東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Machine vision in plant leaf disease...

Mridha, M. F.

FindBook

Google Book

Amazon

博客來

Machine vision in plant leaf disease detection for sustainable agriculture

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine vision in plant leaf disease detection for sustainable agriculture/ edited by M. F. Mridha, Nilanjan Dey.
其他作者:	Mridha, M. F.
出版者:	Singapore :Springer Nature Singapore : : 2025.,
面頁冊數:	ix, 167 p. :ill. (some col.), digital ;24 cm.
內容註:	Towards Explainable AI in Agriculture: SHAP-Interpretable Vision Transformer for Bean Disease Classification -- Challenges and Opportunities with Deep Learning and Computer Vision in disease free Agricultural Product Manufacturing -- Improving Agronomic Disease Detection and Classification: The Superiority of Hybrid Inception-Xception Ensemble Model for Rice Leaf Disease Classification -- Banana Leaf Spot Disease Detection using Deep Learning Algorithms -- A Computer Vision based Approach for Rice Pest Detection Using Deep Learning -- Revolutionizing Agriculture: Deep Learning Models for Crop Pest and Disease Analysis -- Corn Leaf Disease Detection Using Deep Convolutional Neural Networks and Grad-CAM Explainability -- A Hybrid CNN Architecture for Efficient Detection of Maize Plant Diseases -- Deep Learning based watermelon leaf Disease Classification -- Deploying CNN-ResNet50-BiLSTM for Paddy Leaf Disease Detection.
Contained By:	Springer Nature eBook
標題:	Sustainable agriculture. -
電子資源:	https://doi.org/10.1007/978-981-96-4520-6
ISBN:	9789819645206

Machine vision in plant leaf disease detection for sustainable agriculture
Machine vision in plant leaf disease detection for sustainable agriculture[electronic resource] /edited by M. F. Mridha, Nilanjan Dey. - Singapore :Springer Nature Singapore :2025. - ix, 167 p. :ill. (some col.), digital ;24 cm. - Studies in computational intelligence,v. 12021860-9503 ;. - Studies in computational intelligence ;v. 1202..

Towards Explainable AI in Agriculture: SHAP-Interpretable Vision Transformer for Bean Disease Classification -- Challenges and Opportunities with Deep Learning and Computer Vision in disease free Agricultural Product Manufacturing -- Improving Agronomic Disease Detection and Classification: The Superiority of Hybrid Inception-Xception Ensemble Model for Rice Leaf Disease Classification -- Banana Leaf Spot Disease Detection using Deep Learning Algorithms -- A Computer Vision based Approach for Rice Pest Detection Using Deep Learning -- Revolutionizing Agriculture: Deep Learning Models for Crop Pest and Disease Analysis -- Corn Leaf Disease Detection Using Deep Convolutional Neural Networks and Grad-CAM Explainability -- A Hybrid CNN Architecture for Efficient Detection of Maize Plant Diseases -- Deep Learning based watermelon leaf Disease Classification -- Deploying CNN-ResNet50-BiLSTM for Paddy Leaf Disease Detection.

This book offers a comprehensive exploration of the intersection between advanced technology and agricultural sustainability. With a focus on leveraging machine vision techniques for the early detection and management of plant diseases, this book serves as a vital resource for researchers, practitioners, and stakeholders in the agricultural sector. The book begins by providing an overview of the challenges posed by plant diseases to global food security and agricultural sustainability. It highlights the limitations of traditional disease detection methods and underscores the need for innovative approaches that can offer timely and accurate diagnosis. Through a systematic examination of machine vision principles and methodologies, the book delves into the various stages of disease detection, from image acquisition to feature extraction and classification. Key concepts such as image preprocessing, feature selection, and machine learning algorithms are discussed in detail, with emphasis on their practical implementation in real-world scenarios. Moreover, the book explores the potential of machine vision to contribute to sustainable agriculture practices.

