=Paper=
{{Paper
|id=Vol-3058/paper36
|storemode=property
|title=Identification Of Rice Plant Diseases Using Image Processing, Machine Learning & Deep Learning: A Review
|pdfUrl=https://ceur-ws.org/Vol-3058/Paper-061.pdf
|volume=Vol-3058
|authors=Madhu Bala,Dr. Vineet Mehan
}}
==Identification Of Rice Plant Diseases Using Image Processing, Machine Learning & Deep Learning: A Review==
https://ceur-ws.org/Vol-3058/Paper-061.pdf
Identification of Rice Plant Diseases Using Image Processing,
Machine Learning & Deep Learning: A Review
Madhu Bala1 and Vineet Mehan2
1,2
Maharaja Agrasen University, Baddi,H.P, 174103,India
Abstract
Agriculture is the primary source of livelihood for about more than 50% of the Indian
population and rice is one of the major food grains of India. It is observed that rice plant
diseases are the major contributors to reduce the production & quality of food. Identification
of such diseases may improve the production quality. This paper gives an idea about
different methods such as image processing, machine learning & deep learning which are
used to detect deadly diseases in rice plants. Much research has been done to automate the
rice plant disease detection process using images of the leaf. This manuscript has compared
different rice plant disease detection methods and it is found that deep learning methods are
more promising than other two methods.
Keywords 1
Rice plant, image processing, machine learning, segmentation, deep learning
1. Introduction
Agriculture plays an important role in the economic growth of every country and so it is necessary
to ensure its development. The spread of various diseases in rice plants has increased in recent years.
There is a variety of plant pathogens such as viral, bacterial, fungal and these can damage different
plant parts above and below the ground[1]. However, some abiotic factors such as water, light,
radiation, temperature, humidity, atmosphere, acidity, and soil also affect the growth of the plant[2].
Crop diseases are creating problems for farmers due to low output and economic losses and industrial
agriculture[3]. So, it is need of the hour to detect such diseases as earliest as possible. Much research
is going on in this field using various techniques like image processing, machine learning and deep
learning. We have made a survey for disease detection based on these techniques and approaches to
different rice plant diseases. It is observed that deep learning is giving the best results as compared to
the other two methods[4]. This paper is divided into different sections: section 2 presents different
types of rice diseases along with symptoms, Section 3 describes the methodology for plant disease
detection. Section 4 depicts a comparative study among several related research works in rice disease
detection and finally, the paper is concluded in Section 5.
2. Types of Rice Diseases
Rice plant diseases can infect rice at all growth stages and at its all parts (leaf, neck and root).
These are mainly caused by bacteria, viruses, or fungi. Though there exist several rice plant diseases,
based on the survey some of the most prominent diseases affecting the rice plant are listed in table 1.
International Conference on Emerging Technologies: AI, IoT, and CPS for Science & Technology Applications, September 06–07, 2021,
NITTTR Chandigarh, India
EMAIL: madhuanand87@yahoo.com ; mehanvineet@gmail.com
ORCID: 0000-0003-2478-3532 ; 0000-0003-3483-0160
©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)
Table 1
Rice plant diseases and their symptoms
Disease Caused Image Symptoms
due to
Leaf Virus small black linear lesions on leaf blades, leaf
Smut tips may turn grey and dry[5].
Bacterial Bacteria elongated lesions near the leaf tips and
blight margins, and turns white to yellow and then
grey due to fungal attack [5].
Brown Fungi dark brown colored and round to oval-shaped
spot lesions on rice leaves[5].
Blast Fungi white to gray-green lesions or spots, with dark
green borders.
Older lesions on the leaves are elliptical shaped
and whitish to gray centers with red to
brownish or necrotic borders.
Sheath Fungi Greenish grey spots on the sheath of leaf either
Blight in oval or elliptical are formed irregularly. The
enlarged spots become grey combined with
white with an outline border in purple brown
or blackish brown can be seen[6].
Sheath Fungal The formation of small seized black lesions
Rot & bacterial found on the sheath of the outer leaf close to
pathogens the water line, which spread to the sheath of
inner leaf resulting in the rotting of tissues[6].
3. Methodology Used
The basic steps of the rice plant disease detection system include different modules such as image
acquisition, pre-processing, image segmentation, feature extraction, and classification.
Image Image Segmentati Feature Classifica
Acquisition pre- on Extraction tion
processing
Figure1: Methodology for disease detection in rice plants
3.1 Image Acquisition
In image processing, it is defined as the retrieval of an image from some source which can either
be manual capturing of images or some dataset. Most of the researchers have captured images using a
camera with high resolution in paddy fields and then resized the images into some definite number of
pixels[7]–[9]. On the other hand, the dataset named UC Irvine Machine Learning Repository [1] and
imageNet [10] is also used for image acquisition.
