mlsanityData to 14 Oct 2026

Deep Learning Based Classification System For Recognizing Local Spinach

arXiv 2201.02093 ↗First posted 6 Jan 2022cs.CV cs.LG eess.IVcited by 0

Not on the map. Why?

Abstract (arXiv)

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%.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 4 of its 14 references that are in the map. First posted on arXiv 6 Jan 2022.

Builds on

4 of 14 references.

References · 4

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,364
  2. 2015Rethinking the Inception Architecture for Computer Visioncited by 5,475
  3. 2014Going Deeper with Convolutionscited by 4,241
  4. 2016Xception: Deep Learning with Depthwise Separable Convolutionscited by 2,332

Built on by

Not cited yet.