Data on three-year flowering intensity monitoring in an apple orchard: A collection of RGB images acquired from unmanned aerial vehicles
There is a growing body of literature that recognises the importance of UAVs in precision agriculture tasks. Currently, flowering thinning tasks in orchard management rely on the decisions derived from time-consuming manual flower cluster counting in the field by an agrotechnician. Yet it is hard to...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-08-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340923004754 |
| _version_ | 1797744059556560896 |
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| author | Chenglong Zhang João Valente Wensheng Wang Pieter van Dalfsen Peter Frans de Jong Bert Rijk Lammert Kooistra |
| author_facet | Chenglong Zhang João Valente Wensheng Wang Pieter van Dalfsen Peter Frans de Jong Bert Rijk Lammert Kooistra |
| author_sort | Chenglong Zhang |
| collection | DOAJ |
| description | There is a growing body of literature that recognises the importance of UAVs in precision agriculture tasks. Currently, flowering thinning tasks in orchard management rely on the decisions derived from time-consuming manual flower cluster counting in the field by an agrotechnician. Yet it is hard to guarantee the counting accuracy due to numerous human factors. The present dataset contains UAV images during the full blooming period of an apple orchard for three consecutive years, 2018, 2019, and 2020. It is directly linked to a research article entitled “Feasibility assessment of tree-level flower intensity quantification from UAV RGB imagery: A triennial study in an apple orchard”. The data collection site was an apple orchard located at Randwijk, Overbetuwe, The Netherlands (51.938, 5.7068 in WGS84 UTM 31U). Moreover, the flower cluster number and floridity ground truth are also provided in one row from the orchard. The UAV flights were conducted with different flying altitudes, camera resolutions, and lighting conditions. This dataset aims to support researchers focussing on remote sensing, machine vision, deep learning, and image classification, and the stakeholders interested in precision horticulture and orchard management. It can be used for flowering intensity estimation and prediction, and spatial and temporal flowering variability mapping by using digital photogrammetry and 3D reconstruction. |
| first_indexed | 2024-03-12T15:04:18Z |
| format | Article |
| id | doaj.art-55b0c313888841e8a1b0c651cf9b6310 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-03-12T15:04:18Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-55b0c313888841e8a1b0c651cf9b63102023-08-13T04:54:05ZengElsevierData in Brief2352-34092023-08-0149109356Data on three-year flowering intensity monitoring in an apple orchard: A collection of RGB images acquired from unmanned aerial vehiclesChenglong Zhang0João Valente1Wensheng Wang2Pieter van Dalfsen3Peter Frans de Jong4Bert Rijk5Lammert Kooistra6Laboratory of Geo-information Science and Remote Sensing, Wageningen University & Research, Droevendaalsesteeg 3, 6708 PB Wageningen, the Netherlands; Agricultural Information Institute, Chinese Academy of Agriculture Science, Beijing 100086, China; Corresponding author.Information Technology Group, Wageningen University & Research, Hollandseweg 1, 6706 KN Wageningen, the NetherlandsAgricultural Information Institute, Chinese Academy of Agriculture Science, Beijing 100086, ChinaField Crops, Wageningen University & Research, Lingewal 1, 6668 LA Randwijk, the NetherlandsField Crops, Wageningen University & Research, Lingewal 1, 6668 LA Randwijk, the NetherlandsAurea Imaging BV, Nijverheidsweg 16B, 3534AM Utrecht, the NetherlandsLaboratory of Geo-information Science and Remote Sensing, Wageningen University & Research, Droevendaalsesteeg 3, 6708 PB Wageningen, the NetherlandsThere is a growing body of literature that recognises the importance of UAVs in precision agriculture tasks. Currently, flowering thinning tasks in orchard management rely on the decisions derived from time-consuming manual flower cluster counting in the field by an agrotechnician. Yet it is hard to guarantee the counting accuracy due to numerous human factors. The present dataset contains UAV images during the full blooming period of an apple orchard for three consecutive years, 2018, 2019, and 2020. It is directly linked to a research article entitled “Feasibility assessment of tree-level flower intensity quantification from UAV RGB imagery: A triennial study in an apple orchard”. The data collection site was an apple orchard located at Randwijk, Overbetuwe, The Netherlands (51.938, 5.7068 in WGS84 UTM 31U). Moreover, the flower cluster number and floridity ground truth are also provided in one row from the orchard. The UAV flights were conducted with different flying altitudes, camera resolutions, and lighting conditions. This dataset aims to support researchers focussing on remote sensing, machine vision, deep learning, and image classification, and the stakeholders interested in precision horticulture and orchard management. It can be used for flowering intensity estimation and prediction, and spatial and temporal flowering variability mapping by using digital photogrammetry and 3D reconstruction.http://www.sciencedirect.com/science/article/pii/S2352340923004754UAVFlower blossomFlower clusterYield mappingPhotogrammetry |
| spellingShingle | Chenglong Zhang João Valente Wensheng Wang Pieter van Dalfsen Peter Frans de Jong Bert Rijk Lammert Kooistra Data on three-year flowering intensity monitoring in an apple orchard: A collection of RGB images acquired from unmanned aerial vehicles Data in Brief UAV Flower blossom Flower cluster Yield mapping Photogrammetry |
| title | Data on three-year flowering intensity monitoring in an apple orchard: A collection of RGB images acquired from unmanned aerial vehicles |
| title_full | Data on three-year flowering intensity monitoring in an apple orchard: A collection of RGB images acquired from unmanned aerial vehicles |
| title_fullStr | Data on three-year flowering intensity monitoring in an apple orchard: A collection of RGB images acquired from unmanned aerial vehicles |
| title_full_unstemmed | Data on three-year flowering intensity monitoring in an apple orchard: A collection of RGB images acquired from unmanned aerial vehicles |
| title_short | Data on three-year flowering intensity monitoring in an apple orchard: A collection of RGB images acquired from unmanned aerial vehicles |
| title_sort | data on three year flowering intensity monitoring in an apple orchard a collection of rgb images acquired from unmanned aerial vehicles |
| topic | UAV Flower blossom Flower cluster Yield mapping Photogrammetry |
| url | http://www.sciencedirect.com/science/article/pii/S2352340923004754 |
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