Deep Learning Based Classification System For Recognizing Local Spinach
This work addresses the specific problem of spinach species recognition for agricultural or botanical applications, but it is incremental as it applies existing CNN models to a new dataset.
The researchers tackled the problem of automatically identifying local spinach species using image classification, achieving high accuracy rates between 98.68% and 99.79%, with VGG16 reaching 99.79% accuracy.
A deep learning model gives an incredible result for image processing by studying from the trained dataset. Spinach is a leaf vegetable that contains vitamins and nutrients. In our research, a Deep learning method has been used that can automatically identify spinach and this method has a dataset of a total of five species of spinach that contains 3785 images. Four Convolutional Neural Network (CNN) models were used to classify our spinach. These models give more accurate results for image classification. Before applying these models there is some preprocessing of the image data. For the preprocessing of data, some methods need to happen. Those are RGB conversion, filtering, resize & rescaling, and categorization. After applying these methods image data are pre-processed and ready to be used in the classifier algorithms. The accuracy of these classifiers is in between 98.68% - 99.79%. Among those models, VGG16 achieved the highest accuracy of 99.79%.