Robust Grape Cluster Detection in a Vineyard by Combining the AdaBoost Framework and Multiple Color Components
The automatic fruit detection and precision picking in unstructured environments was always a difficult and frontline problem in the harvesting robots field. To realize the accurate identification of grape clusters in a vineyard, an approach for the automatic detection of ripe grape by combining the...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-12-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/16/12/2098 |
| _version_ | 1798004181117698048 |
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| author | Lufeng Luo Yunchao Tang Xiangjun Zou Chenglin Wang Po Zhang Wenxian Feng |
| author_facet | Lufeng Luo Yunchao Tang Xiangjun Zou Chenglin Wang Po Zhang Wenxian Feng |
| author_sort | Lufeng Luo |
| collection | DOAJ |
| description | The automatic fruit detection and precision picking in unstructured environments was always a difficult and frontline problem in the harvesting robots field. To realize the accurate identification of grape clusters in a vineyard, an approach for the automatic detection of ripe grape by combining the AdaBoost framework and multiple color components was developed by using a simple vision sensor. This approach mainly included three steps: (1) the dataset of classifier training samples was obtained by capturing the images from grape planting scenes using a color digital camera, extracting the effective color components for grape clusters, and then constructing the corresponding linear classification models using the threshold method; (2) based on these linear models and the dataset, a strong classifier was constructed by using the AdaBoost framework; and (3) all the pixels of the captured images were classified by the strong classifier, the noise was eliminated by the region threshold method and morphological filtering, and the grape clusters were finally marked using the enclosing rectangle method. Nine hundred testing samples were used to verify the constructed strong classifier, and the classification accuracy reached up to 96.56%, higher than other linear classification models. Moreover, 200 images captured under three different illuminations in the vineyard were selected as the testing images on which the proposed approach was applied, and the average detection rate was as high as 93.74%. The experimental results show that the approach can partly restrain the influence of the complex background such as the weather condition, leaves and changing illumination. |
| first_indexed | 2024-04-11T12:19:42Z |
| format | Article |
| id | doaj.art-bad40b762d61449b9abd7a6ff7530569 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:19:42Z |
| publishDate | 2016-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-bad40b762d61449b9abd7a6ff75305692022-12-22T04:24:08ZengMDPI AGSensors1424-82202016-12-011612209810.3390/s16122098s16122098Robust Grape Cluster Detection in a Vineyard by Combining the AdaBoost Framework and Multiple Color ComponentsLufeng Luo0Yunchao Tang1Xiangjun Zou2Chenglin Wang3Po Zhang4Wenxian Feng5Key Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou 510642, ChinaSchool of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, ChinaKey Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou 510642, ChinaKey Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou 510642, ChinaKey Laboratory of Key Technology on Agricultural Machine and Equipment, Ministry of Education, South China Agricultural University, Guangzhou 510642, ChinaSchool of Civil and Transportation Engineering, Guangdong University of Technology, Guangzhou 510006, ChinaThe automatic fruit detection and precision picking in unstructured environments was always a difficult and frontline problem in the harvesting robots field. To realize the accurate identification of grape clusters in a vineyard, an approach for the automatic detection of ripe grape by combining the AdaBoost framework and multiple color components was developed by using a simple vision sensor. This approach mainly included three steps: (1) the dataset of classifier training samples was obtained by capturing the images from grape planting scenes using a color digital camera, extracting the effective color components for grape clusters, and then constructing the corresponding linear classification models using the threshold method; (2) based on these linear models and the dataset, a strong classifier was constructed by using the AdaBoost framework; and (3) all the pixels of the captured images were classified by the strong classifier, the noise was eliminated by the region threshold method and morphological filtering, and the grape clusters were finally marked using the enclosing rectangle method. Nine hundred testing samples were used to verify the constructed strong classifier, and the classification accuracy reached up to 96.56%, higher than other linear classification models. Moreover, 200 images captured under three different illuminations in the vineyard were selected as the testing images on which the proposed approach was applied, and the average detection rate was as high as 93.74%. The experimental results show that the approach can partly restrain the influence of the complex background such as the weather condition, leaves and changing illumination.http://www.mdpi.com/1424-8220/16/12/2098grape detectionAdaBoost classifiercolor componentsharvesting robot |
| spellingShingle | Lufeng Luo Yunchao Tang Xiangjun Zou Chenglin Wang Po Zhang Wenxian Feng Robust Grape Cluster Detection in a Vineyard by Combining the AdaBoost Framework and Multiple Color Components Sensors grape detection AdaBoost classifier color components harvesting robot |
| title | Robust Grape Cluster Detection in a Vineyard by Combining the AdaBoost Framework and Multiple Color Components |
| title_full | Robust Grape Cluster Detection in a Vineyard by Combining the AdaBoost Framework and Multiple Color Components |
| title_fullStr | Robust Grape Cluster Detection in a Vineyard by Combining the AdaBoost Framework and Multiple Color Components |
| title_full_unstemmed | Robust Grape Cluster Detection in a Vineyard by Combining the AdaBoost Framework and Multiple Color Components |
| title_short | Robust Grape Cluster Detection in a Vineyard by Combining the AdaBoost Framework and Multiple Color Components |
| title_sort | robust grape cluster detection in a vineyard by combining the adaboost framework and multiple color components |
| topic | grape detection AdaBoost classifier color components harvesting robot |
| url | http://www.mdpi.com/1424-8220/16/12/2098 |
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