| Titre : | Predictive analytics in smart agriculture |
| Auteurs : | S. Krishnan, ed. ; A.J. Anand, ed. ; N. Prasanth, ed. ; S. Goundar, ed. ; C. Ananth, ed. |
| Type de document : | Ouvrage |
| Editeur : | Boca Raton [États-Unis] : CRC Press, 2024 |
| ISBN/ISSN/EAN : | 978-1-03-247950-7 |
| Format : | 301 p. |
| Langues : | Anglais |
| Langues du résumé : | Anglais |
| Catégories : |
Catégories principales 06 - AGRICULTURE. FORÊTS. PÊCHES ; 6.4 - Production Agricole. Système de ProductionThésaurus IAMM AGRICULTURE ; AGRICULTURE NUMERIQUE ; AGRICULTURE DE PRECISION ; SYSTEME DE PRODUCTION ; TECHNOLOGIE DE L'INFORMATION ET DE LA COMMUNICATION ; APPROCHE OBJET ; AGRICULTURE URBAINE ; ENERGIE RENOUVELABLE ; PREVISION ; PETITE EXPLOITATION AGRICOLE ; CHANGEMENT CLIMATIQUE |
| Mots-clés: | APPRENTISSAGE AUTOMATIQUE |
| Résumé : |
Predictive Analysis in Smart Agricultureexplores computational engineering techniques and applications in agriculture development. Recent technologies such as cloud computing, IoT, big data, and machine learning are focused on for smart agricultural engineering. The book also provides a case-oriented approach for IoT-based agricultural systems.
This book deals with all aspects of smart agriculture with state-of-the-art predictive analysis in the complete 360-degree view spectrum. The book includes the concepts of urban and vertical farming using Agro IoT systems and renewable energy sources for modern agriculture trends. It discusses the real-world challenges, complexities in Agro IoT, and advantages of incorporating smart technology. It also presents the rapid advancement of the technologies in the existing Agri model by applying the various techniques. Novel architectural solutions in smart agricultural engineering are the core aspects of this book. Several predictive analysis tools and smart agriculture are also incorporated. This book can be used as a textbook for students in predictive analysis, agriculture engineering, precision farming, and smart agriculture. It can also be a reference book for practicing professionals in cloud computing, IoT, big data, machine learning, and deep learning working on smart agriculture applications. |
| Note de contenu : |
Chapter 1. Farming Assistance Using Machine Learning and Internet of Things
Chapter 2. Automated Seasonal Crop Mapping and Acreage Estimation Framework Using Machine Learning Algorithms: A Survey Chapter 3. Artificial Intelligence in Precision Agriculture: A Systematic Review on Tools, Techniques and Applications Chapter 4. Chatbot for Smart Farming using AI and NLP Techniques Chapter 5. Soil Analysis and Nutrient Recommendation System Using IoT and Multilayer Perceptron (MLP) Model Chapter 6. IoT Enabled Smart Irrigation with Machine Learning Models for Precision Farming Chapter 7. Leaf-CAP: A Capsule Network-based Tea Leaf Disease Recognition and Detection Chapter 8. Agri Retail Product Management System Chapter 9. Challenges and Prospects of Implementing Information and Communication Technology for Small-Scale Farmers. Chapter 10. Navigating Ethical and Legal Challenges in Smart Agriculture: Insights from Farmers Chapter 11. Decision Support System for Smart Agriculture in Predictive Analysis Chapter 12. Broad Framework of Digital Twins In Agricultural Domain Chapter 13. Predictive Analytics of Climate Change: The Future of Global Warming Lies in Data Analytics Chapter 14. Applications of Drones in Predictive Analytics Chapter 15. Autonomous Unmanned Ground Vehicles (UGVs) in Smart Agriculture |
| Cote : | I7-KRI-2024 |
| URL / DOI : | https://doi.org/10.1201/9781003391302 |
Exemplaires (1)
| Code-barres | Cote | Support | Localisation | Section | Disponibilité |
|---|---|---|---|---|---|
| 28943 | I7-KRI-2024 | Papier | Centre de documentation | Espace Thématique | Disponible |
