=Paper=
{{Paper
|id=Vol-2380/paper_168
|storemode=property
|title=Plant Identication on Amazonian and Guiana Shield Flora: NEUON submission to LifeCLEF 2019 Plant
|pdfUrl=https://ceur-ws.org/Vol-2380/paper_168.pdf
|volume=Vol-2380
|authors=Sophia Chulif,Kiat Jing Heng,Teck Wei Chan,Mohammad Abdullah Al Monnaf,Yang Loong Chang
|dblpUrl=https://dblp.org/rec/conf/clef/ChulifHCMC19
}}
==Plant Identication on Amazonian and Guiana Shield Flora: NEUON submission to LifeCLEF 2019 Plant==
https://ceur-ws.org/Vol-2380/paper_168.pdf
Plant Identification on Amazonian
and Guiana Shield Flora:
NEUON submission to LifeCLEF 2019 Plant
Sophia Chulif, Kiat Jing Heng, Teck Wei Chan, MD Abdullah Al Monnaf, and
Yang Loong Chang
Department of Artificial Intelligence, NEUON AI, 94300 Sarawak, Malaysia
http://www.neuon.ai
{sophiadouglas,kiatjing,chanteckwei,abdullah,yangloong}@neuon.ai
Abstract. This paper will look into the use of fine-tuned Inception-v4
and Inception-ResNet-v2 models to automate the classification of 10,000
plant species. Prior to training the networks, the training dataset was
pre-processed to remove the noisy data. The team submitted three runs
which achieved comparable performances to human experts on the test
dataset comprising 745 observations for all the evaluation metrics. For
the trained systems to generalise better, the systems were trained for
multi-task classification and is able to classify plant images based on
their species, with support of their genus and family labels. In particular,
an ensemble of Inception-v4 and Inception-ResNet-v2 networks achieved
a Top-1 accuracy of 0.316 and 0.246 for the test set identified by experts
and the whole test set respectively.
Keywords: Plant identification, computer vision, convolutional neural
networks, data cleaning
1 Introduction
The plant identification challenge of the Conference and Labs of the Evaluation
Forum (CLEF) is an annual challenge focusing on the automation of plant iden-
tification. In recent years, automated identification of plants has become a high
interest for botanical specialists and computer experts alike [3]. Transitioning
from the conventional computer vision through feature descriptor methods [14],
deep learning based methods have been able to significantly improve accuracy of
the automation of plant identification [7,8,11]. This application is essential in the
utilisation, management and conservation of flora of any kind. When the plant
identification challenge first commenced in 2011, the main focus was to be able
to identify over 70 different tree species given 3996 leaf images as training data
[5]. The total number of species and training images in the challenge has then
Copyright c 2019 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0). CLEF 2019, 9-12 Septem-
ber 2019, Lugano, Switzerland.
notably increased each year. With the training dataset initially covering only
leaf images, it has now expanded to different plant organs and multiple views of
the plant. Since 2017, the total number of plant species has reached 10,000 along
with the presence of noisy images (images that are unrelated to the plant of in-
terest) in the training dataset - this posed a great challenge to the participants
in evaluating the extend of a noisy dataset competing with a trusted dataset
[2]. Furthermore, the plants in the training dataset shared a low intra-class and
high inter-class similarity. They may belong to the same class but look clearly
different from one another [1]. Besides the variance, some of the distinct plant
organs and views may not be captured in the given training images, resulting
in the lack of training data thus difficulty in predicting the species. This depicts
the realistic challenge of the real-world applications in plant identification.
For PlantCLEF 2019, the dataset provided was focused on the Guiana shield
and the Amazon rainforest which is known to be the largest collection of living
plants and species in the world [6,4]. The task was to predict 10,000 plant species
and the participants were provided with 448,071 images of training data. Along
with a test set containing 2,974 images and 745 observations, the main evaluation
method of the competition was the Top 1 accuracy of the submitted predictions
based on the 745 observations.
The primary challenge of PlantCLEF 2019 was the decreased average number
of images per species in the training dataset. Many species contained only a few
images and some even contained only one image. Moreover, the training dataset
consisted of noisy data that constituted from non-plant images and duplicate
images that may come with incorrect labels.
This paper presents the preparation made prior to the training of the system.
The system was fine-tuned from pretrained Inception-v4 and Inception-ResNet-
v2 models to automate the classification of the given 10,000 plant species.
