  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.
During the 2018 growing season, high definition pictures were captured with
side-looking action cameras in the Flevoland province of the Netherlands. Each
month from March to October, 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 for 17 crop types. Furthermore, the time span included specific
pre-emergence parcel stages, such as differently cultivated bare soil for
spring and summer crops as well as post-harvest cultivation practices, e.g.
green manuring and catch crops. 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.
