1st Edition
Data-Driven Farming Harnessing the Power of AI and Machine Learning in Agriculture
In the dynamic realm of agriculture, artificial intelligence (AI) and machine learning (ML) emerge as catalysts for unprecedented transformation and growth. The emergence of big data, Internet of Things (IoT) sensors, and advanced analytics has opened up new possibilities for farmers to collect and analyze data in real-time, make informed decisions, and increase efficiency. AI and ML are key enablers of data-driven farming, allowing farmers to use algorithms and predictive models to gain insights into crop health, soil quality, weather patterns, and more. Agriculture is an industry that is deeply rooted in tradition, but the landscape is rapidly changing with the emergence of new technologies.
Data-Driven Farming: Harnessing the Power of AI and Machine Learning in Agriculture is a comprehensive guide that explores how the latest advances in technology can help farmers make better decisions and maximize yields. It offers a detailed overview of the intersection of data, AI, and ML in agriculture and offers real-world examples and case studies that demonstrate how these tools can help farmers improve efficiency, reduce waste, and increase profitability. Exploring how AI and ML can be used to achieve sustainable and profitable farming practices, the book provides an introduction to the basics of data-driven farming, including an overview of the key concepts, tools, and technologies. It also discusses the challenges and opportunities facing farmers in today’s data-driven landscape. Covering such topics as crop monitoring, weather forecasting, pest management, and soil health management, the book focuses on analyzing data, predicting outcomes, and optimizing decision-making in a range of agricultural contexts.
1. Leveraging IoT for Precision Health Monitoring in Livestock with Artificial Intelligence
Devinder Kaur and Amandeep Kaur Virk
2. Significance of Machine learning in Apple Disease Detection and Implications
Saimul Bashir, Syed Nisar Hussain Bukhari, Faisal Firdous, and Gursimran Jeet Kour
3. Intelligent Inputs Revolutionizing Agriculture: An Analytical Study
Nelofar Ara, Sani Mustapha Kura, Aswathy VK, and Mohammad Amin Wani
4. Case Studies on the Initiatives and Success Stories of Edge AI Systems for Agriculture
Thamizhiniyan Natarajan and Shanmugavadivu Pichai
5. Crop Recommender: Machine Learning-based Computational Method to Recommend the Best Crop Using Soil and Environmental Features
Syed Nisar Hussain Bukhari, Jewiara Khursheed Wani, Ummer Iqbal, and Muneer Ahmad Dar
6. A Perusal of Machine-Learning Algorithms in Crop-Yield Prediction
Anshika Gupta, Mohit Soni, and Kalpana Katiyar
7. Harvesting Intelligence: AI and ML Revolutionizing Agriculture
Arya Kumari, Muhammad Najeeb Khan, and Amit Kumar Sinha
8. Using Deep Learning to Detect Apple Leaf Disease
Syed Nisar Hussain Bukhari, Rukaya Manzoor, Ummer Iqbal, and Muneer Ahmad Dar
9. Agricultural Crop Yield Prediction: Comparative Analysis Using Machine Learning Models
Kukatlapalli Pradeep Kumar, Babu Kumar S, Amarthya Dutta Gupta, Kevin Johnson, and Meghan Mary Michael
10. Fundamentals of AI and Machine Learning with Specific Examples of Application in Agriculture
Manoj Kumar Mahto, P. Laxmikanth, and V.S.S.P.L.N. Balaji Lanka,
11. Farming Futures: Leveraging Machine Language for Potato Leaf Disease Forecasting and Yield Optimization
Vijayalakshmi A, Deepthi Das, Nidin A
12. Classification of Farms for Recommendation of Rice Cultivation Using Naive Bayes and SVM: A Case Study
Qurat-ul-ain,Uzma Hameed,Hamira Mehraj
13. Neural Networks for Crop Disease Detection
Mohammad Ubaidullah Bokhari, Gaurav Yadav, Md. Zeyauddin
14. Short-Term Weather Forecasting for Precision Agriculture in Jammu and Kashmir: A Deep Learning Approach
Syed Nisar Hussain Bukhari, Sana Farooq Pandit
15. Deep Reinforcement Learning for Smart Irrigation
Aakansha Khanna and Inzimam Ul Hassan
Biography
Dr. Syed Nisar Hussain Bukhari is an accomplished researcher and academician, holding Bachelor’s, Master’s, and Ph.D. degrees in Computer Science. His research interests include artificial intelligence and ML, deep learning, and applying AI and ML in interdisciplinary areas like agriculture and healthcare. His other work areas are bioinformatics, immunoinformatics, and computational biology, and he has taught courses on AI and ML at undergraduate (UG) and postgraduate (PG) levels. He has proven experience in providing expert advice on the use of technology in different domains.
