Maize (<i>Zea mays</i> L.) Stem Target Region Extraction and Stem Diameter Measurement Based on an Internal Gradient Algorithm in Field Conditions
The target region and diameter of maize stems are important phenotyping parameters for evaluating crop vitality and estimating crop biomass. To address the issue that the target region and diameter of maize stems obtained after transplantation may not accurately reflect the true growth conditions of...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-04-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/13/5/1185 |
| _version_ | 1827742429785096192 |
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| author | Jing Zhou Mingren Cui Yushan Wu Yudi Gao Yijia Tang Zhiyi Chen Lixin Hou Haijuan Tian |
| author_facet | Jing Zhou Mingren Cui Yushan Wu Yudi Gao Yijia Tang Zhiyi Chen Lixin Hou Haijuan Tian |
| author_sort | Jing Zhou |
| collection | DOAJ |
| description | The target region and diameter of maize stems are important phenotyping parameters for evaluating crop vitality and estimating crop biomass. To address the issue that the target region and diameter of maize stems obtained after transplantation may not accurately reflect the true growth conditions of maize, a phenotyping monitoring technology based on an internal gradient algorithm is proposed for acquiring the target region and diameter of maize stems. Observations were conducted during the small bell stage of maize. First, color images of maize plants were captured by an Intel RealSense D435i camera. The color information in the color image was extracted by the hue saturation value (HSV) color space model. The maximum between-class variance (Otsu) algorithm was applied for image threshold segmentation to obtain the main stem of maize. Median filtering, image binarization, and morphological opening operations were then utilized to remove noise from the images. Subsequently, the morphological gradient algorithm was applied to acquire the target region of maize stems. The similarity between the three types of gradient images and the manually segmented image was evaluated by pixel ratio extraction and image quality assessment indicators. Evaluation results indicated that the internal gradient algorithm could more accurately obtain the target region of maize stems. Finally, a checkerboard was employed as a reference for measurement assistance, and the stem diameter of maize was calculated by the pinhole imaging principle. The mean absolute error of stem diameter was 1.92 mm, the mean absolute percentage error (MAPE) was 5.16%, and the root mean square error (RMSE) was 2.25 mm. The R² value was 0.79. With an R² greater than 0.7 and a MAPE within 6%, the phenotyping monitoring technology based on the internal gradient algorithm was proven to accurately measure the diameter of maize stems. The application of phenotyping monitoring technology based on the internal gradient algorithm in field conditions provides technological support for smart agriculture. |
| first_indexed | 2024-03-11T04:01:47Z |
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| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-11T04:01:47Z |
| publishDate | 2023-04-01 |
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| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-a041eb09752f4196a0cd1f0fcd85807a2023-11-18T00:04:18ZengMDPI AGAgronomy2073-43952023-04-01135118510.3390/agronomy13051185Maize (<i>Zea mays</i> L.) Stem Target Region Extraction and Stem Diameter Measurement Based on an Internal Gradient Algorithm in Field ConditionsJing Zhou0Mingren Cui1Yushan Wu2Yudi Gao3Yijia Tang4Zhiyi Chen5Lixin Hou6Haijuan Tian7College of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaCollege of Information Technology, Jilin Agricultural University, Changchun 130118, ChinaA Jilin Province Key Laboratory of Grain and Oil Processing, Jilin Business and Technology College, Changchun 130507, ChinaThe target region and diameter of maize stems are important phenotyping parameters for evaluating crop vitality and estimating crop biomass. To address the issue that the target region and diameter of maize stems obtained after transplantation may not accurately reflect the true growth conditions of maize, a phenotyping monitoring technology based on an internal gradient algorithm is proposed for acquiring the target region and diameter of maize stems. Observations were conducted during the small bell stage of maize. First, color images of maize plants were captured by an Intel RealSense D435i camera. The color information in the color image was extracted by the hue saturation value (HSV) color space model. The maximum between-class variance (Otsu) algorithm was applied for image threshold segmentation to obtain the main stem of maize. Median filtering, image binarization, and morphological opening operations were then utilized to remove noise from the images. Subsequently, the morphological gradient algorithm was applied to acquire the target region of maize stems. The similarity between the three types of gradient images and the manually segmented image was evaluated by pixel ratio extraction and image quality assessment indicators. Evaluation results indicated that the internal gradient algorithm could more accurately obtain the target region of maize stems. Finally, a checkerboard was employed as a reference for measurement assistance, and the stem diameter of maize was calculated by the pinhole imaging principle. The mean absolute error of stem diameter was 1.92 mm, the mean absolute percentage error (MAPE) was 5.16%, and the root mean square error (RMSE) was 2.25 mm. The R² value was 0.79. With an R² greater than 0.7 and a MAPE within 6%, the phenotyping monitoring technology based on the internal gradient algorithm was proven to accurately measure the diameter of maize stems. The application of phenotyping monitoring technology based on the internal gradient algorithm in field conditions provides technological support for smart agriculture.https://www.mdpi.com/2073-4395/13/5/1185crop phenotypemaizestem diametermorphological gradienttarget region |
| spellingShingle | Jing Zhou Mingren Cui Yushan Wu Yudi Gao Yijia Tang Zhiyi Chen Lixin Hou Haijuan Tian Maize (<i>Zea mays</i> L.) Stem Target Region Extraction and Stem Diameter Measurement Based on an Internal Gradient Algorithm in Field Conditions Agronomy crop phenotype maize stem diameter morphological gradient target region |
| title | Maize (<i>Zea mays</i> L.) Stem Target Region Extraction and Stem Diameter Measurement Based on an Internal Gradient Algorithm in Field Conditions |
| title_full | Maize (<i>Zea mays</i> L.) Stem Target Region Extraction and Stem Diameter Measurement Based on an Internal Gradient Algorithm in Field Conditions |
| title_fullStr | Maize (<i>Zea mays</i> L.) Stem Target Region Extraction and Stem Diameter Measurement Based on an Internal Gradient Algorithm in Field Conditions |
| title_full_unstemmed | Maize (<i>Zea mays</i> L.) Stem Target Region Extraction and Stem Diameter Measurement Based on an Internal Gradient Algorithm in Field Conditions |
| title_short | Maize (<i>Zea mays</i> L.) Stem Target Region Extraction and Stem Diameter Measurement Based on an Internal Gradient Algorithm in Field Conditions |
| title_sort | maize i zea mays i l stem target region extraction and stem diameter measurement based on an internal gradient algorithm in field conditions |
| topic | crop phenotype maize stem diameter morphological gradient target region |
| url | https://www.mdpi.com/2073-4395/13/5/1185 |
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