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A methodical analysis of deep learning techniques for detecting Indian lentils

Automatic identification of Indian lentils requires the selection of a suitable classification model among various CNN models. The CNN models are the most effective for image classification. However, more than one CNN model occasionally performs closer to each other. Choosing the best classification...

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Bibliographic Details
Published in:Journal of agriculture and food research 2024-03, Vol.15, p.100943, Article 100943
Main Authors: Naik, Nabin Kumar, Sethy, Prabira Kumar, Behera, Santi Kumari, Amat, Rajat
Format: Article
Language:English
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Summary:Automatic identification of Indian lentils requires the selection of a suitable classification model among various CNN models. The CNN models are the most effective for image classification. However, more than one CNN model occasionally performs closer to each other. Choosing the best classification model requires considerable effort in this situation. Automated identification of lentils would allow for faster selection processes and reduced time compared to manual assessment. In this study, 18 CNN models were tested to identify the lentils. The performance of the CNN models was investigated and a two-phase statistical analysis was conducted to select the best CNN model for identifying Indian lentils. The 18 CNN models are Alexnet, Darknet19, Darknet53, Densenet, EfficientNetB0, Google net, InceptionResnetV2, InceptionV3, MobilenetV2, NasnetLarge, NasnetMobile, Resnet18, Resnet50, Resnet101, Squeezenet, Vgg16, Vgg19, Xception. The two-phase statistical analysis was Duncan's multiple range test, and the Wilcoxon signed-rank test was performed. Again, nine measures, such as accuracy, sensitivity, specificity, precision, FPR, F1 Score, MCC, Kappa, and computational time, were considered for statistical analysis. After the execution of 18 CNN models and two-phase statistical analysis, it was revealed that EfficientNetB0 is superior among the CNN models for the identification of lentils. [Display omitted] •It established a new dataset of Indian Lentils of, each comprising 500 images.•Identification of twenty varieties of India Lentils via 18 CNN models.•A two-stage statistical analysis was carried out to choose the best model.•EfficientNet is the best model for the classification of Indian Lentils.•The established Indian Lentil dataset covers most beans, peas, and grams.
ISSN:2666-1543
2666-1543
DOI:10.1016/j.jafr.2023.100943