Using remote sensing to identify individual tree species in orchards: A review

Ozdarici-Ok, Asli; Ok, Ali Özgün

doi:10.1016/j.scienta.2023.112333

Cited by 7 publications

(3 citation statements)

References 74 publications

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“…Light detection and ranging (LiDAR) techniques have been demonstrated to be an efficient and accurate method for distinguishing between trees and vegetation in urban environments [ 6 , 7 ]. Spectral imaging and LiDAR data have been combined to identity woody crop species [ 8 ] in different kinds of orchards, including sweet chestnut. Pádua et al [ 9 ] used spectral images captured by unmanned aerial vehicles (UAVs) to assess chestnut health status, while Rivera et al [ 5 ] reviewed the use of LiDAR data for health monitoring in other woody crops.…”

Section: Introductionmentioning

confidence: 99%

A Handheld Laser-Scanning-Based Methodology for Monitoring Tree Growth in Chestnut Orchards

Pereira-Obaya,

Cabo,

Ordóñez

et al. 2024

Sensors

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Chestnut and chestnut byproducts are of worldwide interest, so there is a constant need to develop faster and more accurate monitoring techniques. Recent advances in simultaneous localization and mapping (SLAM) algorithms and user accessibility have led to increased use of handheld mobile laser scanning (HHLS) in precision agriculture. We propose a tree growth monitoring methodology, based on HHLS point cloud processing, that calculates the length of branches through spatial discretization of the point cloud for each tree. The methodology was tested by comparing two point clouds collected almost simultaneously for each of a set of sweet chestnut trees. The results obtained indicated that our HHLS method was reliable and accurate in efficiently monitoring sweet chestnut tree growth. The same methodology was used to calculate the growth of the same set of trees over 37 weeks (from spring to winter). Differences in week 0 and week 37 scans showed an approximate mean growth of 0.22 m, with a standard deviation of around 0.16 m reflecting heterogeneous tree growth.

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Section: Introductionmentioning

confidence: 99%

A Handheld Laser-Scanning-Based Methodology for Monitoring Tree Growth in Chestnut Orchards

Pereira-Obaya,

Cabo,

Ordóñez

et al. 2024

Sensors

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“…It helps growers take timely and necessary measures, such as timely fertilization, irrigation, and pest control, to maximize the optimization of the banana plant's growing environment and improve yield and quality [4]. Currently, point clouds are widely used to measure plant phenotypic parameters [5], and the way to obtain the plant shape point cloud is mainly obtained by 3D reconstruction of UAV (Unmanned Aerial Vehicle) images and LiDAR (Light Detection and Ranging) scanning [6]. Song et al [7] generated a point cloud model of winter wheat plants based on UAV images to measure the canopy height of winter wheat, and the predicted RMSE and MAE reached 6.37cm and 5.07cm, respectively.…”

Section: Introductionmentioning

confidence: 99%

Banana Individual Segmentation and Phenotypic Parameter Measurements Using Deep Learning and Terrestrial LiDAR

Ma,

Wang,

Zeng

et al. 2024

IEEE Access

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Banana phenotypic parameters are one of the important elements in the study of banana growth and development. Through the measurement of banana phenotypic parameters, we can obtain information about the growth status, nutritional status and quality indexes of banana plants, and pseudostem parameters are significant indicators in banana phenotypic parameters. This research proposes a twostage approach combining morphological features and deep learning point cloud segmentation to extract banana pseudo-stem parameters. Specifically, in the first step, seed points are extracted using the DBSCAN clustering algorithm, and banana individual plant segmentation is accomplished using the region growing algorithm based on seed points. Its precision, recall and F1-score were 97.73%, 97.36% and 97.54%, respectively. This indicates that the DBSCAN clustering algorithm and the seed point based region growing algorithm can effectively realize the plant count of banana plants and initially realize the individual plant segmentation of banana. The second step is to use PointNet++, PointNet, and DGCNN for pseudo-stem and canopy segmentation of individual banana plants. All three models perform well in segmentation, with PointNet++ performing the best. Its precision, recall, F1-score, Matthews correlation coefficient and Dice coefficient reached 0.9956, 0.9709, 0.9831, 0.9670 and 0.9831. this shows that deep learning has a better applicability in segmenting banana plants. In the results of segmentation, we measure the banana pseudostem circumference and pseudo-stem height. The correlation between the extracted pseudo-stem height and pseudo-stem circumference compared to the measured values was 96.70% and 82.32%, respectively. The above two-stage method of extracting banana pseudo-stem parameters overcomes the difficulties of point cloud individual plant segmentation associated with intensive banana cultivation. It makes the management of individual banana plants possible and provides accurate phenotypic parameter information for banana plantation management. It lays the foundation for further assessment of banana growth and nutritional status.

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“…images mainly relies on manual visual interpretation [4]. Recently, automatic segmentation technology reduces abundant labor costs and time consumption, and improves the generalization and efficiency [5], [6].…”

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confidence: 99%

CEDAnet: Individual Tree Segmentation in Dense Orchard via Context Enhancement and Density Prior

Zhu,

Chen,

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Individual Tree Segmentation (ITS) is a pivotal technique in orchard research, estimating tree counts and delineating crown contours. This method provides foundational data for assessing orchard health, nutritional composition, and predicting yield. Unmanned Aerial Vehicles (UAVs) have become an essential data source for Intelligent Transportation Systems (ITS) due to their capability to capture ultra-fine details. However, current DL-based ITS methods struggle to accurately handle densely overlapping fruit tree distributions with similar characteristics in UAVs images, primarily due to the intricate nature of spatial arrangements in such scenarios. In this paper, we propose CEDAnet, a context enhancement and density adjustment network, to address the challenge of dense fruit trees segmentation. Specifically, a Transformer-based Contextual Aggregation Module (TCAM) is designed to distinguish different instances and refine the boundary of the instances. We have proposed a Density-guided Non-maximum Suppression (DG-NMS) method to adaptively generate sufficient candidate bounding boxes, aiming to retain more potential instances in dense trees. To evaluate the effectiveness and robustness of our proposal, we curated two ITS datasets constructed with imagery captured by UAVs, namely iSCHID and iSMMID based on their respective spatial characteristics. Experimental results on both two datasets demonstrated that CEDAnet yields competitive results in ITS tasks, with the bounding box AP of 0.498, segmentation AP of 0.493 in iSCHID, and the bounding box AP of 0.706, segmentation AP of 0.703 in iSMMID. Both two datasets and the code of CEDAnet will be publicly available at https://github.com/Lihy256/CEDAnet.

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Using remote sensing to identify individual tree species in orchards: A review

Cited by 7 publications

References 74 publications

A Handheld Laser-Scanning-Based Methodology for Monitoring Tree Growth in Chestnut Orchards

A Handheld Laser-Scanning-Based Methodology for Monitoring Tree Growth in Chestnut Orchards

Banana Individual Segmentation and Phenotypic Parameter Measurements Using Deep Learning and Terrestrial LiDAR

CEDAnet: Individual Tree Segmentation in Dense Orchard via Context Enhancement and Density Prior

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