Leaf counting in the presence of occlusion in Arabidopsis thaliana plant using convolutional neural networks

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dc.citation.epage052407-18
dc.citation.spage052407-1
dc.citation.volume32
dc.contributor.authorŠtaka, Zorana
dc.contributor.authorMišic, Marko
dc.date.accessioned2024-12-03T11:47:58Z
dc.date.available2024-12-03T11:47:58Z
dc.date.issued2023
dc.description.abstractPlants are crucial in providing sufficient food for the increasing global population. To be able to provide an appropriate amount of food, the maximization of agricultural output is needed while input needs to be minimized. For these purposes, plant phenotyping techniques, i.e., measuring and analyzing the physical and biochemical characteristics of plants, can be employed. One of the most essential indicators of the general health and development of the plant is the color, shape, and number of leaves. To analyze plant images and capture essential plant traits, various algorithms have been developed. However, one of the important challenges in developing these algorithms is the occlusion or overlapping of leaves and biomass. We present a solution for leaf counting in the presence of occlusion in the plant Arabidopsis thaliana that includes four different convolutional neural network architectures. Datasets from the Computer Vision Problems in Plant Phenotyping (CVPPP) 2017 challenge and Photon System Instruments were used. The results are discussed in detail and compared with the existing solutions. Results showed that our solutions for leaf counting are superior to the previous winners of the CVPPP challenges.
dc.identifier.doi10.1117/1.JEI.32.5.052407
dc.identifier.urihttps://vaseljena.ues.rs.ba/handle/123456789/1371
dc.language.isoen
dc.publisherSPIE
dc.sourceJournal of Electronic Imaging
dc.subjectleaf counting; convolutional neural networks; plant phenotyping; Arabidopsis thaliana; occlusion
dc.titleLeaf counting in the presence of occlusion in Arabidopsis thaliana plant using convolutional neural networks
dc.typeArticle
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Електротехнички факултет [Научни радови] / Faculty of Electrical Engineering [Scientific papers]