Apple orchard inventory with a LiDAR equipped unmanned aerial system

Hadas, Edyta; Jóźków, Grzegorz; Walicka, Agata; Borkowski, Andrzej

doi:10.1016/j.jag.2019.101911

Cited by 15 publications

(19 citation statements)

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“…Orchard inventory using a terrestrial scanning system was also developed for an apple orchard (Gené-Mola et al, 2020). 99% of apple trees were identified and some geometric parameters were determined with a sub-decimeter accuracy using high-density point cloud obtained with UAS (Hadas et al, 2019).…”

Section: * Corresponding Authormentioning

confidence: 99%

Identification of Peach Tree Trunks From Laser Scanning Data Obtained With Small Unmanned Aerial System

Hadas

Kölle

Karpina

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Agricultural robotics rely on digital tools and sensor integration in order to improve efficiency and sustainability of cultivations. One part of orchard inventory is the identification of a tree trunk i.e. localization and diameter determination. However, this is a challenging task, due to thin trunks, presence of leaves and low branches. In this paper we present a case study for determining these parameters using the example of peach orchard, for which a high-density LiDAR data (over 3000 points/m2) was obtained with a small unmanned aerial system (UAS) during a leafy and leafless season. We applied point thresholding by height and by components of normal vector, in order to identify points reflected from trunks. Alpha-shape algorithm was used to aggregate together points, that belong to the same trunk and their centroid determined the trunk location. Trunk diameters were calculated using two alternative approaches: the Principal Component Analysis (PCA) and circle fit. For the leafy season trunk identification is challenging. Omission errors were caused due to few reflections from trunks and commission errors occurred because of the unfiltered reflections from low branches and young twigs oriented towards the ground. All 194 trunks were identified from data collected during the leafless season. The accuracy of tree location was 0.27 m and the accuracy of diameter determination using PCA was 0.03 m.

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Section: * Corresponding Authormentioning

confidence: 99%

Identification of Peach Tree Trunks From Laser Scanning Data Obtained With Small Unmanned Aerial System

Hadas

Kölle

Karpina

et al. 2020

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…LiDAR data tend to underestimate crown area when compared against field measurements (RMSE = 1 m 2 , R 2 = 0.17, maximum underestimation = 3.2 m 2 (47% of the field measured crown area)). However, tree height (R 2 = 0.96, RMSE = 0.09 m, maximum absolute height difference = 0.24 m) and canopy base height measured as the vertical distance between the ground and the lowest point of tree branches (absolute height difference < 0.2 m) achieved better accuracies (Hadas et al, 2019). As the cost of UAV based LiDAR systems drops in the future, it is likely that UAV based LiDAR applications will become operational for commonplace tree crown structure assessment.…”

Section: Discussionmentioning

confidence: 92%

“…(2019) used a UAV based LiDAR system to successfully identify 99% of 655 apple trees, and then selected 50 trees for mapping canopy structure, including crown area, tree height and canopy base height, with high accuracies. Hadas et al (2019) found that UAV based…”

Section: Discussionmentioning

confidence: 99%

“…LiDAR data have the ability to capture and measure the earth in five-dimensions (physical x, y, z, time (t) and laser return intensity) (Eitel et al, 2016). Based on the different platforms and scales of application, LiDAR system can be categorised as spaceborne, airborne, terrestrial, UAV and other mobile laser scanning (van Leeuwen and Nieuwenhuis, 2010, Qi and Dubayah, 2016, Hadas et al, 2019, Westling et al, 2018.…”

Section: Remote Sensing Measurements Of Tree Crop Structurementioning

confidence: 99%

“…Although this thesis only focuses on canopy height mapping from UAVs, other canopy structure information such as canopy extent, plant projective cover and tree condition can be extracted coincidently (Johansen et al, 2018, Tu et al, 2019a. In addition, other cameras such as thermal and LiDAR sensors can be installed to the UAV platforms, which can be used for mapping canopy health condition, irrigation requirements and canopy structures (Espinoza et al, 2017, Hadas et al, 2019.…”

Section: Contribution To Knowledge and Recommendations For Mapping Homentioning

confidence: 99%

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Measuring canopy structure for horticulture tree crops using remote sensing datasets

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Quantifying forest growth using terrestrial laser scanning in a free-air CO2 enrichment experiment. 3D Tree Models for Forest Dynamics, 2020 Helsinki, Finland. Contributions by others to the thesis This thesis includes three manuscripts which have either been published, submitted or intended for submission for publication. My supervisors Professor Stuart Phinn, Dr Kasper Johansen and Professor Andrew Robson contributed to the conceptualisation and editing of manuscripts. Statement of parts of the thesis submitted to qualify for the award of another degree No works submitted towards another degree have been included in this thesis. Research Involving Human or Animal Subjects No animal or human subjects were involved in this research. viii Acknowledgements "A PhD is hard. But a good supervisor makes it much easier." A huge thank you to my

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Orchard management with small unmanned aerial vehicles: a survey of sensing and analysis approaches

et al. 2021

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Advances in sensor miniaturization are increasing the global popularity of unmanned aerial vehicle (UAV)-based remote sensing applications in many domains of agriculture. Fruit orchards (the source of the fruit industry chain) require site-specific or even individual-tree-specific management throughout the growing season—from flowering, fruitlet development, ripening, and harvest—to tree dormancy. The recent increase in research on deploying UAV in orchard management has yielded new insights but challenges relating to determining the optimal approach (e.g., image-processing methods) are hampering widespread adoption, largely because there is no standard workflow for the application of UAVs in orchard management. This paper provides a comprehensive literature review focused on UAV-based orchard management: the survey includes achievements to date and shortcomings to be addressed. Sensing system architecture focusing on UAVs and sensors is summarized. Then up-to-date applications supported by UAVs in orchard management are described, focusing on the diversity of data-processing techniques, including monitoring efficiency and accuracy. With the goal of identifying the gaps and examining the opportunities for UAV-based orchard management, this study also discusses the performance of emerging technologies and compare similar research providing technical and comprehensive support for the further exploitation of UAVs and a revolution in orchard management.

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Apple orchard inventory with a LiDAR equipped unmanned aerial system

Cited by 15 publications

References 46 publications

Identification of Peach Tree Trunks From Laser Scanning Data Obtained With Small Unmanned Aerial System

Identification of Peach Tree Trunks From Laser Scanning Data Obtained With Small Unmanned Aerial System

Measuring canopy structure for horticulture tree crops using remote sensing datasets

Orchard management with small unmanned aerial vehicles: a survey of sensing and analysis approaches

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