=Paper=
{{Paper
|id=Vol-3010/PAPER_04
|storemode=property
|title=An enhanced approach for Plant Leaf Disease Detection
|pdfUrl=https://ceur-ws.org/Vol-3010/PAPER_04.pdf
|volume=Vol-3010
|authors=Akash Bhakat,Naveen Nandakumar,Rajkumar S
}}
==An enhanced approach for Plant Leaf Disease Detection==
https://ceur-ws.org/Vol-3010/PAPER_04.pdf
An enhanced approach for Plant Leaf Disease Detection
Akash Bhakat a, Naveen Nandakumar b and Rajkumar S c
a
Vellore Institute of Technology, India
b
Vellore Institute of Technology, India
c
Vellore Institute of Technology, India
Abstract
In the modern world with ever rising population, the demand and stress on the agricultural
produce to meet the ever growing demand is on record-level high. With the innovations like
Smart Agriculture, Natural Fertilizers and Genetically Modified Plants, there still are various
areas to focus upon.One of the main areas to focus that is majorly gone unnoticed is the diseases
in the plants. In many developing countries with less access to human healthcare there is little
to no scope to consider for plant health and disease detection. Due to this a large amount of
produce is lost due to diseases in plants. Furthermore most of these diseases can spread from
one plant to another starting a domino effect destroying the entire fields.Though some work on
this area, the accuracy achieved by the systems can still be increased and systems be made
easier to incorporate, use. This paper aims to solve the problem of Detection of Plant Disease
by analyzing the image of plant leaves. It aims to increase the existing accuracy of the various
existing works with the proposed approach using Transfer Learning and identifying disease in
a broader number of leaves than the existing works.
Keywords 1
Leaf Disease, CNN, transfer learning, AI, farming, plant diseases
1. Introduction
In this modern world, India still depends a lot on Agriculture. Agriculture provides 17% of the GDP
and provides employment to more than 60% of the Population. Also this sector is responsible for feeding
and providing with various Raw Products for Agro-Based Industries to satisfy the needs of the 1.36
Billion People in India.
With the ever growing demand and the land for the supply being limited and even depleting, it is
very much required to make the best use of the resources like land, water. For improving the quantity
and quality of the products, many innovative technologies like the use of Genetically Modified Plants
produce more Produce per Plant. But still there are huge losses endured due to diseases in plants.
Roughly the losses in agricultural production due to pathogens, diseases, weeds 20% to 40% of the
global production. Pathogens and pests are causing 10 percent to 28 percent losses in wheat, 25 percent
to 41 percent losses in rice, 20 percent to 41 percent losses in maize, 8 percent to 21 percent losses in
potato, and 11 percent to 32 percent losses in soybeans on a global scale, according to a study published
in the journal Nature, Ecology & Evolution. In this paper we acknowledge and focus on the disease of
the Plants. We believe that the diseases of the plants if detected earlier can help to contain the spread of
the disease and also help to produce more.
____________________________________
Algorithms, Computing and Mathematics Conference, August 19 – 20, 2021, Chennai, India.
EMAIL: rajkumars@vit.ac.in (Rajkumar S)
ORCID: 0000-0001-5701-9325 (Rajkumar S)
© 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
56
In this paper, we aim to take up this issue of losses due to diseases in plants and propose a method
of solving it. So, in this paper, we aim to study the existing models and propose a model which will
help in identifying disease with good accuracy in the leaves of Tomato, Apple, Blueberry, Cherry,
Grapes, Corn, Orange, Peach, Raspberry, Soya bean, Squash, Strawberry.
The proposed work focuses on 26 different categories of diseases that occur in the mentioned 12
types of plants.
2. Literature Review of Existing Methods
Table 1: Review of various classification methods of leaves
Author Method used Drawbacks
[1] Ghaiwat et al ANN, SVM, fuzzy logic. When training data is not linearly separable, it is
(2014) etc difficult to grasp the structure of the algorithm
and identify appropriate parameters in neural
networks.
