Description
Monitoring crop phenology with streer-level imagery using computer vision
Contact
Contributors
-
- European Commission, Joint Research Centre
- https://ec.europa.eu/jrc/en
How to cite
European Commission, Joint Research Centre (JRC) (2021): FlevoVision. European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/343b89a6-14c0-4602-b62e-a94eb8d8fddc
Data access
Publications
- ELSEVIER SCI LTD, OXFORD, ENGLAND
-
Abstract
Street-level imagery holds a significant potential to scale-up in-situ data collection. This is enabled by combining the use of cheap high quality cameras with recent advances in deep learning compute solutions to derive relevant thematic information. We present a framework to collect and extract crop type and phenological information from street level imagery using computer vision. Each month in 2018, a fixed 200-km route was surveyed collecting one picture per second resulting in a total of 400,000 geo-tagged pictures. At 220 specific parcel locations detailed on the spot crop phenology observations were recorded. Classification was done using TensorFlow with a well-known image recognition model, based on transfer learning with convolutional neural networks (MobileNet). A hypertuning methodology was developed to obtain the best performing model among 160 models. This best model was applied on an independent inference set discriminating crop type with a Macro F1 score of 88.1% and main phenological stage at 86.9% at the parcel level. Potential and caveats of the approach along with practical considerations for implementation and improvement are discussed. The proposed framework speeds up high quality in-situ data collection and suggests avenues for massive data collection via automated classification using computer vision.
Geographic areas
Temporal coverage
From date | To date |
---|---|
2018-01-01 | 2018-12-31 |
Additional information
- Published by
- European Commission, Joint Research Centre
- Created date
- 2021-10-01
- Modified date
- 2022-04-04
- Issued date
- 2021-10-01
- Data theme(s)
- Agriculture, fisheries, forestry and food
- Update frequency
- monthly
- Identifier
- http://data.europa.eu/89h/343b89a6-14c0-4602-b62e-a94eb8d8fddc
- Popularity
-