Kevin Sitati (4 results)

Taschenbuch. Condition: Neu. Automated Sugarcane Leaf Disease Detection Using AI Models | Comparative Analysis of Machine Learning and Deep Learning Models for Sugarcane Health | Kevin Sitati | Taschenbuch | Englisch | 2026 | GlobeEdit | EAN 9786209544873 | Verantwortliche Person für die EU: SIA OmniScriptum Publishing, Brivibas Gatve 197, 1039 RIGA, LETTLAND, customerservice[at]vdm-vsg[dot]de | Anbieter: preigu.…

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 52 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - This book explores the application of artificial intelligence in agriculture, focusing on automated detection of sugarcane leaf diseases. It presents a comprehensive study comparing classical machine learning models such as Decision Tree, Random Forest, AdaBoost, Gradient Boosting, SVM, and MLP with deep learning Convolutional Neural Networks. Using the Sugarcane Leaf Disease Dataset, the book details image preprocessing, feature extraction, model training, and evaluation with metrics such as accuracy, precision, recall, and F1-score. It highlights the advantages, challenges, and practical deployment potential of AI-driven disease detection in real-world agricultural settings, providing valuable insights for researchers, agronomists, and farmers seeking sustainable crop management solutions.…

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book explores the application of artificial intelligence in agriculture, focusing on automated detection of sugarcane leaf diseases. It presents a comprehensive study comparing classical machine learning models such as Decision Tree, Random Forest, AdaBoost, Gradient Boosting, SVM, and MLP with deep learning Convolutional Neural Networks. Using the Sugarcane Leaf Disease Dataset, the book details image preprocessing, feature extraction, model training, and evaluation with metrics such as accuracy, precision, recall, and F1-score. It highlights the advantages, challenges, and practical deployment potential of AI-driven disease detection in real-world agricultural settings, providing valuable insights for researchers, agronomists, and farmers seeking sustainable crop management solutions.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 52 pp. Englisch.…