Unsupervised Segmentation of Greenhouse Plant Images Based on Statistical Method

Abstract Complicated image scene of the agricultural greenhouse plant images makes it very difficult to obtain precise manual labeling, leading to the hardship of getting the accurate training set of the conditional random field (CRF). Considering this problem, this paper proposed an unsupervised co...

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Autores principales: Ping Zhang, Lihong Xu
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Lenguaje:EN
Publicado: Nature Portfolio 2018
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Acceso en línea:https://doaj.org/article/df18495121a34fe3b339323b7099a290
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spelling oai:doaj.org-article:df18495121a34fe3b339323b7099a2902021-12-02T15:08:24ZUnsupervised Segmentation of Greenhouse Plant Images Based on Statistical Method10.1038/s41598-018-22568-32045-2322https://doaj.org/article/df18495121a34fe3b339323b7099a2902018-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-018-22568-3https://doaj.org/toc/2045-2322Abstract Complicated image scene of the agricultural greenhouse plant images makes it very difficult to obtain precise manual labeling, leading to the hardship of getting the accurate training set of the conditional random field (CRF). Considering this problem, this paper proposed an unsupervised conditional random field image segmentation algorithm ULCRF (Unsupervised Learning Conditional Random Field), which can perform fast unsupervised segmentation of greenhouse plant images, and further the plant organs in the image, i.e. fruits, leaves and stems, are segmented. The main idea of this algorithm is to calculate the unary potential, namely the initial label of the Dense CRF, by the unsupervised learning model LDA (Latent Dirichlet Allocation). In view of the ever-changing image features at different stages of fruit growth, a multi-resolution ULCRF is proposed to improve the accuracy of image segmentation in the middle stage and late stage of the fruit growth. An image is down-sampled twice to obtain three layers of different resolution images, and the features of each layer are interrelated with each other. Experiment results show that the proposed method can segment greenhouse plant images in an unsupervised method automatically and obtain a high segmentation accuracy together with a high extraction precision of the fruit part.Ping ZhangLihong XuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 8, Iss 1, Pp 1-13 (2018)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Ping Zhang
Lihong Xu
Unsupervised Segmentation of Greenhouse Plant Images Based on Statistical Method
description Abstract Complicated image scene of the agricultural greenhouse plant images makes it very difficult to obtain precise manual labeling, leading to the hardship of getting the accurate training set of the conditional random field (CRF). Considering this problem, this paper proposed an unsupervised conditional random field image segmentation algorithm ULCRF (Unsupervised Learning Conditional Random Field), which can perform fast unsupervised segmentation of greenhouse plant images, and further the plant organs in the image, i.e. fruits, leaves and stems, are segmented. The main idea of this algorithm is to calculate the unary potential, namely the initial label of the Dense CRF, by the unsupervised learning model LDA (Latent Dirichlet Allocation). In view of the ever-changing image features at different stages of fruit growth, a multi-resolution ULCRF is proposed to improve the accuracy of image segmentation in the middle stage and late stage of the fruit growth. An image is down-sampled twice to obtain three layers of different resolution images, and the features of each layer are interrelated with each other. Experiment results show that the proposed method can segment greenhouse plant images in an unsupervised method automatically and obtain a high segmentation accuracy together with a high extraction precision of the fruit part.
format article
author Ping Zhang
Lihong Xu
author_facet Ping Zhang
Lihong Xu
author_sort Ping Zhang
title Unsupervised Segmentation of Greenhouse Plant Images Based on Statistical Method
title_short Unsupervised Segmentation of Greenhouse Plant Images Based on Statistical Method
title_full Unsupervised Segmentation of Greenhouse Plant Images Based on Statistical Method
title_fullStr Unsupervised Segmentation of Greenhouse Plant Images Based on Statistical Method
title_full_unstemmed Unsupervised Segmentation of Greenhouse Plant Images Based on Statistical Method
title_sort unsupervised segmentation of greenhouse plant images based on statistical method
publisher Nature Portfolio
publishDate 2018
url https://doaj.org/article/df18495121a34fe3b339323b7099a290
work_keys_str_mv AT pingzhang unsupervisedsegmentationofgreenhouseplantimagesbasedonstatisticalmethod
AT lihongxu unsupervisedsegmentationofgreenhouseplantimagesbasedonstatisticalmethod
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