IRJET- Recognition of Plants using Leaf Image with Neural Network and Computer Vision

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 06 Issue: 04 | Apr 2019 www.irjet.net p-ISSN: 2395-0072
© 2019, IRJET | Impact Factor value: 7.211 | ISO 9001:2008 Certified Journal | Page 5499
Recognition Of Plants Using Leaf Image With Neural Network and
Computer Vision
Shivam Upadhyay[1], Aakash Yadav[2], Kaushik Yadav[3], Sonal Chaudhari[4]
[1],[2],[3]Student, Department of Computer Engineering, [4]Associate Professor Datta Meghe College Of Engineering,
Mumbai University, Airoli, India
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Abstract – The most common method people used to differentiate
and identify any given plant is though the leaf of the plant.
Although there are many more parameters which help to
distinguish one plant to another leaves are more common. The
leaves have many different parameters which can be used to
recognize plants, these parameters include shape of the leaf, size of
the leaf, type of venation, area, color and many more. That is why
the leaf of the plant plays a very important role in plant
recognition. This system is a computer based automatic plant
identification system. Vein feature as a derived feature is extracted
based on leaf structure. At the first stage leaf images are obtained
using digital scanner. Then morphological features are extracted
which act as input to the classification stage.
Keywords— Plant Recognition Computer Vision,
Neural Network, Edge Detection, Feature Extraction,
Pre-processing.
I. INTRODUCTION
Plants form and integral part of human life and many people often
find it challenging and difficult to differentiate between different
plants from their leaves. Also due to the increased awareness for
environment protection in the past few decades it becomes very
important to know about the plants we are surrounded with to better
understand the different ways in which plants affect our day to day
life and how to benefit us. Due to the recent reports on global
warming, there is a new curiosity and awareness among people
regarding plants and that involves knowing the plants. But the
process of identifying plans using the leaf is rather difficult. Since the
leaves of plants are two dimensional rather than three dimensional
like actual plants it is slightly easier to extract feature from these
leaves like this shape, size, color, texture, area and to classify the
plant based on these features. It involves training a neural network
model on a Number of image of a particular class to process and
extract the features which would then be used to classify the species
of the plant. The leaf detection applications works by scanning the
leaf where the leaf image is analyzed and compared with the available
dataset and then showing the plant name depending on the match
found.
II. LITERATURE SURVEY
In recent times there have been a number of studies on the
subject of plant recognition or classification or recognition
by using the leaf image. Leaf is most commonly used to
identify the plants by using the leaf color, leaf shape, leaf
area and very complex parameters. This paper work uses a
simple approach of using the neural network models to
extract features from implies i.e. edge detection to obtain the
leaf shape and the vein structure.
A huge amount of research is done on the process of classifying
plants using the leaf image. Arunpriya [1] this proposed approach
involves three phases which include the pre-processing of the image
to obtain a grayscale image from the RGB image it is followed by
the enhancement of boundary for the proper extraction of the five
fundamental image features which are them used for the
classification of the plant using the support vector machine
classification for leaf identification
.Leaf snap [2] Leaf snap is a series of electronic field guides being
developed by researchers from Columbia University, the University
of Maryland, and the Smithsonian Institution. The free mobile apps
use visual recognition software to help identify tree species from
photographs of their leaves. The high-resolution images in the
original app were created by the conservation organization.
The Leaf snap UK app includes trees from the United Kingdom with
species information and imagery provided by the Natural History
Museum in London. More information can be found on the Natural
History Museum website. Some researchers have proposed to
calculate the input images area using another object as a reference
[3]. Some researchers [4][5] proposed the use of faster artificial
neural networks in order to improve the performance of the system
. Others [7] employed k-nearest neighbor classifier for the
classification of the plants.

