popoola, Oseni Kadiri, Jide Ebenezer Taiwo Akinsola, and Charles Taiwo Akanbi

10. Study of Disruptive Technologies for Sustainable Agriculture

Tapalina Bhattasali and Xavier Savarimuthu

11. Role of Dimensionality Reduction Techniques for Plant Disease Prediction

Muhammad Kashif Hanif, Shaeela Ayesha, and Ramzan Talib

12. A Review of Deep Learning Approaches for Plant Disease Detection and Classification

Kusum Lata and Sandeep Saini

13. Cyber Threats in Farming Automation

Muskan Gupta and B. K. Tripathy

14. Prospects of Smart Farming as a Key to Sustainable Agricultural Development: A Case Study of India

Bhabesh Deka and Chittaranjan Baruah

The COVID-19 pandemic has significantly affected the healthcare sector across the globe. Artificial Intelligence (AI) and Internet of Medical Things (IoMT) are playing an important role in dealing with the emergent challenges. These technologies are being applied to problems involving early detection of infections, fast contact tracing, decision-making models risk profiling of cohorts, and remote treatment. Applying these technologies runs against such challenges as interoperability, lack of unified structure for eHealth, and data privacy and security Emerging Technologies for Combatting Pandemics: AI, IoMT, and Analytics examines various models and solutions for various settings including individual, home, work and society. The world’s healthcare system is battling with the novel coronavirus, and government authorities, scientists, medical practitioners, and medical services are striving hard to surmount the challenges.

Highlights of the book include:

• Epidemic forecast models

• Surveillance and tracking systems

• IoMT and IoHT-based integrated systems for COVID-19

• Social network analysis systems

• Radiological image-based diagnosis systems, and

• Computational intelligence methods.

1. AI-Leveraged IoMT and Continuous Health Monitoring and Combating Pandemics within the IoMT Framework

Chitharanjan Billa and Murthy Chavali

2. Assessing the Economic Impact of COVID-19

Sonia Sharma, Anshi Gupta, Jagadeesh Chandra Bose K

3. Assessing The Economic Impact of COVID-19 in the Implications of Internet of Things (IoT) Adoption on Small and Medium Enterprises (SMEs) Business’s Sustainability　

R. Abd Shukor and W.K. Mooi

4. Impact of COVID-19: Insights from Key Sectors of Indian Economy　

Reena Malik

5. Future Scope of Artificial Intelligence in Healthcare for COVID-19

Manas Kumar Yogi and Jyotsna Garikipati

6. Patient Recovery and Tracing Repercussions of COVID 19 in Discharged Patients

B. Patel, K. Patel, D. Patel, M. Bohara, J. Desai, and A. Ganatra

7. The Impact of COVID-19 in the Maritime Economy: A Study of Bangladesh

Bornali Rahman, Jakir Hosain, Mohammad Tameem, and Hossain Azmi

8. Intelligent Optimization and Computational Learning Techniques for Mitigating Pandemics　

Kayode Abiodun Oladapo, Jide Ebenezer Taiwo Akinsola, Moruf Adeagbo, Fathia Onipede, Samuel Ayomikun Akinseinde, and Adebola Abdulwaheed Yusuf

9. Various Deep Learning Methodologies for COVID-19 Diagnosis

K. Patel,B. Patel, M. Bohra, J. Desai, D. Patel, and A. Ganatra

10. Hybridization of Decision Tree Algorithm Using Sequencing Predictive Model for COVID-19

A. A. Awoseyi, J. E. T. Akinsola, O. M. Oladoja, M. A. Adeagbo., and O. O. Adebowale

11. CoVICU: A Smart Model for Predicting the Intensive Care Unit Stay of COVID-19 Patients using Machine Learning Techniques

Sakthi Jaya Sundar Rajasekar and V Aruna Devi

12. LSTM-based RNN Model for COVID-19 Prediction in Different States of India

Sowmya V, Mredulraj S. Pandianchery, E.A. Gopalakrishnan, and K.P. Soman

13. Dengue in the Presence of COVID-19: Evaluation of Tree-based Classifiers Using Stratified K-Fold on Dengue Dataset

Supreet Kaur and Sandeep Sharma

An essential resource work for understanding how to design and develop smart applications for present and future problems of the field of agriculture.— Dr. Deepak Gupta, Maharaja Agrasen Institute of Technology, Delhi, India

As a result of the advances in Artificial Intelligence (AI), many aspects of daily life have been transformed by smart digital technology. Advanced intelligent algorithms can provide powerful solutions to real-world problems. Smart applications have become commonplace. All areas of life are being changed by smart tools developed to deal with complex issues challenging both humanity and the earth.