2 Data Preparation
2.1 Data Analysis
The dataset provided consisted of 10,000 species which mainly focused on the
Guiana shield and the Amazon rainforest. The reported training dataset size is
448,071 (based on the number of rows in PlantCLEF2019MasterTraining.csv)
however, the downloaded training dataset size was 433,810. Therefore, the ac-
tual downloaded dataset included the training dataset of 433,810 images which
consisted of 10,000 classes (species), and a test dataset of size 2,974 from 745
different observations.
As mentioned in the challenges data collection description, among these
10,000 species many contain only a few images and some of them contain only
one image. Moreover, it was observed that there were many variations in the
training dataset that could affect the performance of the plant identification.
Other than real-world plant images i.e. fruit, branch, trunk, stem, root,
flower, leaf etc., the training dataset initially contained many images that do not
look like real actual plants i.e. sketches, paintings, illustrations, plant herbari-
ums, and small regions of the interested plants. In addition, the dataset also con-
tained non-plant images i.e. animals, logos, book covers, graphs, table, medicine
bottles, humans, chemical-bond diagrams, presentation slides, maps etc. Besides
this, the dataset consisted of images with duplicate names in different classes
(folders). Furthermore, the dataset consisted of duplicate images with different
names within the same folder as well as in different folders. Having different
labels for the same images could cause confusion to the machine.
These characteristics observed could affect the performance of the plant iden-
tification therefore the approach was to pre-processed the dataset.
2.2 Data Cleaning
From the analysis in subsection 2.1, the dataset was then pre-processed (cleaned)
to allow better execution of plant identification. First, the images with duplicate
names were removed as those images are actually the same. The dataset given
consisted of 433,810 images and 10,000 classes of plants. After eliminating the
duplicate names, the dataset was reduced to 279,183 images and 8,468 classes.
Then, the duplicate images (without having the same name) were further re-
moved. After removing them, 263,987 images were left with 8,419 classes. Finally,
the non-plant images were eliminated, resulting in a total of 250,646 images and
8,263 of classes for training1 .
In the approach of eliminating images with duplicate names in different fold-
ers, since there is no method to decide which class they actually belong to (no
experts to verify), all images with the same name were removed.
On the other hand, in order to remove duplicate images within the same folder
as well as different folders, inception-v4’s feature extractor layer (Mixed 7d) was
used to compare images so that those with 0.99 cosine similarities in features
can be eliminated. If the duplicate images only exist in the same class (folder),
only one of the images will be retained. Then, the difference hash algorithm
was used to detect more duplicates within the dataset. The hash value for every
image was calculated and then compared to get those with very little difference.
Images with little difference hash values mean they are identical. Likewise, if the
identical images only exist in the same class (folder), only one image is retained,
the rest are eliminated.
To detect non-plant images, a discrimination network for identifying plant
and non-plant images was trained. This process consisted of 3 phases.
In phase 1, the positive samples (plant) were taken from PlantClef 2016 while
the negative samples (non-plant) were taken from ImageNet2012 (excluding the
plant classes). The training dataset size was 4,000, with each class having 2,000
samples. Meanwhile the validation dataset size was 2,000, with each class having
1,000 samples. An Inception-V4 plant and non-plant classifier was then trained
using these datasets.
1
The cleaned list can be found at Github via https://github.com/changyangloong/
plantclef2019_challenge
Fig. 1. Data cleaning process.
In phase 2, 5,000 samples were randomly selected from PlantClef 2019 and
predicted using the trained classifier. The performance on this sample was eval-
uated manually. After evaluating its performance, the training set was refined
by manually correcting the prediction and adding them back to the training
set. Then, the Inception-V4 plant and non-plant classifier was retrained using
the new training set. Phase 2 was repeated until the performance satisfied the
accuracy of over 90%. In this case it was repeated 4 times.
In phase 3, the Inception-V4 plant and non-plant classifier was applied to the
whole dataset to remove the non-plant images. Only the softmax probability of
0.98 for the non-plant class was regarded as non-plant images.
The entire process is visualised in Fig. 1.
3 Methodology
This section will describe the motivations for using Inception-v4 and Inception-
ResNet-v2 for the plant classification challenge, the training setup, network hy-
perparameters and validation results obtained from the trained networks.
3.1 Network Architecture
The backbone networks used for classifying PlantCLEF 2019 images were Incept-
ion-v4 and Inception-ResNet-v2 models. These two models were adopted in this
plant classification task as they come with the lowest Top-1 Error and Top-5
Error for single-crop - single-model experimental results, when being proposed
in the original paper [12]. Besides, the use of inception modules allows filters with
multiple sizes to perform convolution on the same level to cater to the variation
in the location of the information on the image [13], which is applicable on the
training dataset.
