Towards Automated Large-Scale 3D Phenotyping of Vineyards under Field Conditions
In viticulture, phenotypic data are traditionally collected directly in the field via visual and manual means by an experienced person. This approach is time consuming, subjective and prone to human errors. In recent years, research therefore has focused strongly on developing automated and non-inva...
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| Format: | Article |
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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/1424-8220/16/12/2136 |
| _version_ | 1798042102435676160 |
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| author | Johann Christian Rose Anna Kicherer Markus Wieland Lasse Klingbeil Reinhard Töpfer Heiner Kuhlmann |
| author_facet | Johann Christian Rose Anna Kicherer Markus Wieland Lasse Klingbeil Reinhard Töpfer Heiner Kuhlmann |
| author_sort | Johann Christian Rose |
| collection | DOAJ |
| description | In viticulture, phenotypic data are traditionally collected directly in the field via visual and manual means by an experienced person. This approach is time consuming, subjective and prone to human errors. In recent years, research therefore has focused strongly on developing automated and non-invasive sensor-based methods to increase data acquisition speed, enhance measurement accuracy and objectivity and to reduce labor costs. While many 2D methods based on image processing have been proposed for field phenotyping, only a few 3D solutions are found in the literature. A track-driven vehicle consisting of a camera system, a real-time-kinematic GPS system for positioning, as well as hardware for vehicle control, image storage and acquisition is used to visually capture a whole vine row canopy with georeferenced RGB images. In the first post-processing step, these images were used within a multi-view-stereo software to reconstruct a textured 3D point cloud of the whole grapevine row. A classification algorithm is then used in the second step to automatically classify the raw point cloud data into the semantic plant components, grape bunches and canopy. In the third step, phenotypic data for the semantic objects is gathered using the classification results obtaining the quantity of grape bunches, berries and the berry diameter. |
| first_indexed | 2024-04-11T22:30:50Z |
| format | Article |
| id | doaj.art-13432902d78a4579960d2dc13e9d0f14 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T22:30:50Z |
| publishDate | 2016-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-13432902d78a4579960d2dc13e9d0f142022-12-22T03:59:24ZengMDPI AGSensors1424-82202016-12-011612213610.3390/s16122136s16122136Towards Automated Large-Scale 3D Phenotyping of Vineyards under Field ConditionsJohann Christian Rose0Anna Kicherer1Markus Wieland2Lasse Klingbeil3Reinhard Töpfer4Heiner Kuhlmann5Institute of Geodesy and Geoinformation, Department of Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, GermanyJulius Kühn-Institut, Federal Research Centre of Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, 76833 Siebeldingen, GermanyInstitute of Geodesy and Geoinformation, Department of Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, GermanyInstitute of Geodesy and Geoinformation, Department of Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, GermanyJulius Kühn-Institut, Federal Research Centre of Cultivated Plants, Institute for Grapevine Breeding Geilweilerhof, 76833 Siebeldingen, GermanyInstitute of Geodesy and Geoinformation, Department of Geodesy, University of Bonn, Nussallee 17, 53115 Bonn, GermanyIn viticulture, phenotypic data are traditionally collected directly in the field via visual and manual means by an experienced person. This approach is time consuming, subjective and prone to human errors. In recent years, research therefore has focused strongly on developing automated and non-invasive sensor-based methods to increase data acquisition speed, enhance measurement accuracy and objectivity and to reduce labor costs. While many 2D methods based on image processing have been proposed for field phenotyping, only a few 3D solutions are found in the literature. A track-driven vehicle consisting of a camera system, a real-time-kinematic GPS system for positioning, as well as hardware for vehicle control, image storage and acquisition is used to visually capture a whole vine row canopy with georeferenced RGB images. In the first post-processing step, these images were used within a multi-view-stereo software to reconstruct a textured 3D point cloud of the whole grapevine row. A classification algorithm is then used in the second step to automatically classify the raw point cloud data into the semantic plant components, grape bunches and canopy. In the third step, phenotypic data for the semantic objects is gathered using the classification results obtaining the quantity of grape bunches, berries and the berry diameter.http://www.mdpi.com/1424-8220/16/12/2136viticulturefield phenotyping3D point cloudmulti-view-stereoclassificationberry diameternumber of berriesnumber of grape bunches |
| spellingShingle | Johann Christian Rose Anna Kicherer Markus Wieland Lasse Klingbeil Reinhard Töpfer Heiner Kuhlmann Towards Automated Large-Scale 3D Phenotyping of Vineyards under Field Conditions Sensors viticulture field phenotyping 3D point cloud multi-view-stereo classification berry diameter number of berries number of grape bunches |
| title | Towards Automated Large-Scale 3D Phenotyping of Vineyards under Field Conditions |
| title_full | Towards Automated Large-Scale 3D Phenotyping of Vineyards under Field Conditions |
| title_fullStr | Towards Automated Large-Scale 3D Phenotyping of Vineyards under Field Conditions |
| title_full_unstemmed | Towards Automated Large-Scale 3D Phenotyping of Vineyards under Field Conditions |
| title_short | Towards Automated Large-Scale 3D Phenotyping of Vineyards under Field Conditions |
| title_sort | towards automated large scale 3d phenotyping of vineyards under field conditions |
| topic | viticulture field phenotyping 3D point cloud multi-view-stereo classification berry diameter number of berries number of grape bunches |
| url | http://www.mdpi.com/1424-8220/16/12/2136 |
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