1.Wood Anatomy and Utilization Department

Research Institute of Wood Industry

Chinese Academy of Forestry

He Tuo1,2, Prabu Ravindran3,4, Lu Yang1,2, Alex C. Wiedenhoeft3,4, Jiao Lichao1,2, Yin Yafang1,2*

3. Center for Wood anatomy Research

Forest Products Laboratory

United States Department of Agriculture

2. Wood Collections

(WOODPEDIA)

Chinese Academy of Forestry

IAWA-IUFRO Symposium Beijing 2019

Deep-wood: Automated wood species identification using convolutional neural networks

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Outline

01 02 03 04 05 06

International efforts to combat the illegal logging

Chinese Regulations

on Wild Plants

Protection Protect, develop and

utilize wild plant species

that are listed in the

international treaty and

national regulations

USA Lacey Act

Amendment Prohibits all the trade of

the plants and plant

products that are illegally

sourced from US state and

foreign counties

European Union

Timber Regulation

Prohibits the illegally

harvested timber and

timber products on the

European market

Australia

Illegal Logging

Prohibition Act Prohibits wood, pulp and

paper products into Aus.

or process Aus. raw logs

illegally logged

7 new timber proposals:

Cedrela,

Pterocarpus tinctorius,

Dalbergia,

Guibourtia,

Pericopsis elata

CITES (Convention on International Trade in Endangered Species of Wild Fauna and Flora, since 1975)

CITES Cop

Total Number of Timber Species in CITES Appendix

CITES Appendix for Timber Species

Ⅰ Ⅱ Ⅲ

2010 Cop15

111 7 94 10

2013, CoP16

247 7

231 New added：Dalbergia cochinchinensis, D. granadillo, Osyris lanceolata and 48 Dalbergia spp. and 84 Diospyros spp.( populations of Madagascar) From III to II：Dalbergia retusa, D. stevensonii

9

2016, Cop17

~500 7

~486 New added: Dalbergia spp., Guibourtia tessmannii, Guibourtia demeusei, Guibourtia pellegriniana From III to II：Pterocarpus erinaceus

8

Standard lists

Reference samples

Wood anatomists

Technical tools

Professional wood

anatomists working with

highly trained ground staff

Access to xylarium

collections and associated

tools for wood anatomical

analysis

Genus level

Image

Computer Vision

Feature

Classifier

Macroscopic

Microscopic

GLCM

Wavelet Transform

Local banalization

BP-neural network

Support Vector Machine

K-nearest neighbor

…

…

Input Features

Input Output

Artificially feature engineering

Automated feature representation

Output

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Wood specimens from 4 xylaria ---FPL, CAF, INF and IPT

15 Dalbergia species—218 specimens

Species Quantity of Wood Specimens

MADw/SJRw CAFw SPSFw BCTw Total

Dalbergia cearensis 5 0 2 7 14 Dalbergia cochinchinensis 5 1 1 0 7 Dalbergia frutescens var.tomentosa 8 0 1 7 16 Dalbergia hainanensis 1 1 0 0 2 Dalbergia hupeana 2 7 0 0 9 Dalbergia latifolia 15 2 1 3 21 Dalbergia melanoxylon 9 0 0 2 11 Dalbergia nigra 21 1 8 26 56 Dalbergia odorifera 0 4 0 0 4 Dalbergia oliveri 4 2 0 0 6 Dalbergia retusa 16 0 0 0 16 Dalbergia sissoo 18 0 0 3 21 Dalbergia spruceana 3 0 3 6 12 Dalbergia stevensonii 11 0 0 0 11 Dalbergia tucurensis 12 0 0 0 12

