Plant image identification application demonstrates high accuracy in Northern Europe. Issue 4 (27th July 2021)

• Record Type: Journal Article
• Authors: Pärtel, Jaak; Pärtel, Meelis; Wäldchen, Jana
• Editors: Martin, Adam
• Is Part Of: AoB plants. Volume 13:Issue 4(2021)
• Publication Date: 2021-07-27
• Subjects: Artificial intelligence -- automated plant species identification -- citizen science -- convolutional neural networks -- deep learning -- Estonian flora -- Flora Incognita -- identification application -- plant identification; Plants -- Periodicals; Botany -- Periodicals; 580.5
• DOI: 10.1093/aobpla/plab050
• Languages: English
• ISSNs: 2041-2851

Abstract

Abstract: Automated image-based plant identification has experienced rapid development and has been already used in research and nature management. However, there is a need for extensive studies on how accurately automatic plant identification works and which characteristics of observations and study species influence the results. We investigated the accuracy of the Flora Incognita application, a research-based tool for automated plant image identification. Our study was conducted in Estonia, Northern Europe. Photos originated from the Estonian national curated biodiversity observations database, originally without the intention to use them for automated identification (1496 photos, 542 species) were examined. Flora Incognita was also directly tested in field conditions in various habitats, taking images of plant organs as guided by the application (998 observations, 1703 photos, 280 species). Identification accuracy was compared among species characteristics: plant family, growth forms and life forms, habitat type and regional frequency. We also analysed image characteristics (plant organs, background, number of species in focus), and the number of training images that were available for particular species to develop the automated identification algorithm. From database images 79.6 % of species were correctly identified by Flora Incognita ; in the field conditions species identification accuracy reached 85.3 %. Overall, the correct genus was found for 89 % and the correct plant family for 95 % of the species. Accuracy varied among different plant families, life forms and growth forms. Rare and common species and species from different habitats were identified with equal accuracy. Images with reproductive organs or with only the target species in focus were identified with greater success. The number of training images per species was positively correlated with the identification success. Even though a high accuracy has been already achieved for Flora Incognita, allowing its usage for research and practices, our results can guide further improvements of this application and automated plant identification in general. Abstract : During the age of a global biodiversity crisis, it is increasingly important to recognize which plant species surround us in order to protect them. One solution to improve knowledge about plants is using image-based identification applications powered by artificial intelligence. We examined several thousand images of hundreds of species from Northern Europe to explore one such application, Flora Incognita . We found a high accuracy but not in all plant groups. Performance of the application was also dependent how many training images machine learning had used per species. Even more accurate identification could be expected with additional training data.