3.2 Image pre-processing
It includes resizing, cropping and removal of noise from the given image. The preprocessing step
aims to enhance some image features that are required for further processing. This step includes the
removal of some undesired features from the given image. For example, the background and an
irrelevant portion of the image is discarded to reduce image processing time[11].
3.3 Segmentation
Segmentation is an important step in object recognition tasks. It transforms images into a form
that is more meaningful and less complex to analyze. Here, an image can be divided into some
regions based on the desired feature[12]. Several segmentation techniques are the Otsu segmentation
method, K-Means Clustering, region segmentation, contours, etc.
3.4 Feature Extraction
The feature extraction process extorts the features from the segmented based on shapes, colors,
and textures[13]. Some shape-based features are area, axis, and angle[14].
3.5 Classification
Classification is an important module in plant disease detection systems. It is defined as a process
of categorizing plant leaf images based on identified diseases. There are two main classification
techniques namely supervised and unsupervised. In Supervised classification, we have pre-trained
data that helps to predict outcomes for some unforeseen data. The trained classifier is used to group
different pictures. The Unsupervised order utilizes the properties of the pixels to bunch them, these
gatherings known as a group, and process called clustering[15]. Some supervised classification
algorithms like Logistic Regression, K-Nearest Neighbor, Decision Tree, Naive Bayes were applied
for classification[16]. Also, the artificial neural network is one of the emerging methods of
classification.
4. Comparative Analysis
In this section, the most recent proposed solutions that are performing best for different types of
disease identification of paddy crops are presented along with their performance measure (Table II).
Most researchers have identified four major diseases of rice plants: blast, bacterial blight, spot, and
leaf smut. Different segmentation and classification techniques are used for detecting these diseases.
K-means clustering and Otsu’s method are giving significant results for segmentation. Further, it is
observed that deep neural network and decision tree classifiers are giving the highest accuracy of
>=97% for identifying rice plant diseases. Several researches have been done to find the optimum
solution to identify most prominent diseases in the rice plant[17].
Table 2
Comparative study of methodologies applied on rice plant diseases
Author & Aim Dataset/ Methodology used Diseases Accuracy
year Images detected
Suresha M Recognition of 330 images: Global threshold Blast and 76.59%
et.al(2017) Diseases in out of these method and kNN Brown Spot
Paddy Leaves 60% of classifier to classify diseases.
Using kNN images are data
Classifier used for
training and
40% of
images are
used for
testing.
S.Ramesh Rice Blast 300 images K-means clustering for Blast 90%
et al. Disease segmentation and ANN
(2018) Detection and for classification
Classification
using ML
Taohidul A faster 60 images RGB for segmentation brown spot, Blast: Above
Islam et al. technique on and Gaussian Naive bacterial 89%
(2018) rice disease Bayes for classification blight, and Bacterial
detection using blast Blight &
image Brown Spot:
processing of Above 90%
affected area in
agro-field
Kawcher Rice Leaf Training and KNNJ48 (DecisionTree), leaf smut, Decision
Ahmed et Disease Test dataset Naive Bayes and bacterial tree
al. (2019) Detection contains 432 Logistic Regression. leaf blight algorithm:
Using Machine and 48 Decision tree and brown 97.9%
Learning instances algorithm, after 10-fold spot
respectively cross validation
S.Ramesh classification of 450 images deep neural network Bacterial 97%
et al. paddy leaf for training with Jaya algorithm leaf blight,
(2019) diseases using and 128 for Brown spot,
optimized deep testing blast &
neural network sheath rot
with jaya
algorithm
Minu Eliz Detection of UC Irvine Otsu's method for Bacterial 94.6%
Pothen et Rice Leaf Machine segmentation and SVM leaf blight,
al. (2020) Diseases Using Learning for classification Leaf smut
Image Repository and Brown
Processing spot
Md. An Automated 984 images CNN leaf smut, Inception-
Ashiqul Convolutional leaf blast, ResNet-V2 is
Islam et al. Neural bacterial 92.68%.
(2021) Network Based leaf blight,
Approach for and brown
Paddy Leaf spot
Disease
Detection
5. Conclusion & Future scope
Rice plant diseases can reduce the production of the crop. So, it is the need of the hour to find an
optimum solution for this problem. Different techniques are applied on diseased rice images so that
further research can be made in this area to improve the overall performance of the rice disease
detection system. This paper reviewed and summarized techniques of image processing, machine
learning and deep learning that have been used in disease identification.
It is found that extraction of the affected region from the leaf image is the utmost important step,
for which we have studied different segmentation techniques. A comparison between different
methodologies for rice disease detection has been made and it can be concluded that deep neural
network and decision tree classifiers are giving highest accuracy of >=97% for identifying diseases in
rice crop. Still there is a need to work to identify more rice plant diseases other than the four major
diseases which are discussed in this paper.
