Image based fruit category classification by 13-layer deep convolutional neural network and data augmentation

By Yudong Zhang, Zhengchao Dong, Xianqing Chen, Wenjuan Jia, S. Du, Khan Muhammad, Shuihua Wang. Multimedia tools and applications, 2019

351 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Industrial and Manufacturing Engineering, Plant Science

View Paper: https://www.semanticscholar.org/paper/5f2f6a697dabc784bbc4375f70d86df6a5de020a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Plant Science

1. Ruber Hernández-García
2. José Antonio Naranjo-Torres
3. Marco Mora
4. Andres Valenzuela
5. Claudio Fredes
6. Ricardo J. Barrientos

Citations in the corpus

• A Review of Convolutional Neural Network Applied to Fruit Image Processing (2020, methodology): “The study of fruit classification performed by Zhang et al. [25] compared the effect of three types of data augmentation method and max-pooling techniques on the accuracy, as well as using GPU and CPU hardware platforms.”
• Convolutional Neural Networks Based Fire Detection in Surveillance Videos (2018, background): “hand-written digits classification, it has shown promising 163 results for combating different problems including action 164 recognition [20], [21], pose estimation, image classifica165 tion [22]–[26], visual saliency detection, object tracking, 166 image segmentation, scene labeling, object localiz”