Both of the networks implement Convolutional Neural Network (CNN) archi-
tecture, which typically consists of convolutional layers, pooling layers, dropout
layers and fully-connected layers. The models were fine-tuned from pre-trained
weights on the ImageNet dataset [10].
In fact, Inception-v4 and Inception-Resnet-v2 networks share an identical
stem as the input part of these networks. However, these two models demonstrate
differences in architecture after the stem.
For Inception-v4 model, there are three main inception modules following the
stem, namely Inception-A, Inception-B and Inception-C modules. The output
will then be concatenated and fed to the next module. Meanwhile, there is a
reduction module after Inception-A and Inception-B modules to alter the width
and height of the grid. These modules work together to serve as the model’s
feature extractor.
On the other hand, Inception-ResNet-v2 model makes use of residual con-
nections in addition to inception modules after the implementation of stem.
This allows Inception-ResNet-v2 to achieve higher accuracies within a shorter
time frame, while being similarly computationally expensive as the Inception-v4
model. For residual connections to work, the pooling layers from pure Inception
modules are replaced with residual connections. Similarly, there is a respective
reduction module following Inception-ResNet-A and Inception-ResNet-B mod-
ules.
Both Inception-v4 and Inception-ResNet-v2 model have the same structures
for the classification part, which consists of an average pooling layer, a dropout
layer, and a fully-connected layer which return the softmax probabilities over
predicted output classes.
3.2 Training Setup
During the network training, two types of classification methods were investi-
gated, namely single label classification and multi-task classification.
Single Label Classification The first classification method is the conventional
classification method which is based on single label prediction model. The labels
are the plant species, therefore there are 10,000 classes.
Multi-Task Classification Another method used in this plant identification
task is the multi-task classification whereby the labels “Species”,“Genus” and
“Family” of the samples were used in training. This allowed the network to
regularise and generalise better on images from a large number of classes. The
total number of family class is 248, while the genus class is 1,780 2 .
2
The multi-task label can be found at https://github.com/changyangloong/
plantclef2019_challenge
Library Used The networks were implemented using TensorFlow Slim library,
with the weights being pre-trained on ImageNet datset [10]. The networks were
then fine-tuned accordingly using the hyperparameters described in Sub-section
3.4. Since the adopted models pre-trained on ImageNet have only 1,000 classes,
the fully-connected layer of the adopted models was adapted to 10,000 classes
during transfer learning for PlantCLEF 2019. For multi-task classification, there
were two additional fully-connected layers which were catered to 248 “Family”
classes and 1,780 “Genus” classes.
Training Data The input image size of the training was 299 × 299 × 3. By
separating 20,000 samples from the training samples as a validation set, the
total remaining training set comprised 230,646 images. Although the total num-
ber of classes is reduced from 10,000 classes to 8,263 classes, the network was
still trained to classify 10,000 classes through class mapping. This allows model
update when missing classes are to be found.
3.3 Data Augmentation
Data augmentation was performed on the training images to increase the train-
ing sample size. With this, the CNN network can learn features that are invariant
to their locations in the images and various transforms, which then effectively
reduces the chance of overfitting [9]. Random cropping, horizontal flipping and
colour distortion (brightness, saturation, hue, and contrast) of images were ap-
plied to the training dataset to increase the possibility of classifying the correct
plants regardless of their different environments or orientations.
3.4 Network Hyperparameters
The learning dropout rate was set to 0.2 (keeping 80 % of the neurons before
the fully-connected layer) while the optimizer used was Adam Optimizer. The
optimizer took on an initial learning rate of 0.0001. Meanwhile, the gradient was
clipped at 1.25 to prevent the occurrence of exploding gradients. Softmax cross
entropy loss was used to compute the error between the predicted labels and true
labels; while the L2 regularization loss was added with weight decay of 0.00004.
The network hyperparameters can be summarised in Table 1.
3.5 Validation
Prior to training, 20,000 samples were randomly separated from the cleaned
training dataset of 250,646 images as validation set. It was ensured that each of
the remaining 8,263 classes in the training set has at least one sample left for
training. A validation sample was not added if there is only one sample left for
training in each class. In other words, a class will not have a validation sample
if it has only one sample.
Table 1. Network hyperparameters used for training networks.
