A fruit and vegetable recognition system based on EasyDL artificial intelligence development platform

Zihan Tian; Rong Chang; Haofei Ma; Peiyong Zhou

doi:10.1117/12.3041731

29 August 2024 A fruit and vegetable recognition system based on EasyDL artificial intelligence development platform

Zihan Tian, Rong Chang, Haofei Ma, Peiyong Zhou

Proceedings Volume 13249, International Conference on Computer Vision, Robotics, and Automation Engineering (CRAE 2024); 132490Z (2024) https://doi.org/10.1117/12.3041731
Event: 2024 International Conference on Computer Vision, Robotics and Automation Engineering, 2024, Kunming, China

Abstract

Intelligent identification of fruits and vegetables can not only help reduce waste in picking and selling fruits and vegetables, but also effectively reduce production costs and sales prices. Therefore, it can provide better quality and more affordable fruits and vegetables products. In recent years, with the development of artificial intelligence technology, related algorithms based on deep learning have become a research hotspot. Fruit and vegetable identification technology based on deep learning technology can reduce labor costs in the food industry and standardize the industry. We propose a method for identifying fruits and vegetables based on the EasyDL artificial intelligence development platform. This method uses the EasyDL image classification model to design a data expansion process based on the image characteristics of fruits and vegetables. It utilizes multiple training methods of the EasyDL artificial intelligence development platform. It finally achieves accurate identification of fruit and vegetable types.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Zihan Tian, Rong Chang, Haofei Ma, and Peiyong Zhou "A fruit and vegetable recognition system based on EasyDL artificial intelligence development platform", Proc. SPIE 13249, International Conference on Computer Vision, Robotics, and Automation Engineering (CRAE 2024), 132490Z (29 August 2024); https://doi.org/10.1117/12.3041731

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