A Simple Algorithm for Oncidium Orchid Cut Flower Grading with Deep Learning

  • Yin Te TsaiEmail author
  • Hsing Cheng Wu
  • Shao Ming Zhu
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1080)


Utilizing emerging information technology in agriculture automation is arisen for reducing human errors and increasing the productivity and quality. This paper proposes a simple algorithm OCG with deep learning network to determine the grading levels of Oncidium orchid cut flowers which are related to the sale prices. The algorithm consists of two phases. The grading criteria about lengths are estimated by image analysis in the first phase, while the grading criteria about counting branches are predicted using deep learning in the second phase. The experimental results show that our algorithm can achieve accuracy of 0.8 and the algorithm is practical.


Oncidium orchid cut flower grading Branch detection Branch counting Deep learning 



This work was supported in part by Ministry of Science and Technology of Republic of China under grants MOST 107-2321-B-055-003 and 108-2321-B-055-002. We express special thanks to Chair B. H. Liao for supporting the field study and providing Oncidium orchid cut flowers. We also appreciate Yi Hsiu Tsai and Yi Yang Chen during setting up the experimental environment.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Computer Science and Communication EngineeringProvidence UniversityTaichungTaiwan, ROC
  2. 2.Department of Computer Science and Information EngineeringProvidence UniversityTaichungTaiwan, ROC

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