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Classification of Zophobas morio and Tenebrio molitor using transfer learning

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Zophobas Morio and Tenebrio Molitor are popular larvae as feed ingredients that are widely used by animal lovers to feed reptiles, songbirds, and other poultry. These two larvae share a similar appearance, however; the nutritional ingredients are significantly different. Zophobas Morio is more nutritious and has a higher economic value compared to Tenebrio Molitor . Due to limited knowledge, many animal lovers find it difficult to distinguish between the two. This study aims to build a machine learning model that is able to distinguish between the two. The model is trained using images that are taken from a standard camera on a mobile phone. The training is carried on using a deep learning algorithm, by adopting an architecture through transfer learning, namely VGG-19 and Inception v3. The experimental results on the datasets show that the accuracy rates of the model are 94.219% and 96.875%, respectively. The results are quite promising for practical use and can be improved for future works.
Title: Classification of Zophobas morio and Tenebrio molitor using transfer learning
Description:
Zophobas Morio and Tenebrio Molitor are popular larvae as feed ingredients that are widely used by animal lovers to feed reptiles, songbirds, and other poultry.
These two larvae share a similar appearance, however; the nutritional ingredients are significantly different.
Zophobas Morio is more nutritious and has a higher economic value compared to Tenebrio Molitor .
Due to limited knowledge, many animal lovers find it difficult to distinguish between the two.
This study aims to build a machine learning model that is able to distinguish between the two.
The model is trained using images that are taken from a standard camera on a mobile phone.
The training is carried on using a deep learning algorithm, by adopting an architecture through transfer learning, namely VGG-19 and Inception v3.
The experimental results on the datasets show that the accuracy rates of the model are 94.
219% and 96.
875%, respectively.
The results are quite promising for practical use and can be improved for future works.

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