6. References
[1] M. E. Pothen and D. M. L. Pai, “Detection of Rice Leaf Diseases Using Image Processing,”
Proc. 4th Int. Conf. Comput. Methodol. Commun. ICCMC 2020, no. Iccmc, pp. 424–430,
2020, doi: 10.1109/ICCMC48092.2020.ICCMC-00080.
[2] S. Kaur, S. Pandey, and S. Goel, “Plants Disease Identification and Classification Through
Leaf Images: A Survey,” Arch. Comput. Methods Eng., vol. 26, no. 2, pp. 507–530, 2019, doi:
10.1007/s11831-018-9255-6.
[3] R. Sujatha, J. M. Chatterjee, N. Jhanjhi, and S. N. Brohi, “Performance of deep learning vs
machine learning in plant leaf disease detection,” Microprocess. Microsyst., vol. 80, p.
103615, Feb. 2021, doi: 10.1016/j.micpro.2020.103615.
[4] K. P. Ferentinos, “Deep learning models for plant disease detection and diagnosis,” Comput.
Electron. Agric., vol. 145, no. September 2017, pp. 311–318, 2018, doi:
10.1016/j.compag.2018.01.009.
[5] K. Ahmed, T. R. Shahidi, S. M. Irfanul Alam, and S. Momen, “Rice leaf disease detection
using machine learning techniques,” 2019 Int. Conf. Sustain. Technol. Ind. 4.0, STI 2019, vol.
0, pp. 1–5, 2019, doi: 10.1109/STI47673.2019.9068096.
[6] S. V. Vasantha, B. Kiranmai, and S. R. Krishna, “Techniques for Rice Leaf Disease Detection
using Machine LearningAlgorithms,” vol. 9, no. 8, pp. 162–166, 2021.
[7] M. Suresha, K. N. Shreekanth, and B. V. Thirumalesh, “Recognition of diseases in paddy
leaves using knn classifier,” 2017 2nd Int. Conf. Converg. Technol. I2CT 2017, vol. 2017-
Janua, pp. 663–666, 2017, doi: 10.1109/I2CT.2017.8226213.
[8] S. Ramesh and D. Vydeki, “Recognition and classification of paddy leaf diseases using
Optimized Deep Neural network with Jaya algorithm,” Inf. Process. Agric., vol. 7, no. 2, pp.
249–260, 2020, doi: 10.1016/j.inpa.2019.09.002.
[9] S. Ramesh and D. Vydeki, “Rice blast disease detection and classification using machine
learning algorithm,” Proc. - 2nd Int. Conf. Micro-Electronics Telecommun. Eng. ICMETE
2018, pp. 255–259, 2018, doi: 10.1109/ICMETE.2018.00063.
[10] A. Islam, N. Rahman Shuvo, M. Shamsojjaman, S. Hasan, S. Hossain, and T. Khatun, “An
Automated Convolutional Neural Network Based Approach for Paddy Leaf Disease
Detection,” Int. J. Adv. Comput. Sci. Appl., vol. 12, no. 1, pp. 280–288, 2021, doi:
10.14569/IJACSA.2021.0120134.
[11] T. Islam, M. Sah, S. Baral, and R. Roychoudhury, “A Faster Technique on Rice Disease
Detectionusing Image Processing of Affected Area in Agro-Field,” Proc. Int. Conf. Inven.
Commun. Comput. Technol. ICICCT 2018, no. Icicct, pp. 62–66, 2018, doi:
10.1109/ICICCT.2018.8473322.
[12] V. Singh and A. K. Misra, “Detection of plant leaf diseases using image segmentation and soft
computing techniques,” Inf. Process. Agric., vol. 4, no. 1, pp. 41–49, 2017, doi:
10.1016/j.inpa.2016.10.005.
[13] T. Daniya and S. Vigneshwari, “A review on machine learning techniques for rice plant
disease detection in agricultural research,” Int. J. Adv. Sci. Technol., vol. 28, no. 13, pp. 49–62,
2019.
[14] T. Bera, A. Das, J. Sil, and A. K. Das, A survey on rice plant disease identification using
image processing and data mining techniques, vol. 814. Springer Singapore, 2019.
[15] P. K. Sethy, N. K. Barpanda, A. K. Rath, and S. K. Behera, “Image Processing Techniques for
Diagnosing Rice Plant Disease: A Survey,” Procedia Comput. Sci., vol. 167, no. 2019, pp.
516–530, 2020, doi: 10.1016/j.procs.2020.03.308.
[16] V. K. Shrivastava and M. K. Pradhan, “Rice plant disease classification using color features: a
machine learning paradigm,” J. Plant Pathol., vol. 103, no. 1, pp. 17–26, Feb. 2021, doi:
10.1007/s42161-020-00683-3.
[17] S. Phadikar, “Classification of Rice Leaf Diseases Based onMorphological Changes,” Int. J.
Inf. Electron. Eng., vol. 2, no. 3, pp. 460–463, 2012, doi: 10.7763/ijiee.2012.v2.137.