Automatic kernel counting on maize ear using RGB images
Abstract Background The number of kernels per ear is one of the major agronomic yield indicators for maize. Manual assessment of kernel traits can be time consuming and laborious. Moreover, manually acquired data can be influenced by subjective bias of the observer. Existing methods for counting of...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2020-06-01
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| Series: | Plant Methods |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13007-020-00619-z |
| _version_ | 1811328825532874752 |
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| author | Di Wu Zhen Cai Jiwan Han Huawei Qin |
| author_facet | Di Wu Zhen Cai Jiwan Han Huawei Qin |
| author_sort | Di Wu |
| collection | DOAJ |
| description | Abstract Background The number of kernels per ear is one of the major agronomic yield indicators for maize. Manual assessment of kernel traits can be time consuming and laborious. Moreover, manually acquired data can be influenced by subjective bias of the observer. Existing methods for counting of kernel number are often unstable and costly. Machine vision technology allows objective extraction of features from image sensor data, offering high-throughput and low-cost advantages. Results Here, we propose an automatic kernel recognition method which has been applied to count the kernel number based on digital colour photos of the maize ears. Images were acquired under both LED diffuse (indoors) and natural light (outdoor) conditions. Field trials were carried out at two sites in China using 8 maize varieties. This method comprises five steps: (1) a Gaussian Pyramid for image compression to improve the processing efficiency, (2) separating the maize fruit from the background by Mean Shift Filtering algorithm, (3) a Colour Deconvolution (CD) algorithm to enhance the kernel edges, (4) segmentation of kernel zones using a local adaptive threshold, (5) an improved Find-Local-Maxima to recognize the local grayscale peaks and determine the maize kernel number within the image. The results showed good agreement (> 93%) in terms of accuracy and precision between ground truth (manual counting) and the image-based counting. Conclusions The proposed algorithm has robust and superior performance in maize ear kernel counting under various illumination conditions. In addition, the approach is highly-efficient and low-cost. The performance of this method makes it applicable and satisfactory for real-world breeding programs. |
| first_indexed | 2024-04-13T15:31:50Z |
| format | Article |
| id | doaj.art-273c688e03724bdd9f7ca244f85d6004 |
| institution | Directory Open Access Journal |
| issn | 1746-4811 |
| language | English |
| last_indexed | 2024-04-13T15:31:50Z |
| publishDate | 2020-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Plant Methods |
| spelling | doaj.art-273c688e03724bdd9f7ca244f85d60042022-12-22T02:41:21ZengBMCPlant Methods1746-48112020-06-0116111510.1186/s13007-020-00619-zAutomatic kernel counting on maize ear using RGB imagesDi Wu0Zhen Cai1Jiwan Han2Huawei Qin3Institute of Mechanical Engineering, Hangzhou Dianzi UniversityOcean College, Zhejiang UniversitySchool of Software, Shanxi Agricultural UniversityInstitute of Mechanical Engineering, Hangzhou Dianzi UniversityAbstract Background The number of kernels per ear is one of the major agronomic yield indicators for maize. Manual assessment of kernel traits can be time consuming and laborious. Moreover, manually acquired data can be influenced by subjective bias of the observer. Existing methods for counting of kernel number are often unstable and costly. Machine vision technology allows objective extraction of features from image sensor data, offering high-throughput and low-cost advantages. Results Here, we propose an automatic kernel recognition method which has been applied to count the kernel number based on digital colour photos of the maize ears. Images were acquired under both LED diffuse (indoors) and natural light (outdoor) conditions. Field trials were carried out at two sites in China using 8 maize varieties. This method comprises five steps: (1) a Gaussian Pyramid for image compression to improve the processing efficiency, (2) separating the maize fruit from the background by Mean Shift Filtering algorithm, (3) a Colour Deconvolution (CD) algorithm to enhance the kernel edges, (4) segmentation of kernel zones using a local adaptive threshold, (5) an improved Find-Local-Maxima to recognize the local grayscale peaks and determine the maize kernel number within the image. The results showed good agreement (> 93%) in terms of accuracy and precision between ground truth (manual counting) and the image-based counting. Conclusions The proposed algorithm has robust and superior performance in maize ear kernel counting under various illumination conditions. In addition, the approach is highly-efficient and low-cost. The performance of this method makes it applicable and satisfactory for real-world breeding programs.http://link.springer.com/article/10.1186/s13007-020-00619-zKernel recognitionCountingComputer visionAdaptive thresholdLocal Maxima |
| spellingShingle | Di Wu Zhen Cai Jiwan Han Huawei Qin Automatic kernel counting on maize ear using RGB images Plant Methods Kernel recognition Counting Computer vision Adaptive threshold Local Maxima |
| title | Automatic kernel counting on maize ear using RGB images |
| title_full | Automatic kernel counting on maize ear using RGB images |
| title_fullStr | Automatic kernel counting on maize ear using RGB images |
| title_full_unstemmed | Automatic kernel counting on maize ear using RGB images |
| title_short | Automatic kernel counting on maize ear using RGB images |
| title_sort | automatic kernel counting on maize ear using rgb images |
| topic | Kernel recognition Counting Computer vision Adaptive threshold Local Maxima |
| url | http://link.springer.com/article/10.1186/s13007-020-00619-z |
| work_keys_str_mv | AT diwu automatickernelcountingonmaizeearusingrgbimages AT zhencai automatickernelcountingonmaizeearusingrgbimages AT jiwanhan automatickernelcountingonmaizeearusingrgbimages AT huaweiqin automatickernelcountingonmaizeearusingrgbimages |