Proximal sensing for geometric characterization of vines: A review of the latest advances

Moreno, Héctor; Andújar, Dionisio

doi:10.1016/j.compag.2023.107901

Cited by 21 publications

(6 citation statements)

References 249 publications

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“…As a result, the row segmentation method demonstrated outstanding accuracy in all datasets, achieving a flawless segmentation rate of 100%, in line with existing research ( Jurado et al., 2020 ; Moreno and Andújar, 2023 ). Notably, our analysis revealed that the training systems had no noticeable effect on the performance of the row segmentation method.…”

Section: Resultssupporting

confidence: 86%

“…Enhancing and optimizing the productivity and quality of grapevine crops is a primary goal for winegrowers, making vineyard management decisions significant ( Moreno and Andújar, 2023 ). A key factor in achieving this lies in obtaining precise and detailed information about the overall structure of vineyards, which encompasses plant arrangements and geometric canopy attributes.…”

Section: Introductionmentioning

confidence: 99%

“…Key geometric parameters of grapevine crops, such as canopy structure, height, width, volume, and leaf area, are closely connected to plant growth, health, and potential yield. These factors allow breeders to identify and efficiently manage distinct vineyard areas, optimizing their cultivation strategies ( Moreno and Andújar, 2023 ). Estimation of these parameters is traditionally performed by human operators collecting manual measurements about the canopy characteristics.…”

Section: Introductionmentioning

confidence: 99%

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UAV-based individual plant detection and geometric parameter extraction in vineyards

Cantürk,

Zabawa,

Pavlic

et al. 2023

Front. Plant Sci.

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Accurately characterizing vineyard parameters is crucial for precise vineyard management and breeding purposes. Various macroscopic vineyard parameters are required to make informed management decisions, such as pesticide application, defoliation strategies, and determining optimal sugar content in each berry by assessing biomass. In this paper, we present a novel approach that utilizes point cloud data to detect trunk positions and extract macroscopic vineyard characteristics, including plant height, canopy width, and canopy volume. Our approach relies solely on geometric features and is compatible with different training systems and data collected using various 3D sensors. To evaluate the effectiveness and robustness of our proposed approach, we conducted extensive experiments on multiple grapevine rows trained in two different systems. Our method provides more comprehensive canopy characteristics than traditional manual measurements, which are not representative throughout the row. The experimental results demonstrate the accuracy and efficiency of our method in extracting vital macroscopic vineyard characteristics, providing valuable insights for yield monitoring, grape quality optimization, and strategic interventions to enhance vineyard productivity and sustainability.

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Section: Resultssupporting

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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UAV-based individual plant detection and geometric parameter extraction in vineyards

Cantürk,

Zabawa,

Pavlic

et al. 2023

Front. Plant Sci.

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“…The monitoring of the vine's ecosystem structure is a labor-intensive task and is mostly performed manually. Manual measurements are often inconsistent, leading to errors in the metrics, and only small regions can be sampled [15]. The ecosystem structures of vines correlate with plant growth, health status, and potential yield [16].…”

Section: Introductionmentioning

confidence: 99%

“…Satellite-based imagery provides valuable insights at a landscape scale, but the spatial resolution is often too coarse for precision viticulture [17]. In the review paper of Moreno and Andújar [15], the researchers state that future research should investigate the optimal flight angles of UAS-mounted sensors in order to collect valuable data for the geometric characterization of vines. In the current study, 3D point cloud analysis combined with object-based image analysis is used to detect and segment single vines.…”

Section: Introductionmentioning

confidence: 99%

Automated Derivation of Vine Objects and Ecosystem Structures Using UAS-Based Data Acquisition, 3D Point Cloud Analysis, and OBIA

Ruess,

Paulus,

Lang

2024

Applied Sciences

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This study delves into the analysis of a vineyard in Carinthia, Austria, focusing on the automated derivation of ecosystem structures of individual vine parameters, including vine heights, leaf area index (LAI), leaf surface area (LSA), and the geographic positioning of single plants. For the derivation of these parameters, intricate segmentation processes and nuanced UAS-based data acquisition techniques are necessary. The detection of single vines was based on 3D point cloud data, generated at a phenological stage in which the plants were in the absence of foliage. The mean distance from derived vine locations to reference measurements taken with a GNSS device was 10.7 cm, with a root mean square error (RMSE) of 1.07. Vine height derivation from a normalized digital surface model (nDSM) using photogrammetric data showcased a strong correlation (R2 = 0.83) with real-world measurements. Vines underwent automated classification through an object-based image analysis (OBIA) framework. This process enabled the computation of ecosystem structures at the individual plant level post-segmentation. Consequently, it delivered comprehensive canopy characteristics rapidly, surpassing the speed of manual measurements. With the use of uncrewed aerial systems (UAS) equipped with optical sensors, dense 3D point clouds were computed for the derivation of canopy-related ecosystem structures of vines. While LAI and LSA computations await validation, they underscore the technical feasibility of obtaining precise geometric and morphological datasets from UAS-collected data paired with 3D point cloud analysis and object-based image analysis.

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Grape yield estimation with a smartphone’s colour and depth cameras using machine learning and computer vision techniques

Parr,

Legg,

Alam

2023

Computers and Electronics in Agriculture

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Proximal sensing for geometric characterization of vines: A review of the latest advances

Cited by 21 publications

References 249 publications

UAV-based individual plant detection and geometric parameter extraction in vineyards

UAV-based individual plant detection and geometric parameter extraction in vineyards

Automated Derivation of Vine Objects and Ecosystem Structures Using UAS-Based Data Acquisition, 3D Point Cloud Analysis, and OBIA

Grape yield estimation with a smartphone’s colour and depth cameras using machine learning and computer vision techniques

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