Research on a Panoramic Image Stitching Method for Images of Corn Ears, Based on Video Streaming
<b>Background:</b> Corn is the main grain crop grown in China, and the ear shape index of corn is an important parameter for breeding new varieties, including ear length, diameter, row number of ears, row number of grains per ear, and so on. <b>Objective:</b> In order to solv...
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MDPI AG
2024-12-01
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| Series: | Agronomy |
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| author | Yi Huangfu Hongming Chen Zhonghao Huang Wenfeng Li Jie Shi Linlin Yang |
| author_facet | Yi Huangfu Hongming Chen Zhonghao Huang Wenfeng Li Jie Shi Linlin Yang |
| author_sort | Yi Huangfu |
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| description | <b>Background:</b> Corn is the main grain crop grown in China, and the ear shape index of corn is an important parameter for breeding new varieties, including ear length, diameter, row number of ears, row number of grains per ear, and so on. <b>Objective:</b> In order to solve the problem of limited field of view associated with computer detection of the corn ear shape index against a complex background, this paper proposes a panoramic splicing method for corn ears against a complex background, which can splice 10 corn ear panoramic images at the same time, to improve information collection efficiency, display comprehensive information, and support data analysis, so as to realize automatic corn seed examination. <b>Methods:</b> A summary of corn ear panoramic stitching methods under complex backgrounds is presented as follows: 1. a perceptual hash algorithm and histogram equalization were used to extract video frames; 2. the U-Net image segmentation model based on transfer learning was used to predict corn labels; 3. a mask preprocessing algorithm was designed; 4. a corn ear splicing positioning algorithm was designed; 5. an algorithm for irregular surface expansion was designed; 6. an image stitching method based on template matching was adopted to assemble the video frames. <b>Results:</b> The experimental results showed that the proposed corn ear panoramic stitching method could effectively solve the problems of virtual stitching, obvious stitching seams, and too-high similarity between multiple images. The success rate of stitching was as high as 100%, and the speed of single-corn-ear panoramic stitching was about 9.4 s, indicating important reference value for corn breeding and disease and insect detection. <b>Discussions:</b> Although the experimental results demonstrated the significant advantages of the panoramic splicing method for corn ear images proposed in this paper in terms of improving information collection efficiency and automating corn assessment, the method still faces certain challenges. Future research will focus on the following points: 1. addressing the issue of environmental interference caused by diseases, pests, and plant nutritional status on the measurement of corn ear parameters in order to enhance the stability and accuracy of the algorithm; 2. expanding the dataset for the U-Net model to include a wider range of corn ears with complex backgrounds, different growth stages, and various environmental conditions to improve the model’s segmentation recognition rate and precision. Recently, our panoramic splicing algorithm has been deployed in practical applications with satisfactory results. We plan to continue optimizing the algorithm and more broadly promote its use in fields such as corn breeding and pest and disease detection in an effort to advance the development of agricultural automation technology. |
| format | Article |
| id | doaj-art-a38219d150df4cde812a6967c1f73155 |
| institution | OA Journals |
| issn | 2073-4395 |
| language | English |
| publishDate | 2024-12-01 |
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| series | Agronomy |
| spelling | doaj-art-a38219d150df4cde812a6967c1f731552025-08-20T02:00:51ZengMDPI AGAgronomy2073-43952024-12-011412288410.3390/agronomy14122884Research on a Panoramic Image Stitching Method for Images of Corn Ears, Based on Video StreamingYi Huangfu0Hongming Chen1Zhonghao Huang2Wenfeng Li3Jie Shi4Linlin Yang5Mechanical and Electrical Engineering College, Yunnan Agricultural University, Kunming 650201, ChinaMechanical and Electrical Engineering College, Yunnan Agricultural University, Kunming 650201, ChinaMechanical and Electrical Engineering College, Yunnan Agricultural University, Kunming 650201, ChinaMechanical and Electrical Engineering College, Yunnan Agricultural University, Kunming 650201, ChinaMechanical and Electrical Engineering College, Yunnan Agricultural University, Kunming 650201, ChinaMechanical and Electrical Engineering College, Yunnan Agricultural University, Kunming 650201, China<b>Background:</b> Corn is the main grain crop grown in China, and the ear shape index of corn is an important parameter for breeding new varieties, including ear length, diameter, row number of ears, row number of grains per ear, and so on. <b>Objective:</b> In order to solve the problem of limited field of view associated with computer detection of the corn ear shape index against a complex background, this paper proposes a panoramic splicing method for corn ears against a complex background, which can splice 10 corn ear panoramic images at the same time, to improve information collection efficiency, display comprehensive information, and support data analysis, so as to realize automatic corn seed examination. <b>Methods:</b> A summary of corn ear panoramic stitching methods under complex backgrounds is presented as follows: 1. a perceptual hash algorithm and histogram equalization were used to extract video frames; 2. the U-Net image segmentation model based on transfer learning was used to predict corn labels; 3. a mask preprocessing algorithm was designed; 4. a corn ear splicing positioning algorithm was designed; 5. an algorithm for irregular surface expansion was designed; 6. an image stitching method based on template matching was adopted to assemble the video frames. <b>Results:</b> The experimental results showed that the proposed corn ear panoramic stitching method could effectively solve the problems of virtual stitching, obvious stitching seams, and too-high similarity between multiple images. The success rate of stitching was as high as 100%, and the speed of single-corn-ear panoramic stitching was about 9.4 s, indicating important reference value for corn breeding and disease and insect detection. <b>Discussions:</b> Although the experimental results demonstrated the significant advantages of the panoramic splicing method for corn ear images proposed in this paper in terms of improving information collection efficiency and automating corn assessment, the method still faces certain challenges. Future research will focus on the following points: 1. addressing the issue of environmental interference caused by diseases, pests, and plant nutritional status on the measurement of corn ear parameters in order to enhance the stability and accuracy of the algorithm; 2. expanding the dataset for the U-Net model to include a wider range of corn ears with complex backgrounds, different growth stages, and various environmental conditions to improve the model’s segmentation recognition rate and precision. Recently, our panoramic splicing algorithm has been deployed in practical applications with satisfactory results. We plan to continue optimizing the algorithm and more broadly promote its use in fields such as corn breeding and pest and disease detection in an effort to advance the development of agricultural automation technology.https://www.mdpi.com/2073-4395/14/12/2884corn earphenotypic detectionimage stitchingvideo streamingtransfer learningU-Net image segmentation |
| spellingShingle | Yi Huangfu Hongming Chen Zhonghao Huang Wenfeng Li Jie Shi Linlin Yang Research on a Panoramic Image Stitching Method for Images of Corn Ears, Based on Video Streaming Agronomy corn ear phenotypic detection image stitching video streaming transfer learning U-Net image segmentation |
| title | Research on a Panoramic Image Stitching Method for Images of Corn Ears, Based on Video Streaming |
| title_full | Research on a Panoramic Image Stitching Method for Images of Corn Ears, Based on Video Streaming |
| title_fullStr | Research on a Panoramic Image Stitching Method for Images of Corn Ears, Based on Video Streaming |
| title_full_unstemmed | Research on a Panoramic Image Stitching Method for Images of Corn Ears, Based on Video Streaming |
| title_short | Research on a Panoramic Image Stitching Method for Images of Corn Ears, Based on Video Streaming |
| title_sort | research on a panoramic image stitching method for images of corn ears based on video streaming |
| topic | corn ear phenotypic detection image stitching video streaming transfer learning U-Net image segmentation |
| url | https://www.mdpi.com/2073-4395/14/12/2884 |
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