Vine Canopy Reconstruction and Assessment with Terrestrial Lidar and Aerial Imaging

Petrović, Igor; Sečnik, Matej; Hočevar, Marko; Berk, P.

doi:10.3390/rs14225894

Cited by 7 publications

(3 citation statements)

References 32 publications

(49 reference statements)

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“…With the increase in driving time, the error gradually accumulates, which will cause significant positioning errors [12]. Therefore, researchers have integrated positioning sensors such as machine vision, ultrasonic and sensors LiDAR into relative positioning methods [12,40,41]. It will provide fusion positioning for relative positioning methods, thereby improving positioning accuracy.…”

Section: Discussionmentioning

confidence: 99%

Precision Inter-Row Relative Positioning Method by Using 3D LiDAR in Planted Forests and Orchards

Liu,

Ji,

Zeng

et al. 2024

Agronomy

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Accurate positioning at the inter-row canopy can provide data support for precision variable-rate spraying. Therefore, there is an urgent need to design a reliable positioning method for the inter-row canopy of closed orchards (planted forests). In the study, the Extended Kalman Filter (EKF) fusion positioning method (method C) was first constructed by calibrating the IMU and encoder with errors. Meanwhile, 3D Light Detection and Ranging (LiDAR) observations were introduced to be fused into Method C. An EKF fusion positioning method (method D) based on 3D LiDAR corrected detection was designed. The method starts or closes method C by the presence or absence of the canopy. The vertically installed 3D LiDAR detected the canopy body center, providing the vehicle with inter-row vertical distance and heading. They were obtained through the distance between the center of the body and fixed row spacing. This can provide an accurate initial position for method C and correct the positioning trajectory. Finally, the positioning and canopy length measurement experiments were designed using a GPS positioning system. The results show that the method proposed in this study can significantly improve the accuracy of length measurement and positioning at the inter-row canopy, which does not significantly change with the distance traveled. In the orchard experiment, the average positioning deviations of the lateral and vertical distances at the inter-row canopy are 0.1 m and 0.2 m, respectively, with an average heading deviation of 6.75°, and the average relative error of canopy length measurement was 4.35%. The method can provide a simple and reliable inter-row positioning method for current remote-controlled and manned agricultural machinery when working in standardized 3D crops. This can modify the above-mentioned machinery to improve its automation level.

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Section: Discussionmentioning

confidence: 99%

Precision Inter-Row Relative Positioning Method by Using 3D LiDAR in Planted Forests and Orchards

Liu,

Ji,

Zeng

et al. 2024

Agronomy

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“…LiDAR and SfM point clouds have distinct characteristics that impact their suitability for vineyard plant phenotyping, and several studies compared the accuracy of the two point clouds. In one study (Madec et al, 2017), both UAV-based SfM and ground-based LiDAR showed comparable accuracy in wheat crop height determination, while another (Petrovićet al, 2022) showed SfM point cloud superior accuracy in representing grapevine canopies due to its higher data density capture based on ground sampling distance. The affordability of RGB cameras compared to LiDAR has sparked interest, leading to numerous studies utilizing SfM-derived point clouds to estimate vineyard parameters (De Castro et al, 2018;Matese and Di Gennaro, 2018;Jurado et al, 2020;Padua et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

IPPS 2022 - Plant Phenotyping for a Sustainable Future

Schurr,

Kaiser,

Clarke

et al. 2024

Frontiers Research Topics

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Frontiers is more than just an open access publisher of scholarly articles: it is a pioneering approach to the world of academia, radically improving the way scholarly research is managed. The grand vision of Frontiers is a world where all people have an equal opportunity to seek, share and generate knowledge. Frontiers provides immediate and permanent online open access to all its publications, but this alone is not enough to realize our grand goals. Frontiers journal seriesThe Frontiers journal series is a multi-tier and interdisciplinary set of openaccess, online journals, promising a paradigm shift from the current review, selection and dissemination processes in academic publishing. All Frontiers journals are driven by researchers for researchers; therefore, they constitute a service to the scholarly community. At the same time, the Frontiers journal series operates on a revolutionary invention, the tiered publishing system, initially addressing specific communities of scholars, and gradually climbing up to broader public understanding, thus serving the interests of the lay society, too. Dedication to qualityEach Frontiers article is a landmark of the highest quality, thanks to genuinely collaborative interactions between authors and review editors, who include some of the world's best academicians. Research must be certified by peers before entering a stream of knowledge that may eventually reach the public -and shape society; therefore, Frontiers only applies the most rigorous and unbiased reviews. Frontiers revolutionizes research publishing by freely delivering the most outstanding research, evaluated with no bias from both the academic and social point of view. By applying the most advanced information technologies, Frontiers is catapulting scholarly publishing into a new generation. What are Frontiers Research Topics?Frontiers Research Topics are very popular trademarks of the Frontiers journals series: they are collections of at least ten articles, all centered on a particular subject. With their unique mix of varied contributions from Original Research to Review Articles, Frontiers Research Topics unify the most influential researchers, the latest key findings and historical advances in a hot research area.

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“…LiDAR and SfM point clouds have distinct characteristics that impact their suitability for vineyard plant phenotyping, and several studies compared the accuracy of the two point clouds. In one study ( Madec et al., 2017 ), both UAV-based SfM and ground-based LiDAR showed comparable accuracy in wheat crop height determination, while another ( Petrović et al., 2022 ) showed SfM point cloud superior accuracy in representing grapevine canopies due to its higher data density capture based on ground sampling distance. The affordability of RGB cameras compared to LiDAR has sparked interest, leading to numerous studies utilizing SfM-derived point clouds to estimate vineyard parameters ( De Castro et al., 2018 ; Matese and Di Gennaro, 2018 ; Jurado et al., 2020 ; Pádua et al., 2020 ).…”

Section: Introductionmentioning

confidence: 99%

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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Vine Canopy Reconstruction and Assessment with Terrestrial Lidar and Aerial Imaging

Cited by 7 publications

References 32 publications

Precision Inter-Row Relative Positioning Method by Using 3D LiDAR in Planted Forests and Orchards

Precision Inter-Row Relative Positioning Method by Using 3D LiDAR in Planted Forests and Orchards

IPPS 2022 - Plant Phenotyping for a Sustainable Future

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

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