III. METHODOLOGY
The given project consist of two main components
1) Training the model
2) Predicting Label
In the training process Dataset of images is created to train the
neural network model. On this training images dataset image
augmentation is performed in order to increase the number of
training images. Image augmentation also improves the Quality of
data as multiple point of view of image is available. After image
augmentation is performed then the images are pre-processed to
convert each image in a standard format of 100*100 pixels and the
image is converted into grey scale image and then the filters are
applied to detect the edge from the image. The following is shown
the figure below:
1) Preparing image dataset:
The images can be obtained by taking photos from the camera in
the phone or by searching for images on the internet. While
preparing the training data all the images have a white
background in order to avoid background interference and all the
images all taken at a minimum distance of 15cm. The model was
trained on leaves of 5 plants namely Mango, Peepal, Grape,
Eucalyptus, Guava. The dataset set consists of 200 images of each
class and contains a total of 1000 images for 5 classes The samples
are as shown in the below figure.
(a) (b) (c)
(d) (e)
Fig. 2 Leaf Samples : (a) Mangifera indica(Mango) (b) Ficus religiosa(Peepal)
(c) Vitis vinifera(Grape) (d) Eucalyptus Globu(Eucalyptus) (e) Psidium
guajava(Guava)
2) Image augmentation
The images used to create the dataset are then augmented to
increase the size of the training data. This ensures that the
model is properly trained on a number of data and improves
the accuracy of the model and also ensures that the system is
able to recognize the image in different orientations. It
involves performing a number of operations on a given
image to obtain different mirror images. The Image
augmentation is as shown in the below figure.
Fig. 1 Training and Testing model
Image Augmentation

.
Fig 3. Image Augmentation
3) Image pre-processing and edge detection:
The images are processed to convert each image from original size
to required size in this case each image was converted to a size of
100*100 pixels. The images are then converted from RGB to gray
scale images and then the image is converted into binary image.
Now, we apply a 3*3 filter (kernel) to detect the edges and the veins
of the leaf. The main aim to perform edge detection is to separate
and identify the image from its background to extract the features
of image which is then used to train the classifier.
m*n image
100*100 Grayscale
pixel image image
Resize
Grayscale
Edge Detector
Fig. 4 Pre-processing
Fig. 5 Edge Detection
The input image is mapped by adjusting the weights iteratively.
While training the model the weight optimization is done by back
propagation of error. The training is started by initializing the
weight and biases and it progresses in a number of rounds or
epochs so as to minimize the errors unit the desired amount of
accuracy is not obtained. Initially random weights and biases are
used to initialize the neural network once it is over, training can be
started. Once the training of the model is initialized, the Initial
weights and biases change as the training progresses and it tries to
minimize the loss and improve the accuracy of the system. Before
training the data is divided into two categories i.e. training data
and testing data. The training is performed using the training data
and the testing data is used the test to accuracy of the trained
model. Depending on this observation Training and testing data is
changed until the desired Accuracy and performance is reached.
Once the cycles or epochs are completed training is stopped and the
model is saved for classification.
IV. RESULT
The system is designed using tensorflow and python. The
system is executed on a laptop with following configurations:
2.30 GHZ core i5 processor, 8 GB of RAM, run under Microsoft
Windows 10-64 bits. The result is as follows:

V. CONCLUSION
In the above paper, we investigated the use of neural networks and
computer vision for classifying plants using their leaf image and
benefits of such a system to Botanists and other users who use the
system for classifying plants. Our system depends upon the ability of
the neural networks to extract the required characteristics from
the leaves and store it. This model is then used to predict the label
of any input image depending upon the features extracted from the
images of the training data.
The above analysis shows that the system is capable of identifying
multiple input images. However the precision and accuracy of the
system can be improved. Our future research will work towards
training a model to classify the plants from images in a busy or
noisy background having background interference
VI. Reference
[1] ArunPriya C., Balasaravanan T., Antony
Selvadoss Thanamani, “An Efficient Leaf
Recognition Algorithm for Plant Classification
Using Support Vector Machine”, Proceedings of
the International Conference on Pattern Recognition, Informatics
and Medical Engineering, 2012, pp. 428-432.
[2] Neeraj, K., et al., Leafsnap: A Computer Vision System for
Automatic Plant Species Identification, in ECCV 2012. 2012,
Springer. p. 502-516.
[3] Sanjay B patil and Dr. Shrikant K Bodhe, “ Betel Leaf
Measurement Using Image Processing”,
International Journal on Computer Science and engineering(IJSCE),
Vol. 3 No. 7 july 2011.
[4] H. Fu, Z. Chi, D. Feng, and J. Song, “Machine learning techniques
for ontology-based leaf classification,” IEEE 2004 8th International
Conference on Control, Automation, Robotics and Vision, Kunming,
China, 2004.
[5] J. Du, D. Huang, X. Wang, and X. Gu, “Shape recognition based on
radial basis probabilistic neural network and application to plant
species
identification,”inProceedingsof2005
International Symposium of Neural Networks, ser. LNCS 3497.
Springer, 2005.
[6] X. Gu, J. Du, and X. Wang, “Leaf recognition based on the
combination of wavelet transform and gaussian interpolation,” in
Proceedings of
International Conference on Intelligent Computing 2005, ser. LNCS
3644. Springer, 2005.

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