11 Pterocarpus species—161 specimens

Species Quantity of Wood Specimens

MADw/SJRw CAFw SPSFw BCTw Total

Pterocarpus dalbergioides 12 0 0 0 12

Pterocarpus erinaceus 4 5 0 0 9

Pterocarpus indicus 25 2 1 1 29

Pterocarpus macrocarpus 13 3 1 1 18

Pterocarpus marsupium 13 0 0 2 15

Pterocarpus officinalis 20 0 0 1 21

Pterocarpus rohrii 9 0 0 1 10

Pterocarpus santalinus 4 0 0 0 4

Pterocarpus soyauxii 6 4 5 3 18

Pterocarpus tinctorius 5 1 0 0 6

Sample Polish Image Collection Dataset Creation

Dataset with 10,237 images

Total: 132,265

Train: 105,986

Val: 13,134

Test: 13,145

2048x2048

1600x1600 227x227

Patch dataset creation

CNN architecture-VGG16 & AlexNet

Transfer learning

www.image-net.org/ Google Deep Mind Training CNNs pre-trained on ImageNet

VGG16

ImageNet dataset

Wood images

Trained CNNs

14,197,122 images, 21,841 synsets

Training & Testing

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Accuracy

Accuracy

Stochastic Gradient Descent

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VGG16-15 Dalbergia species model

Average accuracy: 85.44%

Correct classified species(100%): D. frutescens D. oliveri D. hupeana D. sissoo D. melanoxylon D. nigra D. stevensonii

Totally misclassified species: D. cochinchinensis

Poorly misclassified species: D. odorifera

Leguminosae_Dalbergia_cochinchinensis_CAFw_20373_15567504-2018-05-30-174456.png

Leguminosae_Dalbergia_odorifera_CAFw_19152_15567504-2018-05-22-014340.png

Misclassified images

Standard images

Average accuracy : 67.44%

Correct classified species(100%): P. soyauxii

Poorly misclassified species: P. indicus P. rohrii

VGG16-11 Pterocarpus species model

P. angolensis P. indicus P. dalbergioides P. macrocarpus P. marsupium P. erinaceus

P. tinctorius P. santalinus P. soyauxii

P. officinalis P. rohrii

(1.000)

(1.000)

(1.000)

(1.000) (0.900)

(1.000) (0.960)

(0.967) (1.000)

(0.900) (0.767)

(1.000) (0.000)

Relative wood anatomical variability within class

Re

lative w

oo

d an

atom

ical distin

ctness

Low

H

igh P. soyauxii (1.000)

P. officinalis (0.967) P. rohrii (0.067)

P. macrocarpus (0.867) P. marsupium (0.743) P. dalbergioides (0.700) P. erinaceus (0.684)

P. angolensis (0.900) P. indicus (0.367)

P. santalinus (0.577) P. tinctorius (0.657)

AlexNet:15-species model

Average accuracy: 93.68%

Correct classified species(100%):

Poorly misclassified species: Dalbergia nigra (78.12%)

Dalbergia melanoxylon Dalbergia odorifera

AlexNet: 11-species model

Average accuracy: 88.38%

Poorly misclassified species: Pterocarpus indicus

AlexNet: 26-species model

Average accuracy:99.34%

Correct classified species: 12 Dalbergia species 7 Pterocarpus species

Poorly misclassified species: Pterocarpus indicus

AlexNet

No. of the specimens per species ≧ 10

Model accuracy ≧ 85%

No. of the images per species ≥ 100 ≥ 300

Model accuracy ≥ 99% Model robustness

Model accuracy: high-quality datasets > low-quality dataset Patch size ≥ 1000×1000

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AlexNet (93.68%/88.68%) outperforms VGG16 (85.44%/67.44%) on the image dataset

(Dalbergia/Pterocarpus) collected in this study.

Parameters for AlexNet model:

More than 10 specimens per species

Over 100 high-quality images per species

Patch size of 1000 × 1000 × 3

Automated computer vision models for field screening of wood species to combat

illegal logging.

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Global network to establish wood image database

Unpack the "black box" for feature representation in terms of wood anatomy

-- National Forestry & Grassland Administration (NFGA), China

-- China CITES Management Authority

-- National Natural Science Foundation of China

-- China Scholarship Council

Wood Anatomy and Utilization Department

Research Institute of Wood Industry

Chinese Academy of Forestry

Center for Wood anatomy Research

Forest Products Laboratory

United States Department of Agriculture

Wood Collections

(WOODPEDIA)

Chinese Academy of Forestry

IAWA-IUFRO Symposium Beijing 2019

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