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

다국어 초록 (Multilingual Abstract)

Tree diameter and circumference measurements are important metrics that should be monitored periodically and which can be used to determine rates of plant growth, timber production (wood), rubber tapping time, and to estimate the nutrient content of the soil in agroforestry especially for rubber and Albizia sp. trees. In this study, we evaluated the use of optical sensors, including a smartphone camera, which were analyzed by an image processing technology to estimate tree circumference of homogeneous and production forests especially rubber and Albizia forest plantations, through a real-time tree diameter measurement approach. Camera measurements were carried out for the diameter at breast height (DBH) and a certain distance of each tree (with the diameter of tree range of 6–50 cm). The results show that the use of smartphone camera measurements is highly correlated with manual measurements obtained using a tree caliper or meter tape in estimating tree circumference with coefficient of determination (R2) and RMSE of 0.95 and 7.9 cm, respectively. Thus, this tool can be employed as an alternative method for measuring tree diameter and circumference. Tree diameter and circumference measurements are important metrics that should be monitored periodically and which can be used to determine rates of plant growth, timber production (wood), rubber tapping time, and to estimate the nutrient content of the soil in agroforestry especially for rubber and Albizia sp. trees. In this study, we evaluated the use of optical sensors, including a smartphone camera, which were analyzed by an image processing technology to estimate tree circumference of homogeneous and production forests especially rubber and Albizia forest plantations, through a real-time tree diameter measurement approach. Camera measurements were carried out for the diameter at breast height (DBH) and a certain distance of each tree (with the diameter of tree range of 6–50 cm). The results show that the use of smartphone camera measurements is highly correlated with manual measurements obtained using a tree caliper or meter tape in estimating tree circumference with coefficient of determination (R2) and RMSE of 0.95 and 7.9 cm, respectively. Thus, this tool can be employed as an alternative method for measuring tree diameter and circumference.

참고문헌 (Reference)

1 Ali W, "Visual tree detection for €autonomous navigation in forest environment" 2008

2 Marques P, "UAV-based automatic detection and monitoring of chestnut trees" 11 (11): 855-, 2019

3 Fabijanska A, "Towards automatic tree rings detection in images of scanned wood samples" 140 : 279-289, 2017

4 Ozcelik R, "Predicting tree height from tree diameter and dominant height using mixedeffects and quantile regression models for two species in Turkey" 419-420 : 240-248, 2018

5 이요한, "Measuring social capital in Indonesian community forest management" 한국산림과학회 13 (13): 133-141, 2017

6 Qureshi WS, "Machine vision for counting fruit on mango tree canopies" 18 (18): 224-244, 2017

7 Onuwaje OU, "Growth response of rubber seedlings to N, P, and K fertilizer in Nigeria" 3 (3): 169-175, 1982

8 Riyanto HD, "Growth model of sengon plantation stand for forest management" 3 (3): 113-120, 2010

9 Chandrasekhar TR, "Girth growth of rubber(Hevea brasiliensis)trees during the immature phase" 17 : 399-415, 2005

10 V.P. Tewari, "Forest inventory, assessment, and monitoring, and long-term forest observational studies, with special reference to India" 한국산림과학회 12 (12): 24-32, 2016

11 Ponder F, "Fertilizer combinations benefit diameter growth of plantation black walnut" 21 (21): 1329-1337, 1998

12 Payne AB, "Estimation of mango crop yield using image analysis – segmentation method" 91 : 57-64, 2013

13 Iizuka K, "Estimating tree height and diameter at breast height(DBH)from digital surface models and orthophotos obtained with an unmanned aerial system for a Japanese Cypress(Chamaecyparis obtusa)forest" 10 (10): 13-, 2017

14 Rodrıguez F, "Diameter versus girth:which variable provides the best estimate of the cross-sectional area?" 24 (24): 33-, 2015

15 Fabijanska A, "DeepDendro – a tree rings detector based on a deep convolutional neural network" 150 : 353-363, 2018

16 Tian H, "Computer vision technology in agricultural automation—a review" 7 (7): 1-19, 2020

17 Wauters JB, "Carbon stock in rubber tree plantations in Western Ghana and Mato Grosso(Brazil)" 255 (255): 2347-2361, 2008

18 Kan J, "Automatic measurement of trunk and branch diameter of standing trees based on computer vision" 995-998, 2008

19 Yu K, "An image analysis pipeline for automated classification of imaging light conditions and for quantification of wheat canopy cover time series in field phenotyping" 13 (13): 15-, 2017

20 Matsushita M, "A novel growth model evaluating age–size effect on long-term trends in tree growth" 29 (29): 1250-1259, 2015

21 Putra BTW, "A new low-cost sensing system for rapid ring estimation of woody plants to support tree management" 7 (7): 369-374, 2019

22 Miller J, "3D modelling of individual trees using a handheld camera : accuracy of height, diameter and volume estimates" 14 (14): 932-940, 2015