Data-Driven Farming (eBook, PDF)

Harnessing the Power of AI and Machine Learning in Agriculture
Redaktion: Bukhari, Syed Nisar Hussain

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis
• Seitenzahl: 300
• Erscheinungstermin: 13. Juni 2024
• Englisch
• ISBN-13: 9781040037232
• Artikelnr.: 70582294

Autorenporträt

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