Data on three-year flowering intensity monitoring in an apple orchard: A collection of RGB images acquired from unmanned aerial vehicles

Chenglong Zhang; João Valente; Wensheng Wang; Pieter van Dalfsen; Peter Frans de Jong; Bert Rijk; Lammert Kooistra

doi:10.1016/j.dib.2023.109356

Data on three-year flowering intensity monitoring in an apple orchard: A collection of RGB images acquired from unmanned aerial vehicles

Data Brief. 2023 Jul 5:49:109356. doi: 10.1016/j.dib.2023.109356. eCollection 2023 Aug.

Authors

Chenglong Zhang^{1

2}, João Valente³, Wensheng Wang², Pieter van Dalfsen⁴, Peter Frans de Jong⁴, Bert Rijk⁵, Lammert Kooistra¹

Affiliations

¹ Laboratory of Geo-information Science and Remote Sensing, Wageningen University & Research, Droevendaalsesteeg 3, 6708 PB Wageningen, the Netherlands.
² Agricultural Information Institute, Chinese Academy of Agriculture Science, Beijing 100086, China.
³ Information Technology Group, Wageningen University & Research, Hollandseweg 1, 6706 KN Wageningen, the Netherlands.
⁴ Field Crops, Wageningen University & Research, Lingewal 1, 6668 LA Randwijk, the Netherlands.
⁵ Aurea Imaging BV, Nijverheidsweg 16B, 3534AM Utrecht, the Netherlands.

Abstract

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.

Keywords: Flower blossom; Flower cluster; Photogrammetry; UAV; Yield mapping.