A Hybrid Method For Detecting Corn Leaf Diseases Using Capsule Neural Networks

Robert Scholz

ISBN 13: 9798182743846

Published by Pippet Sky

About this Item

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.

Seller Inventory # 9798182743846

Bibliographic Details

• Title: A Hybrid Method For Detecting Corn Leaf Diseases Using Capsule Neural Networks
• Publisher: Pippet Sky
• ISBN 13: 9798182743846
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 229W x 152H x 8D millimeters 229 millimeters width by 152 millimeters height by 8 millimeters depth
• Weight: 217 grams

About this title

Synopsis

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.