1st Edition
Sustainable Farming through Machine Learning Enhancing Productivity and Efficiency
This book explores the transformative potential of ML technologies in agriculture. It delves into specific applications, such as crop monitoring, disease detection, and livestock management, demonstrating how AI/ML can optimize resource management and improve overall productivity in farming practices.
Sustainable Farming through Machine Learning: Enhancing Productivity and Efficiency provides an in-depth overview of AI and ML concepts relevant to the agricultural industry. It discusses the challenges faced by the agricultural sector and how AI/ML can address them. The authors highlight the use of AI/ML algorithms for plant disease and pest detection and examine the role of AI/ML in supply chain management and demand forecasting in agriculture. It includes an examination of the integration of AI/ML with agricultural robotics for automation and efficiency. They also cover applications in livestock management, including feed formulation and disease detection, they also explore the use of AI/ML for behavior analysis and welfare assessment in livestock. Finally, the authors also explore ethical and social implications of using such technologies.
This book can be used as a textbook for students in agricultural engineering, precision farming, and smart agriculture. It can also be a reference book for practicing professionals in machine learning, and deep learning working on sustainable agriculture applications.
1. Exploring AI and ML Strategies for Crop Health Monitoring and Management
Preethi Nanjundan, Indu P.V, and Lijo Thomas
2. Enhancing Crop Productivity by Suitable Crop Prediction Using Cutting-Edge Technologies
G.Mamatha and Dr. Jyothi S Nayak
3. Crop Yield Prediction Using Machine Learning Random Forest Algorithm
Dr. Suwarna Gothane
4. A multi-objective based genetic approach for increasing crop yield on sustainable farming
Swadhin Kumar Barisal, Bijayini Mohanty, Santilata Champati,Gayatri Nayak, Pushkar Kishore, and Alakananda Tripathy
5. Drones For Crop Monitoring And Analysis
Preethi Nanjundan, Indu P.V, and Lijo Thomas
6. Decision Support System For Sustainable Farming
Nandini Nenavath, Chintala Sai Akshitha, and Venkata Krishna Reddy M c
7. Empowering Agriculture: Harnessing the Potential of AI-Driven Virtual Tutors for Farmer Education and Investment Strategies
Soumya Priyadarshini Mishra and Munmun Mohanty
8. Enhancing Agricultural Ecosystem Surveillance through Autonomous Sensor Networks
P. Venkata Kishore, K. Sree Latha, D. Naveen Kumar, M. Dilip Kumar, S. Vijay Kumar, and KVB Reddy
9. Crop Disease Detection Using Image Analysis
M. Mohamed Iqbal, Ramireddy Jyothsna , Somu Preethi Deekshitha, and Yadannapudi Renuka Gayatri
10. Automated Detection of Plant Diseases Utilizing Convolutional Neural Networks
Archisman Panda, Arjav Anil Patel, Muhammad Zakaria Shaik, Sandeep Kumar Satapathy, Sung-Bae Cho, Shruti Mishra, Abishi Chowdhury, and Sachi Nandan Mohanty
11. Apple Leaves Diseases Detection Using Deep Learning
Smita Maurya1, Arunima Jaiswal, and Nitin Sachdeva
12. Optimizing Agricultural Yield: Comprehensive Approaches for Recommendation System in Precision Agriculture
Ramesh Patra, Mamata Garanayak, and Bijay Kumar Paikaray
13. Advancements in Precision Agriculture: A Machine Learning-based Approach for Crop Management Optimization
Chinmayee Senapati, Swagatika Senapati, Satyaprakash Swain, Kumar Janardan Patra, Binod Kumar Pattanayak, and Suprava Ranjan Laha
14. Precision Agriculture with Remote Sensing: Integrating Deep Learning for Crop Monitoring
Umarani Nagavelli, Srilakshmi V, M Vijay Kumar, and T Sreeja
15. Farmers Guide: Data-Driven Crop Recommendations for Precision and Sustainable Agriculture Using IoT and ML
Swarna Prabha Jena, Fatimun Nisha, Priya Banerjee, Bijay Kumar Paikaray, and Sujata Chakravarty
16. Application of Machine Learning in the Analysis and Prediction of Animal Disease
Soumen Nayak, Lambodar Jena, Pranati Palai, Sushruta Mishra, and Manas Kumar Swain
17. Transforming Indian Agriculture: A Machine Learning Approach for Informed Decision-Making and Sustainable Crop Recommendations
Smitta Ranjan Dutta, Sanata Kumar Swain, and Swayumjit Ray
18. Automated Detection of Water Quality for Smart Systems using Various Sampling Techniques - An Agricultural Perspective
Sanket Mishra, T. Anithakumari, and Ojasva Jain
19. Scope of Artificial Intelligence (A.I.) in “Agriculture Sector and its applicability in Farm Mechanization in Odisha
Ashok Rout, Gopikrishna Panda, and Sunil Kumar Dhal
20. Ethical Considerations and Social Implications
Jeethu V. Devasia, Deepanramkumar P, Helensharmila A, and Gokul Yenduri
Biography
Dr. Suneeta Satpathy, PhD, is an Associate Professor in Center for AI & ML, Siksha 'O' Anusandhan (Deemed to be) University, Odisha, India. Her research interests include computer forensics, cyber security, data fusion, data mining, big data analysis, decision mining and machine learning. She has published papers in many international journals and conferences in repute. She has two Indian patents in her credit, and is a member of IEEE, CSI, ISTE, OITS, and IE.
Dr. Bijay Kumar Paikaray, PhD, is an Associate Professor at the Center for Data Science, Siksha 'O' Anusandhan (Deemed to be) University, Odisha. His interests include high-performance computing, information security, machine learning and IoT.
Dr. Ming Yang has a Ph.D. in Computer Science from Wright State University, Dayton, Ohio, US, 2006. Currently he is a Professor in the College of Computing and Software Engineering Kennesaw State University, GA, USA. His research interests include multimedia communication, digital image/video processing, computer vision, and machine learning.
Dr. Arunkumar Balakrishnan PhD, holds the position of Assistant Professor Senior Grade in the Computer Science and Engineering department at VIT-AP University. He obtained his Ph.D. in Information Science and Engineering from Anna University, Chennai. He possesses 12 years of academic expertise and an additional 6 years of concurrent research experience in the domains of Cryptography, Medical Image Security, Blockchain, and NFT. His research interests encompass Cryptography, Network Security, Medical Image Encryption, Blockchain, lightweight cryptography methods, and NFT.