[3] Badnakhe et al KNN with neural Crop diseases may be classified using artificial
(2011) network for detecting neural networks, fuzzy logic, and other soft
the diseases on leaves computing techniques.
automatically
[4] Arivazhagan, S. Color co-occurrence The training samples can be increased, along with
et al(2013) method with SVM shape and color characteristics, as well as the best
features, can be used as illness identification input
conditions.
[7] Naikwadi et al The spatial gray-level Results could be easily improved with a larger
(2013) dependency matrices database and advanced feature of color
were used to build the extraction
color co-occurrence
texture analysis
technique.
[11] Mondal et Color Co-Occurrence Used 12 features for classification but the overall
al(2015) technique, k-means accuracy was only 87%
clustering, Bayesian
classifier
[12] Padol et al Color co-occurrence The accuracy could have been improved by using
(2016) technique, K-means fusion classification techniques. The current
clustering algorithm accuracy remains to be 88,9%.
using SVM.
[13] Reza et al Color co-occurrence Uses a multi-SVM classifier which gives an
(2016) methods, Multi SVM accuracy of 86%
classifier.
[14] Tejonidhi et Bhattacharya’s distance It recognizes paddy's burning and blast diseases.
al(2016) method In addition, this technique may be used to detect
a variety of illnesses in different leaves. This might
aid farmers in identifying the illness in the leaf in
a practical and precise manner in a short period of
time.
[17] Pawar et al GLCM(Gray level co- Using extra texture characteristics can improve
(2016) occurrence method), classification accuracy. The current accuracy of
ANN the study remains to be 80.45%.
[18] Narmadha et Color co-occurrence Uses KNN and the accuracy achieved was 94.7%.
al (2017) technique, ANN, FUZZY
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classification, SVM,
KNN
[19] Tripathi et al K-means, GLCM, ANN, Presents a comparative study and gets an
(2016) SURF, CCM, SVM. accuracy of 95% with an SVM classifier.
[20] Prakash et GLCM,SVM,K-Means The research might help diagnose various plant
all(2017) illnesses and increase the classification accuracy,
which is now around 90%.
[21] Phadikar et al Bayes and SVM Accuracy : Baye’s – 68.1 % SVM – 79.5% accuracy
(2008) classifier, mean filtering
technique, and Otsu’s
algorithm
From the survey done on the existing techniques, we found many techniques, where the most popular
being K-means , SVM and Bayesian classification, and ANN(Artificial Neural Networks). We were not
able to find many techniques using Transfer Learning for the purpose.
Based on the review, it was observed that though some work has been done in the field, no
widespread work has been done taking into account a generalized and large number of plants and many
diseases. Most of the present works are concentrated on diseases in 1 category of plants.
Therefore, it is required to propose a generalized approach to predict numerous diseases of several
plants.
As a result, the goal of this research is to provide an approach/method for classifying leaf detection
with improved accuracy rates for different leaves and disease categories.
3. Proposed Method
Figure 1: Flowchart showing the overall development of the method
3.1. Dataset Collection
For any supervised learning project, the main component is the dataset. For this project, we used the
publicly available PlantVillage dataset [5] on Kaggle.
The dataset was accessed directly from Kaggle Notebook which was used to make the model for the
paper.
Over 50,000 pictures of healthy and diseased plant leaves are included in this collection. It contains
the infected images in 38 categories where 26 categories of diseases of the 12 plants, namely, tomato,
apple blueberry, cherry, grapes, corn, orange, peach, raspberry, soya bean, squash, and strawberry Input
leaf image.
