VGG-ICNN: A Lightweight CNN model for crop disease identification

Crop diseases cause a substantial loss in the quantum and quality of agricultural production. Regular monitoring may help in early stage disease detection an d thereby reduction in crop loss. An automatic plant disease identification system based on visual symptoms can provide a smart agriculture so...

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Veröffentlicht in:	Multimedia tools and applications Jg. 82; H. 1; S. 497 - 520
Hauptverfasser:	Thakur, Poornima Singh, Sheorey, Tanuja, Ojha, Aparajita
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	New York Springer US 01.01.2023 Springer Nature B.V
Schlagworte:	Agricultural production Artificial neural networks Computer Communication Networks Computer Science Crop diseases Data Structures and Information Theory Datasets Deep learning Image processing Lightweight Machine learning Medical imaging Multimedia Information Systems Plant diseases Signs and symptoms Special Purpose and Application-Based Systems Deep learning Crop disease identification Convolutional neural network Classification
ISSN:	1380-7501, 1573-7721
Online-Zugang:	Volltext
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