English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

Image recognition supports several applications, for instance, facial recognition, image classification, and achieving accurate fruit and vegetable classification is very important in fresh supply chain, factories, supermarkets, and other fields. In this paper, we develop a hybrid deep learning-based fruit image classification framework, named attention-based densely connected convolutional networks with convolution autoencoder (CAE-ADN), which uses a convolution autoencoder to pre-train the images and uses an attention-based DenseNet to extract the features of image. In the first part of the framework, an unsupervised method with a set of images is applied to pre-train the greedy layer-wised CAE. We use CAE structure to initialize a set of weights and bias of ADN. In the second part of the framework, the supervised ADN with the ground truth is implemented. The final part of the framework makes a prediction of the category of fruits. We use two fruit datasets to test the effectiveness of the model, experimental results show the effectiveness of the framework, and the framework can improve the efficiency of fruit sorting, which can reduce costs of fresh supply chain, factories, supermarkets, etc.

A hybrid deep learning-based fruit classification using attention model and convolution autoencoder