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Plant image identification application demonstrates high accuracy in Northern Europe.

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.

SUBMITTER: Partel J

PROVIDER: S-EPMC8387968 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background and aimsGlobal agriculture is facing the challenge of a phenotyping bottleneck due to large-scale screening/breeding experiments with improved breeds. Phenotypic analysis with high-throughput, high-accuracy and low-cost technologies has therefore become urgent. Recent advances in image-based 3D reconstruction offer the opportunity of high-throughput phenotyping. The main aim of this study was to quantify and evaluate the canopy structure of plant populations in two and three dimensions based on the multi-view stereo (MVS) approach, and to monitor plant growth and development from seedling stage to fruiting stage.MethodsMulti-view images of flat-leaf cucumber, small-leaf pepper and curly-leaf eggplant were obtained by moving a camera around the plant canopy. Three-dimensional point clouds were reconstructed from images based on the MVS approach and were then converted into surfaces with triangular facets. Phenotypic parameters, including leaf length, leaf width, leaf area, plant height and maximum canopy width, were calculated from reconstructed surfaces. Accurate evaluation in 2D and 3D for individual leaves was performed by comparing reconstructed phenotypic parameters with referenced values and by calculating the Hausdorff distance, i.e. the mean distance between two surfaces.Key resultsOur analysis demonstrates that there were good agreements in leaf parameters between referenced and estimated values. A high level of overlap was also found between surfaces of image-based reconstructions and laser scanning. Accuracy of 3D reconstruction of curly-leaf plants was relatively lower than that of flat-leaf plants. Plant height of three plants and maximum canopy width of cucumber and pepper showed an increasing trend during the 70 d after transplanting. Maximum canopy width of eggplants reached its peak at the 40th day after transplanting. The larger leaf phenotypic parameters of cucumber were mostly found at the middle-upper leaf position.ConclusionsHigh-accuracy 3D evaluation of reconstruction quality indicated that dynamic capture of the 3D canopy based on the MVS approach can be potentially used in 3D phenotyping for applications in breeding and field management.

Project description:Over the last century in the European context, animal production has been transformed by the dynamics of centralization and decentralization due to political and economic factors. These processes have influenced knowledge related to healing and ensuring the welfare of domestic animals. Therefore, our study aimed to document and compare current and past ethnoveterinary practices, and to identify trajectories in ethnoveterinary knowledge in study regions from both northern and southern Eastern Europe. In the summers of 2018 and 2019, we conducted 476 interviews, recording the use of 94 plant taxa, 67 of which were wild and 24 were cultivated. We documented 452 use reports, 24 of which were related to the improvement of the quality or quantity of meat and milk, while the other 428 involved ethnoveterinary practices for treating 10 domestic animal taxa. Cattle were the most mentioned target of ethnoveterinary treatments across all the study areas, representing about 70% of all use reports. Only four plant species were reported in five or more countries (Artemisia absinthium, Hypericum spp., Linum usitatissimum, Quercus robur). The four study regions located in Northern and Southern Eastern Europe did not present similar ethnoveterinary knowledge trajectories. Bukovinian mountain areas appeared to hold a living reservoir of ethnoveterinary knowledge, unlike the other regions. Setomaa (especially Estonian Setomaa) and Dzukija showed an erosion of ethnoveterinary knowledge with many uses reported in the past but no longer in use. The current richness of ethnoveterinary knowledge reported in Bukovina could have been developed and maintained through its peculiar geographical location in the Carpathian Mountains and fostered by the intrinsic relationship between the mountains and local pastoralists and by its unbroken continuity of management even during the Soviet era. Finally, our results show some patterns common to several countries and to the veterinary medicine promoted during the time of the Soviet Union. However, the Soviet Union and its centralized animal breeding system, resulted in a decline of ethnoveterinary knowledge as highly specialized veterinary doctors worked in almost every village. Future research should examine the complex networks of sources from where farmers derive their ethnoveterinary knowledge.

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Plant image identification application demonstrates high accuracy in Northern Europe.

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