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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Vydáno v:Multimedia tools and applications Ročník 82; číslo 1; s. 497 - 520
Hlavní autoři: Thakur, Poornima Singh, Sheorey, Tanuja, Ojha, Aparajita
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York Springer US 01.01.2023
Springer Nature B.V
Témata:
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
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Shrnutí: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 solution to such problems. Various solutions for plant disease identification have been provided by researchers using image processing, machine learning and deep learning techniques. In this paper a lightweight Convolutional Neural Network ‘VGG-ICNN’ is introduced for the identification of crop diseases using plant-leaf images. VGG-ICNN consists of around 6 million parameters that are substantially fewer than most of the available high performing deep learning models. The performance of the model is evaluated on five different public datasets covering a large number of crop varieties. These include multiple crop species datasets: PlantVillage and Embrapa with 38 and 93 categories, respectively, and single crop datasets: Apple, Maize, and Rice, each with four, four, and five categories, respectively. Experimental results demonstrate that the method outperforms some of the recent deep learning approaches on crop disease identification, with 99.16% accuracy on the PlantVillage dataset. The model is also shown to perform consistently well on all the five datasets, as compared with some recent lightweight CNN models.
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ISSN:1380-7501
1573-7721
DOI:10.1007/s11042-022-13144-z