AI-Based Plant Disease Detection Using Deep Convolutional Neural Networks
Language: English
Published by SHARK NAIL, 2026
- Softcover
- New
Seller: AHA-BUCH GmbH, Einbeck, GermanyAHA-BUCH GmbH
AbeBooks seller since August 14, 2006
Condition: New
US$ 47.10
Quantity: 2 available
Add to basketItem description from seller
nach der Bestellung gedruckt Neuware - Printed after ordering - AI-Based Plant Disease Detection Using Deep Convolutional Neural Networks presents a focused technical treatment of computer vision and deep learning methods for identifying plant diseases from leaf images. The book explains how image-based analysis can support automated disease recognition by combining visual feature extraction, convolutional neural networks, image classification, and machine learning principles. It introduces the role of digital plant imagery in agricultural diagnostics and describes how deep neural architectures learn discriminative patterns associated with visible symptoms such as lesions, discoloration, spots, and other disease-related changes.The book provides a structured view of the computational pipeline used in AI-based plant disease detection, from image acquisition and preparation through feature learning, model training, classification, and evaluation. Particular attention is given to deep convolutional neural networks because they can learn hierarchical image representations directly from visual data, reducing dependence on manually engineered features. The discussion places these methods within the broader context of precision agriculture, smart farming, crop monitoring, and agricultural information systems.…
Seller Inventory # 9781962116602
- Title
- AI-Based Plant Disease Detection Using Deep Convolutional Neural Networks
- Author
- Nancy Kova
- Publisher
- SHARK NAIL
- Publication year
- 2026
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 1962116603
- ISBN 13
- 9781962116602
- Item weight
- 217 grams
- Dimensions
- 229x152x8 mm
AI-Based Plant Disease Detection Using Deep Convolutional Neural Networks presents a focused technical treatment of computer vision and deep learning methods for identifying plant diseases from leaf images. The book explains how image-based analysis can support automated disease recognition by combining visual feature extraction, convolutional neural networks, image classification, and machine learning principles. It introduces the role of digital plant imagery in agricultural diagnostics and describes how deep neural architectures learn discriminative patterns associated with visible symptoms such as lesions, discoloration, spots, and other disease-related changes.
The book provides a structured view of the computational pipeline used in AI-based plant disease detection, from image acquisition and preparation through feature learning, model training, classification, and evaluation. Particular attention is given to deep convolutional neural networks because they can learn hierarchical image representations directly from visual data, reducing dependence on manually engineered features. The discussion places these methods within the broader context of precision agriculture, smart farming, crop monitoring, and agricultural information systems.
"Synopsis" may belong to another edition of this title.
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|---|---|---|
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