Parameter Value
Batch Size 256
Optimizer Adam Optimizer
Initial Learning Rate 0.0001
Gradient Clipping 1.25
Loss Function Softmax Cross Entropy
Weight Decay 0.00004
There were 4 approaches in testing the validation set on 4 different kinds
of network. The approaches include “Top 1 Centre Crop”, “Top 1 Centre Crop
+ Corner Crop”, “Top 5 Centre Crop”, and “Top 5 Centre Crop + Corner
Crop”. Meanwhile the 4 different networks included “Inception-v4”, “Inception-
v4 Multi-task”, “Inception-ResNet-v2 Multi-task”, and “Inception-v4 Multi-task
+ Inception-ResNet-v2 Multi-task” with different dataset sizes. Note that all the
trained networks were validated upon the same 20,000 validation images.
The “Centre Crop” approach considers the centre region of the sample. The
centre region was cropped and resized then passed into the network for testing.
The “Corner Crop” approach on the other hand focused on the centre, top
left, top right, lower left, and lower right region of the image. Each region was
cropped and resized then passed into the network for testing. The “Top 1” and
“Top 5” approaches represent the Top 1 and Top 5 predictions based on the
testing results.
3.6 Inference Procedures
The following procedures were adopted for inferencing the predictions of the
test dataset. There were three models used for inference, namely “Multi-Task
Inception-v4”, “Multi-Task Inception-ResNet-v2” and “Multi-Task Inception-v4
+ Multi-Task Inception-ResNet-v2”.
1. The test images with the same observation ID were grouped together.
2. A total of five center crop and corner crop images were then produced for a
single test image.
3. The test images grouped under the same observation ID were fed into the
CNN models for label predictions, as shown in Fig. 2. This would mean a
total of five predictions for a single test image.
4. The probabilities of each prediction were averaged over the total number of
test image crops grouped under the same observation ID.
5. The top 100 probabilities with value greater than 0.001 were collected for
result tabulations.
6. Step 2 to 5 were repeated for every different observation ID.
Fig. 2. Process of feeding test images into the trained models.
4 Results and Discussions
4.1 Validation Results
Inception-v4 and Inception-ResNet-v2 were the 2 networks used for training and
classification in these 4 approaches. The Multi-task description represents the
classification on multiple labels, i.e. Species, Family and Genus.
The validation results are shown in Table 2. The results have shown that
while eliminating the duplicate image could increase the performance of the
single-task network, multi-task classification method was able to achieve a even
higher accuracy on the validation dataset.
Interestingly enough, removing non-plant images after filtering duplicate im-
ages out leads to a slight drop in the single-task Inception-v4 network’s perfor-
mance. Since no Inception-v4 network is trained without the non-plant images
(i.e. keeping potentially duplicate images but of plants only) as benchmark, there
is no way to deduce whether the presence of duplicate images or non-plant images
is the main factor to the performance drop in our current experiments.
4.2 Submitted Runs
The team submitted a total of three runs based on three different trained models
which achieved the top-3 validation accuracy. The models are described in the
followings:
Holmes Run 1 utilises fine-tuned Inception-v4 model catered to multi-task
classification (which are Species, Genus and Family).
Table 2. Validation accuracy for different networks.
Top 1 Top 5
Dataset Top 1 Center Crop Top 5 Center Crop
Network
Size Center Crop + Center Crop +
Corner Crop Corner Crop
Inception-v4 433,810 42.83 % 43.70 % 61.48 % 62.67 %
Inception-v4 279,183 46.87 % 47.97 % 64.16 % 65.08 %
Inception-v4 250,646 46.73 % 47.76 % 63.87 % 64.95 %
Multi-Task
250,646 48.87 % 49.68 % 65.20 % 65.94 %
Inception-v4
Multi-Task
250,646 48.30 % 49.10 % 65.07 % 65.72 %
Inception-ResNet-v2
Multi-Task
Inception-v4
+ 250,646 51.96 % 52.68 % 65.79 % 68.47 %
Multi-Task
Inception-ResNet-v2
Holmes Run 2 is an ensemble of Inception-v4 and Inception-ResNet-v2, and
is also catered to multi-task classification.
Holmes Run 3 summarises prediction results from fine-tuned Inception-ResNet-
v2 catered to multi-task classification.
The run files were formatted as ObservationID; ClassID; Probability; Rank.
4.3 Sample of Predictions
Fig. 3 depicts the prediction output of test images grouped under the same
observation ID. This was to visualise the predictions generated by the models
for evaluating the model performance.
4.4 LifeCLEF 2019 Plant Challenge results
The team submitted three runs with Holmes Run2 being the best performance
among our submission in terms of Top-1 accuracy,Top-3 accuracy and Top-5
accuracy. Holmes Run 2 achieved Top 1 accuracy of 31.6% for the test set iden-
tified by experts, despite the occurrence of missing classes after data cleaning.
