ESG-YOLO: A Method for Detecting Male Tassels and Assessing Density of Maize in the Field
The intelligent acquisition of phenotypic information on male tassels is critical for maize growth and yield assessment. In order to realize accurate detection and density assessment of maize male tassels in complex field environments, this study used a UAV to collect images of maize male tassels un...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-01-01
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| Series: | Agronomy |
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| Online Access: | https://www.mdpi.com/2073-4395/14/2/241 |
| _version_ | 1797299149850279936 |
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| author | Wendi Wu Jianhua Zhang Guomin Zhou Yuhang Zhang Jian Wang Lin Hu |
| author_facet | Wendi Wu Jianhua Zhang Guomin Zhou Yuhang Zhang Jian Wang Lin Hu |
| author_sort | Wendi Wu |
| collection | DOAJ |
| description | The intelligent acquisition of phenotypic information on male tassels is critical for maize growth and yield assessment. In order to realize accurate detection and density assessment of maize male tassels in complex field environments, this study used a UAV to collect images of maize male tassels under different environmental factors in the experimental field and then constructed and formed the ESG-YOLO detection model based on the YOLOv7 model by using GELU as the activation function instead of the original SiLU and by adding a dual ECA attention mechanism and an SPD-Conv module. And then, through the model to identify and detect the male tassel, the model’s average accuracy reached a mean value (mAP) of 93.1%; compared with the YOLOv7 model, its average accuracy mean value (mAP) is 2.3 percentage points higher. Its low-resolution image and small object target detection is excellent, and it can be more intuitive and fast to obtain the maize male tassel density from automatic identification surveys. It provides an effective method for high-precision and high-efficiency identification of maize male tassel phenotypes in the field, and it has certain application value for maize growth potential, yield, and density assessment. |
| first_indexed | 2024-03-07T22:46:31Z |
| format | Article |
| id | doaj.art-52c77c3596bd4d0a9f42b19ca51ffd81 |
| institution | Directory Open Access Journal |
| issn | 2073-4395 |
| language | English |
| last_indexed | 2024-03-07T22:46:31Z |
| publishDate | 2024-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agronomy |
| spelling | doaj.art-52c77c3596bd4d0a9f42b19ca51ffd812024-02-23T15:03:57ZengMDPI AGAgronomy2073-43952024-01-0114224110.3390/agronomy14020241ESG-YOLO: A Method for Detecting Male Tassels and Assessing Density of Maize in the FieldWendi Wu0Jianhua Zhang1Guomin Zhou2Yuhang Zhang3Jian Wang4Lin Hu5College of Tropical Crops, Hainan University, Haikou 570228, ChinaNational Nanfan Restasselch Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572024, ChinaNational Nanfan Restasselch Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572024, ChinaCollege of Tropical Crops, Hainan University, Haikou 570228, ChinaNational Nanfan Restasselch Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572024, ChinaNational Nanfan Restasselch Institute (Sanya), Chinese Academy of Agricultural Sciences, Sanya 572024, ChinaThe intelligent acquisition of phenotypic information on male tassels is critical for maize growth and yield assessment. In order to realize accurate detection and density assessment of maize male tassels in complex field environments, this study used a UAV to collect images of maize male tassels under different environmental factors in the experimental field and then constructed and formed the ESG-YOLO detection model based on the YOLOv7 model by using GELU as the activation function instead of the original SiLU and by adding a dual ECA attention mechanism and an SPD-Conv module. And then, through the model to identify and detect the male tassel, the model’s average accuracy reached a mean value (mAP) of 93.1%; compared with the YOLOv7 model, its average accuracy mean value (mAP) is 2.3 percentage points higher. Its low-resolution image and small object target detection is excellent, and it can be more intuitive and fast to obtain the maize male tassel density from automatic identification surveys. It provides an effective method for high-precision and high-efficiency identification of maize male tassel phenotypes in the field, and it has certain application value for maize growth potential, yield, and density assessment.https://www.mdpi.com/2073-4395/14/2/241maizetasseltarget detectionattention mechanismSPD-ConvESG-YOLO |
| spellingShingle | Wendi Wu Jianhua Zhang Guomin Zhou Yuhang Zhang Jian Wang Lin Hu ESG-YOLO: A Method for Detecting Male Tassels and Assessing Density of Maize in the Field Agronomy maize tassel target detection attention mechanism SPD-Conv ESG-YOLO |
| title | ESG-YOLO: A Method for Detecting Male Tassels and Assessing Density of Maize in the Field |
| title_full | ESG-YOLO: A Method for Detecting Male Tassels and Assessing Density of Maize in the Field |
| title_fullStr | ESG-YOLO: A Method for Detecting Male Tassels and Assessing Density of Maize in the Field |
| title_full_unstemmed | ESG-YOLO: A Method for Detecting Male Tassels and Assessing Density of Maize in the Field |
| title_short | ESG-YOLO: A Method for Detecting Male Tassels and Assessing Density of Maize in the Field |
| title_sort | esg yolo a method for detecting male tassels and assessing density of maize in the field |
| topic | maize tassel target detection attention mechanism SPD-Conv ESG-YOLO |
| url | https://www.mdpi.com/2073-4395/14/2/241 |
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