Strawberry Maturity Classification from UAV and Near-Ground Imaging Using Deep Learning
Strawberry is ranked third in the value of production of the crops in Florida, USA. Classifying strawberry maturity and monitoring strawberry growth status in the field is very critical for accurate strawberry yield prediction, efficient strawberry field management, and achieving the highest crop pr...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-12-01
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| Series: | Smart Agricultural Technology |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2772375521000010 |
| _version_ | 1819199390486626304 |
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| author | Xue Zhou Won Suk Lee Yiannis Ampatzidis Yang Chen Natalia Peres Clyde Fraisse |
| author_facet | Xue Zhou Won Suk Lee Yiannis Ampatzidis Yang Chen Natalia Peres Clyde Fraisse |
| author_sort | Xue Zhou |
| collection | DOAJ |
| description | Strawberry is ranked third in the value of production of the crops in Florida, USA. Classifying strawberry maturity and monitoring strawberry growth status in the field is very critical for accurate strawberry yield prediction, efficient strawberry field management, and achieving the highest crop production. The traditional method of distinguishing strawberry maturity is based on either physical appearance or internal chemical substance content. However, the traditional method is time-consuming and costly. In this research, an automatic strawberry maturity classification system was developed for the rapid and accurate classification of different strawberry maturity stages. A state-of-the-art deep learning method, You Only Look Once (YOLOv3), which is good at small object detection, was trained and applied to detect strawberry flowers and strawberry fruit at different maturity stages. Two strawberry image acquisition methods, aerial imaging and near-ground imaging, were compared by using the same deep learning image processing method. As a result, three and seven strawberry maturity stages were classified for unmanned aerial vehicle (UAV) images and near-ground digital camera images, respectively. For UAV images, the highest mean average precision (mAP) of strawberry maturity classification was 0.88 for a test data set at 2 m, and the highest classification average precision (AP) was 0.93 for fully matured fruit. For near-ground digital camera images, the mAP of strawberry maturity classification was 0.89, and the highest classification AP was 0.94 for fully matured fruit as well. The result shows that YOLOv3 is an excellent approach for strawberry maturity classification on both image types. |
| first_indexed | 2024-12-23T03:15:35Z |
| format | Article |
| id | doaj.art-deb9ac5aa37342fab3bd90b92e2278ac |
| institution | Directory Open Access Journal |
| issn | 2772-3755 |
| language | English |
| last_indexed | 2024-12-23T03:15:35Z |
| publishDate | 2021-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Smart Agricultural Technology |
| spelling | doaj.art-deb9ac5aa37342fab3bd90b92e2278ac2022-12-21T18:02:07ZengElsevierSmart Agricultural Technology2772-37552021-12-011100001Strawberry Maturity Classification from UAV and Near-Ground Imaging Using Deep LearningXue Zhou0Won Suk Lee1Yiannis Ampatzidis2Yang Chen3Natalia Peres4Clyde Fraisse5Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USADepartment of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USA; Corresponding authorsDepartment of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USA; Southwest Florida Research and Education Center, University of Florida, Immokalee, FL 34142, USA; Corresponding authorsDepartment of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USA; College of Biosystem Engineering and Food Science, Zhejiang University, Hangzhou 310058, ChinaGulf Coast Research and Education Center, University of Florida, Wimauma, FL 33598, USADepartment of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USAStrawberry is ranked third in the value of production of the crops in Florida, USA. Classifying strawberry maturity and monitoring strawberry growth status in the field is very critical for accurate strawberry yield prediction, efficient strawberry field management, and achieving the highest crop production. The traditional method of distinguishing strawberry maturity is based on either physical appearance or internal chemical substance content. However, the traditional method is time-consuming and costly. In this research, an automatic strawberry maturity classification system was developed for the rapid and accurate classification of different strawberry maturity stages. A state-of-the-art deep learning method, You Only Look Once (YOLOv3), which is good at small object detection, was trained and applied to detect strawberry flowers and strawberry fruit at different maturity stages. Two strawberry image acquisition methods, aerial imaging and near-ground imaging, were compared by using the same deep learning image processing method. As a result, three and seven strawberry maturity stages were classified for unmanned aerial vehicle (UAV) images and near-ground digital camera images, respectively. For UAV images, the highest mean average precision (mAP) of strawberry maturity classification was 0.88 for a test data set at 2 m, and the highest classification average precision (AP) was 0.93 for fully matured fruit. For near-ground digital camera images, the mAP of strawberry maturity classification was 0.89, and the highest classification AP was 0.94 for fully matured fruit as well. The result shows that YOLOv3 is an excellent approach for strawberry maturity classification on both image types.http://www.sciencedirect.com/science/article/pii/S2772375521000010Strawberry maturity classificationUAV imagingNear-ground imagingDeep learning |
| spellingShingle | Xue Zhou Won Suk Lee Yiannis Ampatzidis Yang Chen Natalia Peres Clyde Fraisse Strawberry Maturity Classification from UAV and Near-Ground Imaging Using Deep Learning Smart Agricultural Technology Strawberry maturity classification UAV imaging Near-ground imaging Deep learning |
| title | Strawberry Maturity Classification from UAV and Near-Ground Imaging Using Deep Learning |
| title_full | Strawberry Maturity Classification from UAV and Near-Ground Imaging Using Deep Learning |
| title_fullStr | Strawberry Maturity Classification from UAV and Near-Ground Imaging Using Deep Learning |
| title_full_unstemmed | Strawberry Maturity Classification from UAV and Near-Ground Imaging Using Deep Learning |
| title_short | Strawberry Maturity Classification from UAV and Near-Ground Imaging Using Deep Learning |
| title_sort | strawberry maturity classification from uav and near ground imaging using deep learning |
| topic | Strawberry maturity classification UAV imaging Near-ground imaging Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S2772375521000010 |
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