Mask R‐CNN‐based feature extraction and three‐dimensional recognition of rice panicle CT images
Abstract The rice panicle seed setting rate is extremely important for calculating rice yield and performing genetic analysis. Unlike machine vision, X‐ray computed tomography (CT) imaging is a nondestructive technique that provides direct information on the internal and external structure of rice p...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2021-05-01
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| Series: | Plant Direct |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/pld3.323 |
| _version_ | 1818949720271224832 |
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| author | Huihua Kong Ping Chen |
| author_facet | Huihua Kong Ping Chen |
| author_sort | Huihua Kong |
| collection | DOAJ |
| description | Abstract The rice panicle seed setting rate is extremely important for calculating rice yield and performing genetic analysis. Unlike machine vision, X‐ray computed tomography (CT) imaging is a nondestructive technique that provides direct information on the internal and external structure of rice panicles. However, occlusion and adhesion of panicles and grains in a CT image sequence make these objects difficult to identify, which in turn hinders accurate determination of the seed setting rate of rice panicles. Therefore, this paper proposes a method based on a mask region convolutional neural network (Mask R‐CNN) for feature extraction and three‐dimensional (3‐D) recognition of CT images of rice panicles. X‐ray CT feature characterization was combined with the Mask R‐CNN algorithm to perform feature extraction and classification of a panicle and grains in each layer of the CT sequence. The Euclidean distance between adjacent layers was minimized to extract the features of a 3‐D panicle and grains. The results were used to calculate the rice panicle seed setting rate. The proposed method was experimentally verified using eight sets of different rice panicles. The results showed that the proposed method can efficiently identify and count plump grains and blighted grains to achieve an accuracy above 99% for the seed setting rate. |
| first_indexed | 2024-12-20T09:07:11Z |
| format | Article |
| id | doaj.art-9c08e2d69e7a4c239647cbb5faac2a22 |
| institution | Directory Open Access Journal |
| issn | 2475-4455 |
| language | English |
| last_indexed | 2024-12-20T09:07:11Z |
| publishDate | 2021-05-01 |
| publisher | Wiley |
| record_format | Article |
| series | Plant Direct |
| spelling | doaj.art-9c08e2d69e7a4c239647cbb5faac2a222022-12-21T19:45:41ZengWileyPlant Direct2475-44552021-05-0155n/an/a10.1002/pld3.323Mask R‐CNN‐based feature extraction and three‐dimensional recognition of rice panicle CT imagesHuihua Kong0Ping Chen1North University of China Taiyuan ChinaNorth University of China Taiyuan ChinaAbstract The rice panicle seed setting rate is extremely important for calculating rice yield and performing genetic analysis. Unlike machine vision, X‐ray computed tomography (CT) imaging is a nondestructive technique that provides direct information on the internal and external structure of rice panicles. However, occlusion and adhesion of panicles and grains in a CT image sequence make these objects difficult to identify, which in turn hinders accurate determination of the seed setting rate of rice panicles. Therefore, this paper proposes a method based on a mask region convolutional neural network (Mask R‐CNN) for feature extraction and three‐dimensional (3‐D) recognition of CT images of rice panicles. X‐ray CT feature characterization was combined with the Mask R‐CNN algorithm to perform feature extraction and classification of a panicle and grains in each layer of the CT sequence. The Euclidean distance between adjacent layers was minimized to extract the features of a 3‐D panicle and grains. The results were used to calculate the rice panicle seed setting rate. The proposed method was experimentally verified using eight sets of different rice panicles. The results showed that the proposed method can efficiently identify and count plump grains and blighted grains to achieve an accuracy above 99% for the seed setting rate.https://doi.org/10.1002/pld3.3233‐D recognitionmask R‐CNNrice phenotypeseed setting rateX‐ray CT imaging |
| spellingShingle | Huihua Kong Ping Chen Mask R‐CNN‐based feature extraction and three‐dimensional recognition of rice panicle CT images Plant Direct 3‐D recognition mask R‐CNN rice phenotype seed setting rate X‐ray CT imaging |
| title | Mask R‐CNN‐based feature extraction and three‐dimensional recognition of rice panicle CT images |
| title_full | Mask R‐CNN‐based feature extraction and three‐dimensional recognition of rice panicle CT images |
| title_fullStr | Mask R‐CNN‐based feature extraction and three‐dimensional recognition of rice panicle CT images |
| title_full_unstemmed | Mask R‐CNN‐based feature extraction and three‐dimensional recognition of rice panicle CT images |
| title_short | Mask R‐CNN‐based feature extraction and three‐dimensional recognition of rice panicle CT images |
| title_sort | mask r cnn based feature extraction and three dimensional recognition of rice panicle ct images |
| topic | 3‐D recognition mask R‐CNN rice phenotype seed setting rate X‐ray CT imaging |
| url | https://doi.org/10.1002/pld3.323 |
| work_keys_str_mv | AT huihuakong maskrcnnbasedfeatureextractionandthreedimensionalrecognitionofricepaniclectimages AT pingchen maskrcnnbasedfeatureextractionandthreedimensionalrecognitionofricepaniclectimages |