1st Edition
Artificial Intelligence and Smart Agriculture Applications
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 macronutrient 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
Biography
Dr. Utku Kose is Associate Professor in Suleyman Demirel University, Turkey. He has more than 100 publications including articles, authored and edited books, proceedings, and reports.
V.B. Surya Prasath is an assistant professor in the Division of Biomedical Informatics at the Cincinnati Children's Hospital Medical Center, and at the Departments of Biomedical Informatics, Electrical Engineering and Computer Science, University of Cincinnati from 2018.
M. Rubaiyat Hossain Mondal is a faculty member at the Institute of Information and Communication Technology (IICT) in BUET, Bangladesh. He has published a number of papers in journals of IEEE, IET, Elsevier, Springer, Wiley, De Gruyter, PLOS, and MDPI.
Prajoy Podder is currently a researcher at the Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology. He worked as a lecturer in the department of Electrical and Electronic Engineering, Ranada Prasad Shaha University, Narayanganj, Bangladesh.
Subrato Bharati is a researcher in the Institute of Information and Communication Technology, Bangladesh University of Engineering and Technology, Dhaka, Bangladesh. He is a regular reviewer of a number of international journal including Elsevier, Springer, and Wiley.
