Machine Learning and AI Technology for Agricultural Applications

Herausgeber: Swain, Kishore Chandra; Pradhan, Biswajeet; Pham, Quoc Bao; Moghimi, Armin; Sahoo, Satiprasad; Singha, Chiranjit

• Broschiertes Buch

Produktbeschreibung

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.

Produktdetails

• Verlag: Elsevier Science
• Seitenzahl: 300
• Erscheinungstermin: 1. Juni 2026
• Englisch
• ISBN-13: 9780443450501
• ISBN-10: 0443450501
• Artikelnr.: 75638960

Inhaltsangabe

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