An Ensemble Learning Model for Detecting Soybean Seedling Emergence in UAV Imagery
Efficient detection and evaluation of soybean seedling emergence is an important measure for making field management decisions. However, there are many indicators related to emergence, and using multiple models to detect them separately makes data processing too slow to aid timely field management....
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/15/6662 |
| _version_ | 1797586043744026624 |
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| author | Bo Zhang Dehao Zhao |
| author_facet | Bo Zhang Dehao Zhao |
| author_sort | Bo Zhang |
| collection | DOAJ |
| description | Efficient detection and evaluation of soybean seedling emergence is an important measure for making field management decisions. However, there are many indicators related to emergence, and using multiple models to detect them separately makes data processing too slow to aid timely field management. In this study, we aimed to integrate several deep learning and image processing methods to build a model to evaluate multiple soybean seedling emergence information. An unmanned aerial vehicle (UAV) was used to acquire soybean seedling RGB images at emergence (VE), cotyledon (VC), and first node (V1) stages. The number of soybean seedlings that emerged was obtained by the seedling emergence detection module, and image datasets were constructed using the seedling automatic cutting module. The improved AlexNet was used as the backbone network of the growth stage discrimination module. The above modules were combined to calculate the emergence proportion in each stage and determine soybean seedlings emergence uniformity. The results show that the seedling emergence detection module was able to identify the number of soybean seedlings with an average accuracy of 99.92%, a R<sup>2</sup> of 0.9784, a RMSE of 6.07, and a MAE of 5.60. The improved AlexNet was more lightweight, training time was reduced, the average accuracy was 99.07%, and the average loss was 0.0355. The model was validated in the field, and the error between predicted and real emergence proportions was up to 0.0775 and down to 0.0060. It provides an effective ensemble learning model for the detection and evaluation of soybean seedling emergence, which can provide a theoretical basis for making decisions on soybean field management and precision operations and has the potential to evaluate other crops emergence information. |
| first_indexed | 2024-03-11T00:17:40Z |
| format | Article |
| id | doaj.art-a758c0596278458fa4e0b60c736513c0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T00:17:40Z |
| publishDate | 2023-07-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj.art-a758c0596278458fa4e0b60c736513c02023-11-18T23:32:47ZengMDPI AGSensors1424-82202023-07-012315666210.3390/s23156662An Ensemble Learning Model for Detecting Soybean Seedling Emergence in UAV ImageryBo Zhang0Dehao Zhao1College of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaCollege of Information and Electrical Engineering, Heilongjiang Bayi Agricultural University, Daqing 163319, ChinaEfficient detection and evaluation of soybean seedling emergence is an important measure for making field management decisions. However, there are many indicators related to emergence, and using multiple models to detect them separately makes data processing too slow to aid timely field management. In this study, we aimed to integrate several deep learning and image processing methods to build a model to evaluate multiple soybean seedling emergence information. An unmanned aerial vehicle (UAV) was used to acquire soybean seedling RGB images at emergence (VE), cotyledon (VC), and first node (V1) stages. The number of soybean seedlings that emerged was obtained by the seedling emergence detection module, and image datasets were constructed using the seedling automatic cutting module. The improved AlexNet was used as the backbone network of the growth stage discrimination module. The above modules were combined to calculate the emergence proportion in each stage and determine soybean seedlings emergence uniformity. The results show that the seedling emergence detection module was able to identify the number of soybean seedlings with an average accuracy of 99.92%, a R<sup>2</sup> of 0.9784, a RMSE of 6.07, and a MAE of 5.60. The improved AlexNet was more lightweight, training time was reduced, the average accuracy was 99.07%, and the average loss was 0.0355. The model was validated in the field, and the error between predicted and real emergence proportions was up to 0.0775 and down to 0.0060. It provides an effective ensemble learning model for the detection and evaluation of soybean seedling emergence, which can provide a theoretical basis for making decisions on soybean field management and precision operations and has the potential to evaluate other crops emergence information.https://www.mdpi.com/1424-8220/23/15/6662emergence evaluationunmanned aerial vehicleimageryensemble learning modelgrowth stagesemergence proportion |
| spellingShingle | Bo Zhang Dehao Zhao An Ensemble Learning Model for Detecting Soybean Seedling Emergence in UAV Imagery Sensors emergence evaluation unmanned aerial vehicle imagery ensemble learning model growth stages emergence proportion |
| title | An Ensemble Learning Model for Detecting Soybean Seedling Emergence in UAV Imagery |
| title_full | An Ensemble Learning Model for Detecting Soybean Seedling Emergence in UAV Imagery |
| title_fullStr | An Ensemble Learning Model for Detecting Soybean Seedling Emergence in UAV Imagery |
| title_full_unstemmed | An Ensemble Learning Model for Detecting Soybean Seedling Emergence in UAV Imagery |
| title_short | An Ensemble Learning Model for Detecting Soybean Seedling Emergence in UAV Imagery |
| title_sort | ensemble learning model for detecting soybean seedling emergence in uav imagery |
| topic | emergence evaluation unmanned aerial vehicle imagery ensemble learning model growth stages emergence proportion |
| url | https://www.mdpi.com/1424-8220/23/15/6662 |
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