Classification and Grading of Okra-ladies finger using Deep Learning
| Autor: | Chaitra Kuchanur, Pratiksha Benagi, Shantala Girraddi, Meena S M, Meenaxi M. Raikar |
|---|---|
| Rok vydání: | 2020 |
| Předmět: |
Contextual image classification
Computer science Machine vision business.industry Deep learning Hibiscus esculentus 020206 networking & telecommunications Dirt 02 engineering and technology food.food food Statistics 0202 electrical engineering electronic engineering information engineering General Earth and Planetary Sciences 020201 artificial intelligence & image processing Artificial intelligence Grading (education) business General Environmental Science |
| Zdroj: | Procedia Computer Science. 171:2380-2389 |
| ISSN: | 1877-0509 |
| DOI: | 10.1016/j.procs.2020.04.258 |
| Popis: | Okra (Hibiscus esculentus) is a fruit/vegetable crop commonly known as lady’s finger, Gumbo, Bhindi or gombo. The grading for Okra is performed to enable pricing equitableness. The different grades of the okra are based on the freshness, tenderness, color, shape, decay, scarred, bruised, cuts, insects, dirt, wormhole, and trim. In this paper, based on the length of the pod, four classifications performed are small, medium, large and extra-large. The machine vision technology is explored to grade the okra based on the length of the pod. Deep learning technology is emerging as the major approaches for signal and information processing with applications specific to image classification, speech recognition, and medical analysis. In grading the Okra’s three deep learning models: AlexNet, GoogLeNet and ResNet50 are used. The dataset of ladies finger is collected, and the size of the dataset is 3200 of all the sizes. The accuracies obtained are 63.45% for AlexNet, 68.99% for GoogLeNet model and 99% for ResNet50 which is better than the others. The challenges are in the identification of the tenderness of the pods, wormholes, insects, and dirt. |
| Databáze: | OpenAIRE |
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