Machine Learning and AI Technology for Agricultural Applications

157.50 LEI

175 LEI

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Machine Learning and AI Technology in Agricultural Applications

offers a comprehensive overview of how artificial intelligence and machine learning are transforming the agricultural industry.

By delving into image processing and advanced data analysis, the book demonstrates how technology addresses modern agricultural challenges, including climate change, urbanization, and increasing global populations.

It emphasizes the importance of integrating sensors and data collection methods to generate vast pools of information, which can be efficiently analyzed through AI-driven solutions.

The text lays a strong foundation for understanding the role of technological innovation in supporting sustainable and secure food production.

Beyond introducing core machine learning models such as random forest, support vector machines, logistic regression, and decision trees, the book highlights the centralization of critical agricultural data in the cloud.

This resource benefits both students and seasoned agricultural scientists, providing practical insights for optimizing crop yields, monitoring soil and weather conditions, and managing resources like fertilizers and pesticides.

The book also explores the rapid analysis of complex datasets, empowering users to make informed, timely decisions in real-world agricultural scenarios.

Section I: Understanding AI and Machine Learning

1. Introduction to AI and Machine Learning (Written by Dr. Pradhan)

2. Implementing AI and ML in Agriculture: From Conventional to Smart Agricultural Practices

3. Revolutionizing Sustainable Agriculture: The Artificial Intelligence Approach

4. Challenges of Future Nexus: Combinatorial Reasoning with Machine Learning for Sustainable Agricultural Development

5. Embracing Technology for Sustainable Agriculture: A Survey of Information Systems, Precision Agriculture, and Automation

6. Scope and adoption of Machine learning and Deep learning in remote sensing in agriculture

7. Viability Study of Variable Rate Technology through Machine Learning

8. Market Impact Assessment of AI-Enabled Agricultural Technologies Utilizing SAR/Optical Data

9. Implication of Artificial Intelligence in sustainable and smart farming:

10. Understanding and performing a cost analysis of smart agriculture

Section II: Application of AI and Machine Learning in Agricultural Scenarios

11. From Pixels to Fields: Leveraging SAR and Optical Imagery Integration for Crop Area Mapping

12. Monitoring Crop Development and Yield Estimation Through Satellite and UAV Imagery Analysis Using Artificial Intelligence and Machine Learning

13. An Image Processing Approach for Plant Disease Detection

14. Weather based Crop Yield Modeling and Prediction using Statistical and Machine Learning techniques: The state of the art

15. Dynamic Crop Insights, Crop Dynamic Analytics: A Case Study of Real-Time Monitoring and Predictive Analytics for Corn and Soybean Growth

16. Efficient monitoring of agriculture fields using off-the-shelf satellite imagery.

17. Integrating Machine Vision Control to Spot Spraying System using Controller Area Network

18. Integrating IoT for Real-time Monitoring and Control in Smart Hydroponics Crop Production

19. 3D-ResNet-RNNs: Integrating Recurrent Neural Networks and 3D-ResNet for Enhanced Soybean Yield Predictions Using Multi-Modal Remote Sensing Data

20. Crop-Net: A Novel Deep Learning Framework for Crop Classification using Time-series Sentinel-1 Imagery by Google Earth Engine

21. Soil moisture monitoring using SAR polarimetry: A critical review

22. A comprehensive review of the role of artificial intelligence and computer vision for post-harvest analysis of fruits

23. Timely animal intrusion detection: Protection of agricultural fields

Section III: Application of AI and Machine Learning in Aquatic Scenarios

24. Optimizing Groundwater Recharge Estimation and Mapping with Google Earth Engine: A Case Study of the Mahanadi River Basin, India

25. Leveraging Artificial Intelligence for Enhanced Aquaculture Management: A Focus on Toxicity Monitoring in Fish Farming

26. Modeling growth of Catla (Catla Catla) fish using artificial neural network (ANN)

27. Utilizing Machine Learning for Fish Resource Management in Aquaculture

28. Water Quality Index Prediction through Artificial Intelligence

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Machine Learning and AI Technology for Agricultural Applications

157.50 LEI

175 LEI

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