Artificial Intelligence and Smart Agriculture Applications presents the latest smart agriculture applications developed across the globe. It covers a broad array of solutions using data science and AI to attack problems facing agriculture worldwide.

Features:

• Application of drones and sensors in advanced farming

• A cloud-computing model for implementing smart agriculture

• Conversational AI for farmer's advisory communications

• Intelligent fuzzy logic to predict global warming’s effect on agriculture

• Machine learning algorithms for mapping soil macro-nutrient elements variability

• A smart IoT framework for soil fertility enhancement

• AI applications in pest management

• A model using Python for predicting rainfall

The book examines not only present solutions but also potential future outcomes. It looks at the role of AI-based algorithms and the almost infinite combinations of variables for agricultural applications. Researchers, public and private sector representatives, agriculture scientists, and students can use this book to develop sustainable and solutions for smart agriculture. This book’s findings are especially important as the planet is facing unprecedented environmental challenges from over-farming and climate change due to global warming.

1. Application of Drone and Sensors in Advanced Farming: The Future Smart Farming Technology

Kumar Chiranjeeb, Rajani Shandilya, and Kali Charan Rath

2. Development and Research of a Greenhouse Monitoring System

Murat Kunelbayev and Amantur Umarov

3. A Cloud-Computing Model for Implementing Smart Agriculture

M. Zhou and C. Matsika

4. Application of Conversational Artificial Intelligence for Farmer's Advisory and Communication

Anurag Sinha and Den Whilrex Garcia

5. The Use of an Intelligent Fuzzy Logic Controller to Predict the Global Warming Effect on Agriculture: The Case of Chickpea (Cicer arietinum L.)

H. Chekenbah, I. El Hassani , S. El Fatehi, Y. Hmimsa, M. L. Kerkeb, and R. Lasri

6. Using Machine Learning Algorithms for Mapping Soil Macronutrient Elements Variability with Digital Environmental Data in an Alluvial Plain

Fuat Kaya and Levent Başayiğit

7. A Smart IoT Framework for Soil Fertility Enhancement Assisted via Deep Neural Networks

Sannidhan Manjaya Shetty, Jason Elroy Martis, and Sudeepa Keregadde Balakrishna

8. Plant Disease Detection with the Help of Advanced Imaging Sensors

Shivam Singh, Raina Bajpai, MD. Mahtab Rashid, Basavaraj Teli, and Gagan Kumar

9. Artificial Intelligence-Aided Phenomics in High throughput Stress Phenotyping of Plants

Debadatta Panda, M. Kumar, L. Mahalingam, M. Raveendran

10. Plant Disease Detection using Hybrid Deep Learning Architecture in Smart Agriculture Application

Murugan Subramanian, Nelson Iruthayanathan, Annadurai Chinnamuthu, Nirmala Devi Kathamuthu, Manikandan Ramachandran, and Ambeshwar Kumar

11. Classification of Coffee Leaf Diseases through Image Processing Techniques

Ali Hakan Işik and Ömer Can Eskicioglu

12. The Use of Artificial Intelligence to Model Oil Extraction Yields from Seeds and Nuts

Chinedu M. Agu, Charles C. Orakwue, and Albert C. Agulanna

13. Applications of Artificial Intelligence in Pest Management

Muhammad Kashif Hanif, Shouket Zaman Khan, and Maria Bibi

14. Applying Clustering Technique for Rainfall Received by Different District of Maharashtra State

Nitin Jaglal Untwal

15. Predicting Rainfall for Aurangabad Division of Maharashtra by Applying Auto-Regressive Moving Average Model (ARIMA) Using Python Programming

Nitin Jaglal Untwal