Yanikoglu, B.Aptoula, E.Tirkaz, C.2024-05-252024-05-252014650932-80921432-176910.1007/s00138-014-0612-72-s2.0-84904468634https://doi.org/10.1007/s00138-014-0612-7https://hdl.handle.net/20.500.14517/738Aptoula, Erchan/0000-0001-6168-2883; Yanikoglu, Berrin/0000-0001-7403-7592We present a plant identification system for automatically identifying the plant in a given image. In addition to common difficulties faced in object recognition, such as light, pose and orientation variations, there are further difficulties particular to this problem, such as changing leaf shapes according to plant age and changes in the overall shape due to leaf composition. Our system uses a rich variety of shape, texture and color features, some being specific to the plant domain. The system has achieved the best overall score in the ImageCLEF'12 plant identification campaign in both the automatic and human-assisted categories. We report the results of this system on the publicly available ImageCLEF' 12 plant dataset, as well as the effectiveness of individual features. The results show 61 and 81 % accuracies in classifying the 126 different plant species in the top-1 and top-5 choices.eninfo:eu-repo/semantics/closedAccessPlant identificationLeaf shapeColor image segmentationMathematical morphologyArticleQ2Q225613691383WOS:000342435400001