Big Data Analytics in Agriculture
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Big Data Analytics in Agriculture
Algorithms and Applications
Srivastava, Prashant K.; Pandey, Manish K.; Pradhan, Biswajeet; Kumar Mall, Rajesh
Elsevier Science & Technology
06/2025
350
Mole
Inglês
9780323999328
Pré-lançamento - envio 15 a 20 dias após a sua edição
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Section 1: Introduction to Big Data Analytics in Agriculture
1. Introduction to Traditional Data Analytics
2. Introduction to Big Data and Big Data Analytics
Section II: Big Data Management and Processing
3. The efficient management of Big Data from Scalability and Cost Evaluation Perspective
4. The Approaches for the Big Data Processing: Applications and Challenges
Section III: Big Data Analytics Algorithms
5. Big Data Mining in real-time scenarios with limited resources and computational power
6. Big Data Analytics techniques comprising descriptive, predictive, prescriptive and preventive analytics with an emphasis on feature engineering and model fitting
Section IV: Big Data Applications
7. IoT foundations in Precision Agriculture and its Application.
8. Practical applications of Big Data-driven Smart farming
9. Practical applications of Smart & Precise irrigation
10. Weed or Disease Detection using AI/ML/Deep Learning techniques
11. Nutrient Stress Detection using AI/ML/Deep Learning techniques
12. Leaf Disease Detection using AI/ML/Deep Learning techniques
13. Efficient soil water management using AI/ML
14. Microclimatic Forecasting using AI/ML/Deep Learning techniques
15. AI/ML/Deep Learning techniques in precipitation forecast
16. Yield Prediction using AI/ML/Deep Learning techniques
17. Practical applications of Supply Chain Analytics in Agriculture
18. Efficient Farm Analytics using AI/ML/Deep Learning techniques
Section V: Challenges and prospects
19. Challenges and future pathway for big data analytics algorithms and applications in Agriculture
1. Introduction to Traditional Data Analytics
2. Introduction to Big Data and Big Data Analytics
Section II: Big Data Management and Processing
3. The efficient management of Big Data from Scalability and Cost Evaluation Perspective
4. The Approaches for the Big Data Processing: Applications and Challenges
Section III: Big Data Analytics Algorithms
5. Big Data Mining in real-time scenarios with limited resources and computational power
6. Big Data Analytics techniques comprising descriptive, predictive, prescriptive and preventive analytics with an emphasis on feature engineering and model fitting
Section IV: Big Data Applications
7. IoT foundations in Precision Agriculture and its Application.
8. Practical applications of Big Data-driven Smart farming
9. Practical applications of Smart & Precise irrigation
10. Weed or Disease Detection using AI/ML/Deep Learning techniques
11. Nutrient Stress Detection using AI/ML/Deep Learning techniques
12. Leaf Disease Detection using AI/ML/Deep Learning techniques
13. Efficient soil water management using AI/ML
14. Microclimatic Forecasting using AI/ML/Deep Learning techniques
15. AI/ML/Deep Learning techniques in precipitation forecast
16. Yield Prediction using AI/ML/Deep Learning techniques
17. Practical applications of Supply Chain Analytics in Agriculture
18. Efficient Farm Analytics using AI/ML/Deep Learning techniques
Section V: Challenges and prospects
19. Challenges and future pathway for big data analytics algorithms and applications in Agriculture
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Cloud Computing; Hyperspectral Data Analytics; Hyperspectral Image Processing; Sensor Analytics; Third Computing Paradigm; Social Mobility Analytics and Cloud (SMAC); Cyber-Physical Systems; Internet of Everything (IoE); Internet of Things (IoT); Computational Intelligence; Collective Intelligence; Recommender Systems; Spatial Data Analytics; Greenhouse IoT; Livestock Monitoring; Decision Support Systems; Supply Chain Analytics; Optimization; Augmented Analytics
Section 1: Introduction to Big Data Analytics in Agriculture
1. Introduction to Traditional Data Analytics
2. Introduction to Big Data and Big Data Analytics
Section II: Big Data Management and Processing
3. The efficient management of Big Data from Scalability and Cost Evaluation Perspective
4. The Approaches for the Big Data Processing: Applications and Challenges
Section III: Big Data Analytics Algorithms
5. Big Data Mining in real-time scenarios with limited resources and computational power
6. Big Data Analytics techniques comprising descriptive, predictive, prescriptive and preventive analytics with an emphasis on feature engineering and model fitting
Section IV: Big Data Applications
7. IoT foundations in Precision Agriculture and its Application.
8. Practical applications of Big Data-driven Smart farming
9. Practical applications of Smart & Precise irrigation
10. Weed or Disease Detection using AI/ML/Deep Learning techniques
11. Nutrient Stress Detection using AI/ML/Deep Learning techniques
12. Leaf Disease Detection using AI/ML/Deep Learning techniques
13. Efficient soil water management using AI/ML
14. Microclimatic Forecasting using AI/ML/Deep Learning techniques
15. AI/ML/Deep Learning techniques in precipitation forecast
16. Yield Prediction using AI/ML/Deep Learning techniques
17. Practical applications of Supply Chain Analytics in Agriculture
18. Efficient Farm Analytics using AI/ML/Deep Learning techniques
Section V: Challenges and prospects
19. Challenges and future pathway for big data analytics algorithms and applications in Agriculture
1. Introduction to Traditional Data Analytics
2. Introduction to Big Data and Big Data Analytics
Section II: Big Data Management and Processing
3. The efficient management of Big Data from Scalability and Cost Evaluation Perspective
4. The Approaches for the Big Data Processing: Applications and Challenges
Section III: Big Data Analytics Algorithms
5. Big Data Mining in real-time scenarios with limited resources and computational power
6. Big Data Analytics techniques comprising descriptive, predictive, prescriptive and preventive analytics with an emphasis on feature engineering and model fitting
Section IV: Big Data Applications
7. IoT foundations in Precision Agriculture and its Application.
8. Practical applications of Big Data-driven Smart farming
9. Practical applications of Smart & Precise irrigation
10. Weed or Disease Detection using AI/ML/Deep Learning techniques
11. Nutrient Stress Detection using AI/ML/Deep Learning techniques
12. Leaf Disease Detection using AI/ML/Deep Learning techniques
13. Efficient soil water management using AI/ML
14. Microclimatic Forecasting using AI/ML/Deep Learning techniques
15. AI/ML/Deep Learning techniques in precipitation forecast
16. Yield Prediction using AI/ML/Deep Learning techniques
17. Practical applications of Supply Chain Analytics in Agriculture
18. Efficient Farm Analytics using AI/ML/Deep Learning techniques
Section V: Challenges and prospects
19. Challenges and future pathway for big data analytics algorithms and applications in Agriculture
Este título pertence ao(s) assunto(s) indicados(s). Para ver outros títulos clique no assunto desejado.
Cloud Computing; Hyperspectral Data Analytics; Hyperspectral Image Processing; Sensor Analytics; Third Computing Paradigm; Social Mobility Analytics and Cloud (SMAC); Cyber-Physical Systems; Internet of Everything (IoE); Internet of Things (IoT); Computational Intelligence; Collective Intelligence; Recommender Systems; Spatial Data Analytics; Greenhouse IoT; Livestock Monitoring; Decision Support Systems; Supply Chain Analytics; Optimization; Augmented Analytics
