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 2. Significance of Machine learning in Apple Disease Detection and Implications 3. Intelligent Inputs Revolutionizing Agriculture: An Analytical Study 4. Case Studies on the Initiatives and Success Stories of Edge AI Systems for Agriculture 5. Crop Recommender: Machine Learning-based Computational Method to Recommend the Best Crop Using Soil and Environmental Features 6. A Perusal of Machine-Learning Algorithms in Crop-Yield Prediction 7. Harvesting Intelligence: AI and ML Revolutionizing Agriculture 8. Using Deep Learning to Detect Apple Leaf Disease 9. Agricultural Crop Yield Prediction: Comparative Analysis Using Machine Learning Models 10. Fundamentals of AI and Machine Learning with Specific Examples of Application in Agriculture 11. Farming Futures: Leveraging Machine Language for Potato Leaf Disease Forecasting and Yield Optimization 12. Classification of Farms for Recommendation of Rice Cultivation Using Naive Bayes and SVM: A Case Study 13. Neural Networks for Crop Disease Detection 14. Short-Term Weather Forecasting for Precision Agriculture in Jammu and Kashmir: A Deep Learning Approach 15. Deep Reinforcement Learning for Smart Irrigation

Dr. Syed Nisar Hussain Bukhari holds a PhD in Computer Science from Chandigarh University India. His research interests include artificial intelligence and machine learning, deep learning, applying AI and ML in interdisplinary areas like Agriculture, Health care. His other work areas are bioinformatics, Immunoinformatics and computational biology and has taught courses on Artificial Intelligence and Machine Learning at UG and PG level. He has a proven experience of providing expert advice on the use of technology in different domain.