A procedure for automated tree pruning suggestion using LiDAR scans of fruit trees

Westling, Fredrik; Underwood, James; Bryson, Mitch

doi:10.1016/j.compag.2021.106274

Cited by 18 publications

(8 citation statements)

References 24 publications

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“…The progress of scanning technology and computer vision allows producing increasingly faithful digital reconstructions of real trees [38][39][40]. A wider availability of relatively low-cost devices equipped with LiDAR scanners should enable further advance of the field, as already demonstrated by some recent research [11,41]. In such scenarios, the multi-objective assessment of pruning effects may need to rely on estimation of tree parameters that are not fully observable, or cannot be captured to a sufficient level of accuracy.…”

Section: Discussionmentioning

confidence: 99%

“…Another functionality enabled by software is algorithmic determination and evaluation of pruning performed on virtual trees with respect to specific goals. Existing research in this direction includes goal-oriented assessment of pruning effects [8], selection of pruning points in 3D tree reconstructions from images or point clouds as part of automated pruning [9][10][11], and algorithmic pruning optimization for support in computer-aided education [12]. In a previous study, the immediate and delayed effects of selective pruning on fruit tree development were assessed using the estimated amount of light received by the flower buds in the current and the next season [13].…”

Section: Introductionmentioning

confidence: 99%

“…The limitation of existing approaches to tree pruning optimization is that they are focused on a single pruning objective, which is the improvement of lighting conditions in the crown. Only Westling et al [11] used the crown volume as an additional component in the calculation of the pruned tree's final score. The problem of light intake maximization was approached from the perspective of both single-objective [12] and multi-objective optimization [13].…”

Section: Introductionmentioning

confidence: 99%

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Constrained Multi-Objective Optimization of Simulated Tree Pruning with Heterogeneous Criteria

Strnad

Kohek

2021

Applied Sciences

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Virtual pruning of simulated fruit tree models is a useful functionality provided by software tools for computer-aided horticultural education and research. It also enables algorithmic pruning optimization with respect to a set of quantitative objectives, which is important for analytical purposes and potential applications in automated pruning. However, the existing studies in pruning optimization focus on a single type of objective, such as light distribution within the crown. In this paper, we propose the use of heterogeneous objectives for discrete multi-objective optimization of simulated tree pruning. In particular, the average light intake, crown shape, and tree balance are used to observe the emergence of different pruning patterns in the non-dominated solution sets. We also propose the use of independent constraint objectives as a new mechanism to confine overfitting of solutions to individual pruning criteria. Finally, we perform the comparison of NSGA-II, SPEA2, and MOEA/D-EAM on this task. The results demonstrate that SPEA2 and MOEA/D-EAM, which use external solution archives, can produce better sets of non-dominated solutions than NSGA-II.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Constrained Multi-Objective Optimization of Simulated Tree Pruning with Heterogeneous Criteria

Strnad

Kohek

2021

Applied Sciences

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“…pts is the minimum fruit points in training samples. 3 CE stands for Cross Entropy. 4 Training batch size set as 3.…”

Section: Experiments On Training Strategymentioning

confidence: 99%

Semantic Segmentation of Fruits on Multi-sensor Fused Data in Natural Orchards

Kang¹,

Wang²

2022

Preprint

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Semantic segmentation is a fundamental task for agricultural robots to understand the surrounding environments in natural orchards. The recent development of the LiDAR techniques enables the robot to acquire accurate range measurements of the view in the unstructured orchards. Compared to RGB images, 3D point clouds have geometrical properties. By combining the LiDAR and camera, rich information on geometries and textures can be obtained. In this work, we propose a deep-learning-based segmentation method to perform accurate semantic segmentation on fused data from a LiDAR-Camera visual sensor. Two critical problems are explored and solved in this work. The first one is how to efficiently fused the texture and geometrical features from multi-sensor data. The second one is how to efficiently train the 3D segmentation network under severely imbalance class conditions. Moreover, an implementation of 3D segmentation in orchards including LiDAR-Camera data fusion, data collection and labelling, network training, and model inference is introduced in detail. In the experiment, we comprehensively analyze the network setup when dealing with highly unstructured and noisy point clouds acquired from an apple orchard. Overall, our proposed method achieves 86.2% mIoU on the segmentation of fruits on the high-resolution point cloud (100k-200k points). The experiment results show that the proposed method can perform accurate segmentation in real orchard environments.

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“…LiDAR point clouds have also been applied to the precise mapping of tree branch structures and their automatic classification to support precision pruning (Zhang et al, 2020). In this line, a procedure for automated tree pruning using LiDAR scans was also proposed by Westling et al (2021). However, LiDAR point clouds still constitute voluminous data sets, which are difficult to convert into useful information to support management decisions (Gregorio & Llorens, 2021).…”

Section: Introductionmentioning

confidence: 99%

Satellite multispectral indices to estimate canopy parameters and within-field management zones in super-intensive almond orchards

et al. 2022

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Continuous canopy status monitoring is an essential factor to support and precisely apply orchard management actions such as pruning, pesticide and foliar treatment applications, or fertirrigation, among others. For that, this work proposes the use of multispectral vegetation indices to estimate geometric and structural orchard parameters from remote sensing images (high temporal and spatial resolution) as an alternative to more time-consuming processing techniques, such as LiDAR surveys or UAV photogrammetry. A super-intensive almond (Prunus dulcis) orchard was scanned using a mobile terrestrial laser (LiDAR) in two different vegetative stages (after spring pruning and before harvesting). From the LiDAR point cloud, canopy orchard parameters, including maximum height and width, cross-sectional area and porosity, were summarized every 0.5 m along the rows and interpolated using block kriging to the pixel centroids of PlanetScope (3 × 3 m) and Sentinel-2 (10 × 10 m) image grids. To study the association between the LiDAR-derived parameters and 4 different vegetation indices. A canonical correlation analysis was carried out, showing the normalized difference vegetation index (NDVI) and the green normalized difference vegetation index (GNDVI) to have the best correlations. A cluster analysis was also performed. Results can be considered optimistic both for PlanetScope and Sentinel-2 images to delimit within-field management zones, being supported by significant differences in LiDAR-derived canopy parameters.

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A procedure for automated tree pruning suggestion using LiDAR scans of fruit trees

Cited by 18 publications

References 24 publications

Constrained Multi-Objective Optimization of Simulated Tree Pruning with Heterogeneous Criteria

Constrained Multi-Objective Optimization of Simulated Tree Pruning with Heterogeneous Criteria

Semantic Segmentation of Fruits on Multi-sensor Fused Data in Natural Orchards

Satellite multispectral indices to estimate canopy parameters and within-field management zones in super-intensive almond orchards

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