ISBN: 9789819645206

Standard No.: 10.1007/978-981-96-4520-6doiSubjects--Topical Terms:

647177
Sustainable agriculture.

LC Class. No.: S494.5.S86

Dewey Class. No.: 630.2086

Machine vision in plant leaf disease detection for sustainable agriculture
LDR:03222nmm a2200337 a 4500 001 2414303
003 DE-He213
005 20250822130225.0
006 m d
007 cr nn 008maaau
008 260205s2025 si s 0 eng d
020 $a 9789819645206 $q (electronic bk.)
020 $a 9789819645190 $q (paper)
024 7 $a 10.1007/978-981-96-4520-6 $2 doi
035 $a 978-981-96-4520-6
040 $a GP $c GP
041 0 $a eng
050 4 $a S494.5.S86
072 7 $a UYQ $2 bicssc
072 7 $a COM004000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 630.2086 $2 23
090 $a S494.5.S86 $b M149 2025
245 0 0 $a Machine vision in plant leaf disease detection for sustainable agriculture $h [electronic resource] / $c edited by M. F. Mridha, Nilanjan Dey.
260 $a Singapore : $b Springer Nature Singapore : $b Imprint: Springer, $c 2025.
300 $a ix, 167 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Studies in computational intelligence, $x 1860-9503 ; $v v. 1202
505 0 $a Towards Explainable AI in Agriculture: SHAP-Interpretable Vision Transformer for Bean Disease Classification -- Challenges and Opportunities with Deep Learning and Computer Vision in disease free Agricultural Product Manufacturing -- Improving Agronomic Disease Detection and Classification: The Superiority of Hybrid Inception-Xception Ensemble Model for Rice Leaf Disease Classification -- Banana Leaf Spot Disease Detection using Deep Learning Algorithms -- A Computer Vision based Approach for Rice Pest Detection Using Deep Learning -- Revolutionizing Agriculture: Deep Learning Models for Crop Pest and Disease Analysis -- Corn Leaf Disease Detection Using Deep Convolutional Neural Networks and Grad-CAM Explainability -- A Hybrid CNN Architecture for Efficient Detection of Maize Plant Diseases -- Deep Learning based watermelon leaf Disease Classification -- Deploying CNN-ResNet50-BiLSTM for Paddy Leaf Disease Detection.
520 $a This book offers a comprehensive exploration of the intersection between advanced technology and agricultural sustainability. With a focus on leveraging machine vision techniques for the early detection and management of plant diseases, this book serves as a vital resource for researchers, practitioners, and stakeholders in the agricultural sector. The book begins by providing an overview of the challenges posed by plant diseases to global food security and agricultural sustainability. It highlights the limitations of traditional disease detection methods and underscores the need for innovative approaches that can offer timely and accurate diagnosis. Through a systematic examination of machine vision principles and methodologies, the book delves into the various stages of disease detection, from image acquisition to feature extraction and classification. Key concepts such as image preprocessing, feature selection, and machine learning algorithms are discussed in detail, with emphasis on their practical implementation in real-world scenarios. Moreover, the book explores the potential of machine vision to contribute to sustainable agriculture practices.
650 0 $a Sustainable agriculture. $3 647177
650 0 $a Artificial intelligence $x Agricultural applications. $3 3299845
650 0 $a Computer vision. $3 540671
650 0 $a Leaves $x Diseases and pests. $3 3790919
650 1 4 $a Computational Intelligence. $3 1001631
650 2 4 $a Machine Learning. $3 3382522
650 2 4 $a Agriculture. $3 518588
700 1 $a Mridha, M. F. $3 3753350
700 1 $a Dey, Nilanjan. $3 2200043
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Studies in computational intelligence ; $v v. 1202. $3 3790918
856 4 0 $u https://doi.org/10.1007/978-981-96-4520-6
950 $a Intelligent Technologies and Robotics (SpringerNature-42732)