Flower Recognition

an app for iOS

Download from:

(for China-only)

The Flower Recognition app is really as straightforward as it sounds – take a picture of a flower, and our app will tell you its common name, as well as a few other details. Simple!

Created for iOS, Flower Recognition is local to China only – no online connections is required, so feel free to take it with you on a mountain hike, or anywhere a data connection may be iffy.

Meet the team

Microsoft Research
"You mean we could instantly identify local flowers?"
Chinese Botanist

Garage Team

Xi Li, Xin Hua, Fang Yin, Chenxi Wang, Long Chen, Zhaoyang Zeng, Ambrosio Blanco, Guobin Wu, Kaiming Qu, Jianlong Fu, Hao Ni, Xiaochen Zhao, Fang Qi, Mingyu Guo

Microsoft Research

Beijing, China

Backstory

If you go on walks and hikes around China, you may encounter flowers you don’t recognize. With Flower Recognition, an app for iOS, quickly identify the flower or any other plant native to China. At least 250,000 species of flowers exist and even experienced botanists have trouble identifying them all. Now there’s a way thanks to the rising power and sophistication of image recognition and the ease of taking pictures with your smartphone.

This app is not only for a hobbyist. Botanists can also benefit from the science behind this project. Flower Recognition might never have happened were it not for a chance encounter last year between Microsoft researchers and botanists at the Institute of Botany, Chinese Academy of Sciences (IBCAS). Yong Rui, assistant managing director of Microsoft Research Asia (MSRA), was explaining image-recognition technology at a seminar — much to the delight of IBCAS botanists whose own arduous efforts to collect data on regional flower distribution were experiencing poor results. The IBCAS botanists soon realized the potential of MSRA’s image-recognition technology. At the same time, Yong Rui knew he had found the perfect vehicle to improve image recognition while addressing a reality-based problem that benefits society. It also helped that IBCAS had accumulated a massive public store of 2.6 million images. Since anyone in the world could upload pictures to this flower photo dataset — and no human could possibly supervise the uploads — the MSRA team had to create algorithms to filter out the “bad” pictures. That was the first of many difficult problems facing researcher Jianlong Fu and his team in building a tool capable of discerning tiny anomalies among the many species of flowers.

To do so they trained a deep neural network to recognize images using a set of learnable filters. Inputting millions of pictures into the deep-learning framework, MSRA researchers eventually enabled the engine to accurately identify images more than 90 percent of the time, an astonishing rate just shy of human capabilities.

We hope you enjoy this learning tool.