Hybrid Method Detecting Corn by Scholz Robert (10 results)

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Paperback. Condition: new. Paperback. A Hybrid Method For Detecting Corn Leaf Diseases Using Capsule Neural Networks examines artificial intelligence and computer vision techniques for identifying diseases affecting corn leaves. The book focuses on image-based disease detection and the use of capsule neural networks for analyzing visual characteristics in agricultural images. It introduces concepts in deep learning, image processing, feature extraction, pattern recognition, neural network architectures, and agricultural data analysis. Attention is given to image preprocessing, representation of leaf characteristics, model development, classification, and performance evaluation. The hybrid approach is considered from a computational perspective, combining relevant analytical methods to support automated recognition of disease-related patterns in corn leaf images. The discussion provides technical background for understanding how deep learning and computer vision can be applied to agricultural disease analysis. This book is intended for students, researchers, agricultural engineers, computer scientists, data scientists, and professionals interested in precision agriculture, plant disease detection, machine learning, computer vision, and intelligent agricultural systems. A technical study of corn leaf disease detection using capsule neural networks, deep learning, computer vision, image processing, and agricultural analytics. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware 142 pp. Englisch.

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - A Hybrid Method For Detecting Corn Leaf Diseases Using Capsule Neural Networks examines artificial intelligence and computer vision techniques for identifying diseases affecting corn leaves. The book focuses on image-based disease detection and the use of capsule neural networks for analyzing visual characteristics in agricultural images. It introduces concepts in deep learning, image processing, feature extraction, pattern recognition, neural network architectures, and agricultural data analysis. Attention is given to image preprocessing, representation of leaf characteristics, model development, classification, and performance evaluation. The hybrid approach is considered from a computational perspective, combining relevant analytical methods to support automated recognition of disease-related patterns in corn leaf images. The discussion provides technical background for understanding how deep learning and computer vision can be applied to agricultural disease analysis. This book is intended for students, researchers, agricultural engineers, computer scientists, data scientists, and professionals interested in precision agriculture, plant disease detection, machine learning, computer vision, and intelligent agricultural systems.…

Paperback. Condition: new. Paperback. A Hybrid Method For Detecting Corn Leaf Diseases Using Capsule Neural Networks examines artificial intelligence and computer vision techniques for identifying diseases affecting corn leaves. The book focuses on image-based disease detection and the use of capsule neural networks for analyzing visual characteristics in agricultural images. It introduces concepts in deep learning, image processing, feature extraction, pattern recognition, neural network architectures, and agricultural data analysis. Attention is given to image preprocessing, representation of leaf characteristics, model development, classification, and performance evaluation. The hybrid approach is considered from a computational perspective, combining relevant analytical methods to support automated recognition of disease-related patterns in corn leaf images. The discussion provides technical background for understanding how deep learning and computer vision can be applied to agricultural disease analysis. This book is intended for students, researchers, agricultural engineers, computer scientists, data scientists, and professionals interested in precision agriculture, plant disease detection, machine learning, computer vision, and intelligent agricultural systems. A technical study of corn leaf disease detection using capsule neural networks, deep learning, computer vision, image processing, and agricultural analytics. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Paperback. Condition: new. Paperback. A Hybrid Method For Detecting Corn Leaf Diseases Using Capsule Neural Networks examines artificial intelligence and computer vision techniques for identifying diseases affecting corn leaves. The book focuses on image-based disease detection and the use of capsule neural networks for analyzing visual characteristics in agricultural images. It introduces concepts in deep learning, image processing, feature extraction, pattern recognition, neural network architectures, and agricultural data analysis. Attention is given to image preprocessing, representation of leaf characteristics, model development, classification, and performance evaluation. The hybrid approach is considered from a computational perspective, combining relevant analytical methods to support automated recognition of disease-related patterns in corn leaf images. The discussion provides technical background for understanding how deep learning and computer vision can be applied to agricultural disease analysis. This book is intended for students, researchers, agricultural engineers, computer scientists, data scientists, and professionals interested in precision agriculture, plant disease detection, machine learning, computer vision, and intelligent agricultural systems. A technical study of corn leaf disease detection using capsule neural networks, deep learning, computer vision, image processing, and agricultural analytics. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.…

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -A Hybrid Method For Detecting Corn Leaf Diseases Using Capsule Neural Networks examines artificial intelligence and computer vision techniques for identifying diseases affecting corn leaves. The book focuses on image-based disease detection and the use of capsule neural networks for analyzing visual characteristics in agricultural images. It introduces concepts in deep learning, image processing, feature extraction, pattern recognition, neural network architectures, and agricultural data analysis. Attention is given to image preprocessing, representation of leaf characteristics, model development, classification, and performance evaluation. The hybrid approach is considered from a computational perspective, combining relevant analytical methods to support automated recognition of disease-related patterns in corn leaf images. The discussion provides technical background for understanding how deep learning and computer vision can be applied to agricultural disease analysis. This book is intended for students, researchers, agricultural engineers, computer scientists, data scientists, and professionals interested in precision agriculture, plant disease detection, machine learning, computer vision, and intelligent agricultural systems. 142 pp. Englisch.…

Taschenbuch. Condition: Neu. A Hybrid Method For Detecting Corn Leaf Diseases Using Capsule Neural Networks | Robert Scholz | Taschenbuch | Englisch | 2026 | Pippet Sky | EAN 9798182743846 | Verantwortliche Person für die EU: Libri GmbH, Europaallee 1, 36244 Bad Hersfeld, gpsr[at]libri[dot]de | Anbieter: preigu Print on Demand.…