Optimising drone flight planning for measuring horticultural tree crop structure

Tu, Yu-Hsuan; Phinn, Stuart R.; Johansen, Kasper; Robson, Andrew; Wu, Dan

doi:10.1016/j.isprsjprs.2019.12.006

Cited by 90 publications

(67 citation statements)

References 47 publications

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“…Therefore, this upper end of the image overlap range is adopted in cases where a high level of accuracy is required [56,57,59], while the lowest overlaps, for rapid observation with low accuracy requirements, should not be less than 55% [60]. Some studies have also found that interactions between flight collection variables, including flight altitude, image overlap, flying direction, speed and solar elevation affect UAS data quality and the subsequent processing results, emphasizing the need for flight planning optimization [61].…”

Section: Camera Settings and Uas Control Softwarementioning

confidence: 99%

Current Practices in UAS-based Environmental Monitoring

et al. 2020

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With the increasing role that unmanned aerial systems (UAS) are playing in data collection for environmental studies, two key challenges relate to harmonizing and providing standardized guidance for data collection, and also establishing protocols that are applicable across a broad range of environments and conditions. In this context, a network of scientists are cooperating within the framework of the Harmonious Project to develop and promote harmonized mapping strategies and disseminate operational guidance to ensure best practice for data collection and interpretation. The culmination of these efforts is summarized in the present manuscript. Through this synthesis study, we identify the many interdependencies of each step in the collection and processing chain, and outline approaches to formalize and ensure a successful workflow and product development. Given the number of environmental conditions, constraints, and variables that could possibly be explored from UAS platforms, it is impractical to provide protocols that can be applied universally under all scenarios. However, it is possible to collate and systematically order the fragmented knowledge on UAS collection and analysis to identify the best practices that can best ensure the streamlined and rigorous development of scientific products.

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Section: Camera Settings and Uas Control Softwarementioning

confidence: 99%

Current Practices in UAS-based Environmental Monitoring

et al. 2020

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“…Many other applications (e.g., ecological modeling and forestry management) require accurate estimation of tree parameters [40]. Similarly, Tu et al [41] mention the need for accurate 3D models in horticulture.…”

Section: Discussionmentioning

confidence: 99%

Metrics of Growth Habit Derived from the 3D Tree Point Cloud Used for Species Determination—A New Approach in Botanical Taxonomy Tested on Dragon Tree Group Example

Vahalík

Drápela

Procházková

et al. 2020

Forests

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Detailed, three-dimensional modeling of trees is a new approach in botanical taxonomy. Representations of individual trees are a prerequisite for accurate assessments of tree growth and morphological metronomy. This study tests the abilities of 3D modeling of trees to determine the various metrics of growth habit and compare morphological differences. The study included four species of the genus Dracaena: D. draco, D. cinnabari, D. ombet, and D. serrulata. Forty-nine 3D tree point clouds were created, and their morphological metrics were derived and compared. Our results indicate the possible application of 3D tree point clouds to dendrological taxonomy. Basic metrics of growth habit and coefficients derived from the 3D point clouds developed in the present study enable the statistical evaluation of differences among dragon tree species.

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“…Decreasing the GSD would imply (a combination of) flying lower, slower, with an increased sensor size and/or an increased focal length, further restricting the drone platform and sensor type choices. Either way, working with smaller GSDs lengthens the processing time for a given field size because of the larger number of pixels [46]. Moreover, as the trees are not static due to wind (especially so for the fine branches at the top) and the drone flight itself can cause additional wind circulation (depending on the platform type and flight height), there will be a limit on the GSD and the dense point matching that can be achieved under given meteorological conditions.…”

Section: Limitations and Recommendations For Future Researchmentioning

confidence: 99%

Pear Flower Cluster Quantification Using RGB Drone Imagery

et al. 2020

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High quality fruit production requires the regulation of the crop load on fruit trees by reducing the number of flowers and fruitlets early in the growing season, if the bearing is too high. Several automated flower cluster quantification methods based on proximal and remote imagery methods have been proposed to estimate flower cluster numbers, but their overall performance is still far from satisfactory. For other methods, the performance of the method to estimate flower clusters within a tree is unknown since they were only tested on images from one perspective. One of the main reported bottlenecks is the presence of occluded flowers due to limitations of the top-view perspective of the platform-sensor combinations. In order to tackle this problem, the multi-view perspective from the Red–Green–Blue (RGB) colored dense point clouds retrieved from drone imagery are compared and evaluated against the field-based flower cluster number per tree. Experimental results obtained on a dataset of two pear tree orchards (N = 144) demonstrate that our 3D object-based method, a combination of pixel-based classification with the stochastic gradient boosting algorithm and density-based clustering (DBSCAN), significantly outperforms the state-of-the-art in flower cluster estimations from the 2D top-view (R2 = 0.53), with R2 > 0.7 and RRMSE < 15%.

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Optimising drone flight planning for measuring horticultural tree crop structure

Cited by 90 publications

References 47 publications

Current Practices in UAS-based Environmental Monitoring

Current Practices in UAS-based Environmental Monitoring

Metrics of Growth Habit Derived from the 3D Tree Point Cloud Used for Species Determination—A New Approach in Botanical Taxonomy Tested on Dragon Tree Group Example

Pear Flower Cluster Quantification Using RGB Drone Imagery

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