Pl@ntNet Crops: merging citizen science observations and structured survey data to improve crop recognition for agri-food-environment applications
2023
VAN DER VELDE Marijn | GOEAU Herve | BONNET Pierre | D'ANDRIMONT Raphael | YORDANOV Momchil | AFFOUARD Antoine | CLAVERIE Martin | CZUCZ Balint | ELVEKJAER Neija | MARTINEZ SANCHEZ Laura | ROTLLAN PUIG Xavier | SIMA Aleksandra | VERHEGGHEN Astrid | JOLY Alexis
We present a new application to recognize 218 species of cultivated crops on geo-tagged photos, ‘Pl@ntNet Crops’. The application and underlying algorithms are developed using more than 750k photos voluntarily collected by Pl@ntNet users. The app is then enriched by data and photos coming from the European Union’s (EU) Land Use and Coverage Area frame Survey (LUCAS). During five tri-annual LUCAS campaigns from 2006 to 2018, 242 476 close-up ‘cover’ photos of crops were collected. The survey protocol for these photos specified that ‘the picture should be taken at a close distance, so that the structure of leaves can be clearly seen, as well as flowers or fruits’. This unique labelled data provides an opportunity to further generalize the Pl@ntNet computer vision algorithms to recognize crops and enlarge their geographic representivity across the EU. To include LUCAS cover photos, we semantically match Pl@ntNet species and LUCAS legends, predict the species on LUCAS cover photos with the existing Pl@ntNet algorithm, and consider the accuracy of the classification and the number of species enriched by the photos. By setting a threshold of 0.5 on the Pl@ntNet prediction probabilities, 70 170 LUCAS photos representing 101 species classified with an accuracy of 0.9 were added to the ‘Crops’ app. The thematic accuracy of the legacy LUCAS data was improved by distinguishing 218 species, opposed to the original 36 LUCAS levels.
显示更多 [+] 显示较少 [-]JRC.D.5 - Food Security
显示更多 [+] 显示较少 [-]