This proves that using an ensemble of two different state-of-the-art CNN models
can increase the robustness of the system and return better predictions. Table 3
summaries the results achieved by the team.
Mean Reciprocal Rank (MRR) was also used to evaluate the performance of
the submitted runs. The MRR is the average of the reciprocal ranks of the whole
test set, with the reciprocal rank of a query response being the multiplicative
inverse of the rank of the first correct answer. It can be mathematically repre-
sented as shown, with | Q | being the frequency of plant occurrences in the test
set.
Fig. 3. Visual representation of predictions for test images with observation ID = 348.
Table 3. Validation accuracy for different networks.
Top 1 Top 3 Top 5 MRR
Test Top 1 Test Test Top 5 Test MRR
Set Whole Set Set Whole Set Whole
Team Run Rank
Identified Test Identified Identified Test Identified Test
by Set by by Set by Set
Experts Experts Experts Experts
Holmes Run 2 1 0.316 0.247 0.376 0.419 0.357 0.362 0.298
Holmes Run 3 2 0.282 0.225 0.359 0.376 0.321 0.329 0.274
Holmes Run 1 3 0.248 0.222 0.325 0.368 0.325 0.302 0.269
|Q|
1 X 1
M RR = (1)
| Q | n=1 ranki
Holmes Run 2, being our best performing machine, has achieved a MRR
of 0.362 and 0.298 for the test set identified by experts and the whole test set
respectively, as shown in Fig. 6.
At the same time, all the three runs submitted by the team outperform 1 out
of 5 human experts of the Amazonian French Guina flora according to Top-1 (Fig.
7) and Top-3 accuracies. As for Top-5 accuracy, Holmes Run 2 outperformed 2
human experts, as captured in Fig. 8.
Fig. 4. Top-1 accuracy achieved by all the submitted runs.
Fig. 5. Top-5 accuracy achieved by all the submitted runs.
Fig. 6. MRR achieved by all the submitted runs.
Fig. 7. Top-1 accuracy achieved by all the submissions including human experts.
Fig. 8. Top-5 accuracy achieved by all the submissions including human experts.
4.5 Discussions
The automated identification of plants for PlantCLEF 2019 has shown a notable
decrease in performance compared to the previous editions. This may be due
to the decrease of per class samples as mentioned in the challenge’s description.
In such real-world condition, the dataset requires pre-processing before training
is done. Although it has been seen that training with noisy data can be more
profitable [2], the noisy data in this challenge worsens the performance of the
automatic plant identification. At this stage of understanding, the team believe
the portion of noisy images should not be occupying a large margin (duplicates
file names are 55.13% out of the whole training set) of training set to act as good
regularisation agent.
Therefore, the dataset was cleaned before training. This prevents the network
from getting trained by large amount of noisy data such as the non-plant images
and duplicate images/names (which may come with incorrect labels). Moreover,
by adding multi-task classification in our system, it has helped in regularising
the network and improving the performance. Modelling a relationship between
Species, Genus and Family is essential for better plant recognition.
Since the cleaned dataset is reduced to 8,263 classes, there was no capabil-
ity to distinguish the missing classes. Hence, the label for the missing classes
was unable to be predicted if it is present in the test set resulting in a lower
performance. Additionally, 20,000 images had been sidelined for validation pur-
poses which are believed to be helpful in increasing the performance if they were
added.
5 Conclusion
The task of the PlantCLEF 2019 challenge was to return the most likely matching
species for each observation (a set of images of the same plant) of the test set.
In this paper, the team has presented the overview and results of our approach
in doing so. With regards to the diversity of the dataset, the team has found
that the cleaning of the dataset and multi-task classification of Species, Genus
and Family improved the prediction results.
According to the competition results released, our trained model was better
than one of the experts in Top 1 and Top 3 accuracy. Additionally, it has a close
performance with Expert 4. Our model even outperformed 2 experts for Top 5
accuracy. Overall, 3 of our submitted runs obtained the good results in every
machine category.
The identification of plants based off images is indeed a difficult task even
for some of the botanical experts. Relying on plant images alone is usually in-
sufficient to determine the correct species as they may contain only partial in-
formation of the plant [3]. Based on the competition results of PlantCLEF 2018
and 2019, it can be considered that with sufficient data for training, machines
are able to perform nearly equal or better than a human.
For future work, the plant images from the missing classes can be retrieved
if the ground-truth labels are known and will be used for training the networks
for better predictions.
Acknowledgment
The resources of this project is supported by NEUON AI SDN. BHD., Malaysia.
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