The book provides a detailed overview of the intersection of data, AI, and machine learning in agriculture. Offering real-world examples and case studies, it demonstrates how AI can help improve efficiency, reduce waste, and increase profitability.
The book provides a detailed overview of the intersection of data, AI, and machine learning in agriculture. Offering real-world examples and case studies, it demonstrates how AI can help improve efficiency, reduce waste, and increase profitability.
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.
Inhaltsangabe
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 Predictions, 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
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 Predictions, 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
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