The disease into which the diseases were classified:-
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Table 2: Fruit Diseases
Serial
Fruit Disease
Number
Apple scrab
1. Apple Apple Black rot
Cedar apple rust
2. Cherry Cherry Powdery Mildew
Corn Cercospora leaf spot Gray leaf spot
3. Corn Corn Common Rust
Corn (maize) Northern Leaf Blight
Grape Black Rot
Grape Esca (Black Measles)
4. Grape
Grape Esca (Black Measles)
Grape Leaf blight (Isariopsis Leaf Spot)
5. Orange Orange Huanglongbing (Citrus greening)
6. Peach Peach Bacterial spot
7. Pepper Bell Bacterial spot
Potato Early blight
8. Potato
Potato Late blight
9. Squash Squash Powdery mildew
10. Strawberry Strawberry Leaf scorch
Tomato Bacterial spot
Tomato Early blight
Tomato Late blight
11. Tomato Tomato Leaf Mold
Tomato Septoria leaf spot
Tomato Spider mites Two-spotted spider mite
Tomato Target Spot
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Tomato Yellow Leaf Curl Virus
Tomato mosaic virus
Figure 2: Distribution of pictures of leaves of different plants
3.2. Data Pre-Processing
For the dataset, the image contains background noise. In order to extract the relevant region from
the input picture, the Tiramisu model must be used. It is based on DensNet,where all the layers are
interconnected. Also the Tiramisu model adds skip connections to the up-sampling layer like Unet.
3.3. Designing the Neural Network
For making the model which would be trained on the images, we incorporated the transfer learning
technique.
Transfer learning is a novel method to deep learning in which models that have been pre-trained for
one task are repurposed for another.
One of the biggest advantages being that there is no need for extra feature extraction step. This is
because they are very deep neural networks where the initial layers act as feature extractor.
In our case we went on with the use of Inception V3 pre-trained neural network. It is a family of
Inception neural networks where all the previous features of inception v1 and v2 are incorporated along
with label smoothing, factorized 7x7 optimizer, RMSProp optimizer and BatchNorm.
Also, in comparison with its counterparts like VGGNet, Inception networks work better and provide
more computationally efficiency both in the terms of parameters generated by the network and the
economical cost incurred in terms of memory and other resources.
In this model we added further layers at the end to get the prediction into the 5 categories as we
desired. After the model being compiled with trained it with our large dataset with 25 epoch and batch
size of 16.
3.4. Training the model
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The model is being trained on the training dataset, which is produced by taking 70% of the entire
dataset for training and 30% for testing.
3.5. Checking for Accuracy Achieved
In the model, we were able to achieve an accuracy of 96.74% on Validation Accuracy.
4. Experimental Results and Analysis
In this section, we are briefly explaining the result of the proposed model.
4.1. Results of Data Preprocessing
With the Tiramisu model, we were able to extract the only leaf image from the overall image.
Figure 3: Image showing the segmented image of the leaf A: Original leaf image with the background.
B: Image of leaf extracted and background removed.
4.2. Result of the proposed model
The approach aimed to capture a large base of leaves that can be checked for any diseases. With the
work, we were able to achieve the aim of targeting 12 different plant leaves and detecting 26 different
diseases.
With the work we were able to achieve pretty good performance as we can see from the
visualizations.
Figure 4: Graph showing the accuracy of the training phase
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Figure 5: Graph showing the accuracy of the validation phase
Figure 6: Graph showing the loss of the validation phase and training phase
Figure 7: A graph depicting the validation and training phases' accuracy.
The system when fed with the input of the image of a plant leaf, predicts the top 5 diseases with bar chart
visualization. With this, we aim to address anomaly that can occur in predictions. Meaning it provides options and
its opinion on the diseases that the plant leaf has with the amount of confidence in each diseases.
Also it provides the user,i.e., farmers to view alternative diseases that can be present.
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Figure 8: Image showing the prediction of the model on an input image
5. Conclusion
We had started work on the paper focusing on detecting multiple diseases in multiple plants.
We focused on 12 categories of plants namely-
Table 3: Fruit category
Tomato Apple Blueberry Cherry
Grapes Corn Orange Peach
Raspberry Soya Bean Squash Strawberry
On our model, we achieved an accuracy of 96.74%. With our paper, we conclude that with the model
developed we can classify the images into vast 26 categories of the diseases from 11 different plant
types efficiently and effectively with an overall accuracy of 96.74% higher than any of the existing
works and also into more categories than any existing works.
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We think that the future work on this can be to convert this model into an application, which can
easily be used by the real farmers to use the model for fast and efficient disease identification.
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