Smart Agriculture (eBook, PDF)
Harnessing Machine Learning for Crop Management
Redaktion: Dhaygude, Amol Dattatray; Rathore, Yogesh Kumar; Chugh, Priya; Kumar Swarnkar, Suman
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Smart Agriculture (eBook, PDF)
Harnessing Machine Learning for Crop Management
Redaktion: Dhaygude, Amol Dattatray; Rathore, Yogesh Kumar; Chugh, Priya; Kumar Swarnkar, Suman
- Format: PDF
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This book is a comprehensive guide designed to explore the various facets of integrating machine learning into agricultural practices. It aims to provide readers with a solid foundation in machine learning concepts while demonstrating their practical applications in real-world farming scenarios.
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- Produktdetails
- Verlag: Taylor & Francis
- Seitenzahl: 182
- Erscheinungstermin: 18. Dezember 2024
- Englisch
- ISBN-13: 9781040269053
- Artikelnr.: 72523964
- Verlag: Taylor & Francis
- Seitenzahl: 182
- Erscheinungstermin: 18. Dezember 2024
- Englisch
- ISBN-13: 9781040269053
- Artikelnr.: 72523964
- Herstellerkennzeichnung Die Herstellerinformationen sind derzeit nicht verfügbar.
Mechanism. 2. Future Prospects and Challenges of Digital Transformation in
Agriculture and Dairy Industries Mechanisms. 3. Innovative IoT-Driven
Solutions for Real-Time Crop Health Surveillance and Precision Agriculture.
4. Optimizing Resource Allocation in Precision Agriculture through the
Application of K-Means Clustering. 5. Upholding Ethical Standards in Modern
Agriculture: An Examination of Privacy-Preserving Machine Learning
Techniques. 6. Exploring the Effectiveness of Decision Trees for
Comprehensive Detection of Crop Diseases in Agricultural Environments. 7.
Integrating Deep Learning and Image Recognition in Smart Farming. 8.
Exploring the Effectiveness of Decision Trees for Comprehensive Detection
of Crop Diseases in Agricultural Environments. 9. Enhancing Crop Yield
Prediction Accuracy through the Application of Gradient Descent
Optimization Algorithms. 10. Machine Learning Models for Early Detection of
Pest Infestation in Crops: A Comparative Study.
Mechanism. 2. Future Prospects and Challenges of Digital Transformation in
Agriculture and Dairy Industries Mechanisms. 3. Innovative IoT-Driven
Solutions for Real-Time Crop Health Surveillance and Precision Agriculture.
4. Optimizing Resource Allocation in Precision Agriculture through the
Application of K-Means Clustering. 5. Upholding Ethical Standards in Modern
Agriculture: An Examination of Privacy-Preserving Machine Learning
Techniques. 6. Exploring the Effectiveness of Decision Trees for
Comprehensive Detection of Crop Diseases in Agricultural Environments. 7.
Integrating Deep Learning and Image Recognition in Smart Farming. 8.
Exploring the Effectiveness of Decision Trees for Comprehensive Detection
of Crop Diseases in Agricultural Environments. 9. Enhancing Crop Yield
Prediction Accuracy through the Application of Gradient Descent
Optimization Algorithms. 10. Machine Learning Models for Early Detection of
Pest Infestation in Crops: A Comparative Study.