Deep Learning Based Classification System For Recognizing Local Spinach

TL;DR

This paper presents a deep learning-based image classification system that accurately identifies five species of spinach using CNN models, achieving up to 99.79% accuracy after data preprocessing.

Contribution

It introduces a CNN-based approach with effective preprocessing techniques for high-accuracy spinach species classification.

Findings

VGG16 achieved 99.79% accuracy

Preprocessing improved model performance

Four CNN models tested for classification

Abstract

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%.