Rice Leaf Disease Classification—A Comparative Approach Using Convolutional Neural Network (CNN), Cascading Autoencoder with Attention Residual U-Net (CAAR-U-Net), and MobileNet-V2 Architectures

Classifying rice leaf diseases in agricultural technology helps to maintain crop health and to ensure a good yield. In this work, deep learning algorithms were, therefore, employed for the identification and classification of rice leaf diseases from images of crops in the field. The initial algorith...

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Vydané v:Technologies (Basel) Ročník 12; číslo 11; s. 214
Hlavní autori: Dutta, Monoronjon, Islam Sujan, Md Rashedul, Mojumdar, Mayen Uddin, Chakraborty, Narayan Ranjan, Marouf, Ahmed Al, Rokne, Jon G., Alhajj, Reda
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Basel MDPI AG 01.11.2024
Predmet:
Accuracy
Algorithms
Artificial neural networks
cascading autoencoder with attention residual U-net (CAAR-U-Net)
Classification
convolutional neural network (CNN)
Datasets
Deep learning
Identification
Image filters
Image quality
Leaves
Machine learning
Medical imaging
mobilenetv2
neural network architectures
Neural networks
Pathogens
Plant diseases
Rice
rice leaf disease
Signal quality
Signal to noise ratio
ISSN:2227-7080, 2227-7080
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Shrnutí:Classifying rice leaf diseases in agricultural technology helps to maintain crop health and to ensure a good yield. In this work, deep learning algorithms were, therefore, employed for the identification and classification of rice leaf diseases from images of crops in the field. The initial algorithmic phase involved image pre-processing of the crop images, using a bilateral filter to improve image quality. The effectiveness of this step was measured by using metrics like the Structural Similarity Index (SSIM) and the Peak Signal-to-Noise Ratio (PSNR). Following this, this work employed advanced neural network architectures for classification, including Cascading Autoencoder with Attention Residual U-Net (CAAR-U-Net), MobileNetV2, and Convolutional Neural Network (CNN). The proposed CNN model stood out, since it demonstrated exceptional performance in identifying rice leaf diseases, with test Accuracy of 98% and high Precision, Recall, and F1 scores. This result highlights that the proposed model is particularly well suited for rice leaf disease classification. The robustness of the proposed model was validated through k-fold cross-validation, confirming its generalizability and minimizing the risk of overfitting. This study not only focused on classifying rice leaf diseases but also has the potential to benefit farmers and the agricultural community greatly. This work highlights the advantages of custom CNN models for efficient and accurate rice leaf disease classification, paving the way for technology-driven advancements in farming practices.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
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ISSN:2227-7080
2227-7080
DOI:10.3390/technologies12110214