The use of computer vision to estimate tree diameter and circumference in homogeneous and production forests using a non-contact method

Figures & data

Figure 1. Agroforestry condition status; (a) the canopy condition of rubber trees; (b) silk trees plantation (Albizia sp.).

Figure 2. Visualization of the collected data; (a) calibration data; (b) validation data.

Figure 3. Illustration of the (a) captured image and (b) measurement method using camera.

Figure 4. Workflow of image processing. (a) Cropped image based on particular image coordinate (sloping tree/trunk); (b) cropped image based on a certain image coordinate (perpendicular tree/trunk); (c) binary-converted image based on the threshold (sloping tree/trunk); (d) binary-converted image based on the threshold (perpendicular tree/trunk).

Figure 5. Linear regression of predicted and actual values; (a) tree circumference (predicted); (b) standardized residual of circumference (predicted); (c) tree diameter (predicted); and (d) standardized residual of diameter (predicted).

Table 1. Parameter estimates and statistical fitting summary model of tree diameter and tree circumference.

Variable	Parameters	Estimates	Standard error	t-Value	p > |t|	R^2	RMSE
Tree diameter (predicted, cm)	α0	−0.812	0.958	−0.848	0.400	0.94	2.749
	α1	1.037	0.033	31.232	<0.0001
Tree circumference (predicted, cm)	α0	−1.201	2.741	−0.438	0.663	0.95	7.866
	α1	1.030	0.030	34.047	<0.0001

Figure 6. Validation model and standardized residual of estimating tree diameter and circumference using Computer Vision; Validation model and standardized residual of estimating tree diameter and circumference using Computer Vision; (a) tree circumference; (b) standardized residual of circumference; (c) tree diameter; and (d) standardized residual of diameter.