TY - GEN
T1 - Booming Blooming
T2 - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023
AU - Cheng, Yun Chiao
AU - Chou, Yan Hung
AU - Lin, Chia Yu
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - The blooming season is a crucial aspect of tourism in Taiwan, but it is subject to annual variations caused by weather factors such as rainfall and temperature. While many AI models are on the market for predicting flowering, they often need more applicability to specific regions due to climate variations. Moreover, Taiwan's climate is known for being changeable, which can further complicate flower prediction. Using Taiwan's climate and flowering date as training parameters, our model can achieve significantly higher accuracy than models that do not incorporate Taiwan's climate information. This paper presents an App called "Booming Blooming,"which integrates a flower prediction model with real-time weather information. The App utilizes weather data from Taiwan's Central Weather Bureau to predict the optimal time for flower viewing and provides users with up-to-date weather forecasts. With this App, users can plan their flower-viewing trips more effectively. Moreover, the App includes a built-in Google map to assist users in locating nearby stores, traffic conditions, and other people at popular flower-viewing locations. Additionally, Booming Blooming offers a flower-sharing platform where users can share the latest information on flower blooming conditions. Overall, the proposed flower blooming prediction model and App provide a convenient and efficient way for Taiwanese to enjoy flower-viewing activities.
AB - The blooming season is a crucial aspect of tourism in Taiwan, but it is subject to annual variations caused by weather factors such as rainfall and temperature. While many AI models are on the market for predicting flowering, they often need more applicability to specific regions due to climate variations. Moreover, Taiwan's climate is known for being changeable, which can further complicate flower prediction. Using Taiwan's climate and flowering date as training parameters, our model can achieve significantly higher accuracy than models that do not incorporate Taiwan's climate information. This paper presents an App called "Booming Blooming,"which integrates a flower prediction model with real-time weather information. The App utilizes weather data from Taiwan's Central Weather Bureau to predict the optimal time for flower viewing and provides users with up-to-date weather forecasts. With this App, users can plan their flower-viewing trips more effectively. Moreover, the App includes a built-in Google map to assist users in locating nearby stores, traffic conditions, and other people at popular flower-viewing locations. Additionally, Booming Blooming offers a flower-sharing platform where users can share the latest information on flower blooming conditions. Overall, the proposed flower blooming prediction model and App provide a convenient and efficient way for Taiwanese to enjoy flower-viewing activities.
UR - http://www.scopus.com/inward/record.url?scp=85174970770&partnerID=8YFLogxK
U2 - 10.1109/ICCE-Taiwan58799.2023.10226926
DO - 10.1109/ICCE-Taiwan58799.2023.10226926
M3 - 會議論文篇章
AN - SCOPUS:85174970770
T3 - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
SP - 335
EP - 336
BT - 2023 International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 17 July 2023 through 19 July 2023
ER -
