Estimation of Tree Volume Using Mask R-CNN based Deep Learning

Juyal, Piyush; Sharma, Sachin

doi:10.1109/icccnt49239.2020.9225509

Cited by 18 publications

(2 citation statements)

References 3 publications

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“…Juyal et al [24] developed a method for calculating treetop volume using Mask-RCNN. The diameter detection model was trained using the Mask-RCNN to segment the images and identify the X coordinates of tree boundaries and references.…”

Section: Artificial Intelligence and Tree Biometric Calculationsmentioning

confidence: 99%

Pinus pinaster Diameter, Height, and Volume Estimation Using Mask-RCNN

Malta,

Lopes,

Salas-González

et al. 2023

Sustainability

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Pinus pinaster, commonly called the maritime pine, is a vital species in Mediterranean forests. Its ability to thrive in the local climate and rapid growth make it an essential resource for wood production and reforestation efforts. Accurately estimating the volume of wood within a pine forest is of great significance to the wood industry. The traditional process is either a rough estimation without measurements or a time-consuming process based on manual measurements and calculations. This article presents a method for determining a tree’s diameter, total height, and volume based on a photograph. The method involves placing reference targets of known dimensions on the trees. A deep learning neural network is used to extract the tree trunk and the targets from the background, and the dimensions of the trunk are estimated based on the dimensions of the targets. The results indicate less than 10% estimation errors for diameter, height, and volume in general. The proposed methodology automates the estimation of the dendrometric characteristics of trees, reducing field time consumed in a forest inventory and without the need to use nonprofessional instruments.

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Section: Artificial Intelligence and Tree Biometric Calculationsmentioning

confidence: 99%

Pinus pinaster Diameter, Height, and Volume Estimation Using Mask-RCNN

Malta,

Lopes,

Salas-González

et al. 2023

Sustainability

View full text Add to dashboard Cite

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“…Some scholars have applied deep learning to forestry image processing. Juyal et al [ 17 ] used Mask R-CNN to compute tree volume efficiently, but a white rectangular board is required as a reference when photographing trees. Itakura et al [ 18 ] proposed a method combining stereo cameras and YOLO v2 to estimate tree diameter with an RMSE of about 3.14 cm, but the method is unstable for trunk diameters larger than 80 cm.…”

Section: Introductionmentioning

confidence: 99%

Automatic Tree Height Measurement Based on Three-Dimensional Reconstruction Using Smartphone

Shen,

Huang,

Hua

et al. 2023

Sensors

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Tree height is a crucial structural parameter in forest inventory as it provides a basis for evaluating stock volume and growth status. In recent years, close-range photogrammetry based on smartphone has attracted attention from researchers due to its low cost and non-destructive characteristics. However, such methods have specific requirements for camera angle and distance during shooting, and pre-shooting operations such as camera calibration and placement of calibration boards are necessary, which could be inconvenient to operate in complex natural environments. We propose a tree height measurement method based on three-dimensional (3D) reconstruction. Firstly, an absolute depth map was obtained by combining ARCore and MidasNet. Secondly, Attention-UNet was improved by adding depth maps as network input to obtain tree mask. Thirdly, the color image and depth map were fused to obtain the 3D point cloud of the scene. Then, the tree point cloud was extracted using the tree mask. Finally, the tree height was measured by extracting the axis-aligned bounding box of the tree point cloud. We built the method into an Android app, demonstrating its efficiency and automation. Our approach achieves an average relative error of 3.20% within a shooting distance range of 2–17 m, meeting the accuracy requirements of forest survey.

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