Economy highly depends on
Increasing amount the growth of crops need automatic monitoring disease. Detecting
disease from the images of the rice crops is one of the interesting research
areas. This survey presents different image processing techniques used in
detection of rice crops images of different techniques but also discuss
concepts of image processing applied to rice plant disease detection and
classification. Its include size of image dataset no. of diseases, segmentation
techniques, pre-processing, accuracy of classifiers etc. We utilize our survey
to design our work on detection and classification of rice plant diseases.
Image processing, classification; clustering, disease classification, disease
Most of the indian peoples income is depend on
farming. Rice is the most cultivated food all over the world. The losses of
crops it brings indian economy decrease on agricultural field because 70% of
the indian population depends on producing crops. Rice disease destroys 10 to
15% of production in Asia. Fungus, bacteria and viruses are responsible for
disease in the plant, so monitoring of disease on plant an important role in
the successful cultivation. Different disease that occur on rice plants are
leaf blast, brown spot, sheath blight and leaf scald. This survey
focuses on how image processing is utilized in detection of diseases in rice
plants. Disease identification. The rice crop diseases are discussed in detail.
1- Leaf Blast Disease: A region varying from
small round, dark spot to oval spots with narrow reddish-brown margins and gray
or white centre.
2- Brown Spots Disease: Round to oval shape with dark brown lesions. its occurs on leaves of the rice plant.
3- Bacterial Blight Disease: Lesions consist of elongated lesions near the
leaf tip. turn white to yellow and then gray due to saprophytic fungi.
4- Sheath Blight Disease: Lesions consist of alternating wide band of
white, reddish-brown or brown. Fungal survival structures called sclerotia may
from on the leaf surface. under favourable conditions, bird nest area of dead
tissue may form.
5- Sheath Rot Disease: General reddish-brown discoloration of flag leaf
sheath, panicles emerging poorly; white frosting of conidia on inside of leaf
sheath, florets discolored a uniform reddish-brown or dark brown.
6- Node Blast Disease: Clum mode turns black and gray as plants approach
maturity; nodes turn dark to blue-gray.
Brown Leaf Spot
Fig. 1. Different Types
of Rice Leaf Diseases
BY PREVIOUS RESEARCHER
Here we describe different works that
already done by researchers in different fields such as leaf disease
classification, classification and segmentation of rice leaf. so aim of the
survey used all these control methods for a good harvesting rice plantation.
Suraksha I.S., et al,” Disease Prediction of
Paddy Crops Using Data Mining and Image Processing Techniques,” IJAREEIE,
Vol. 5, Issue 5, ISSN: 2320-3765 (2011).
First, the input is digital a colour image of paddy
disease leaf. Then a method of mathematics morphology is used to segment these
images. Erosion method has been used to removes small-scale details from a
binary images but simultaneously reduces the size of regions of interest. The
dilation is one of the basic operations in mathematical morphology. The
dilation operation usually uses a mesh for expanding the shapes contained in
the input image.
Santanu Phadikar, et al, ” Rice diseases
classification using feature selection and rule generation techniques,”
Computers and Electronics in Agriculture (2012).
Proposed a method classifying diseases of
the rice plant. In their approach, fermi energy based region extraction method
is applied to overcome the limitation of selecting the proper threshold value.
To identify the shape of the infected region, GA is applied that best
approximates the structure of the region. The position of infection is
determined by partitioning the spot into different blocks and arranged as a
quadtree at different lables. The binary representation of each block reduces
computational complexity reasonably. Using rough set concept features are
selected by generating all reduces which minimize loss of information. From the
reduced dataset a set of classification rules is derived using a novel
classification rule mining technique. The advantage of the proposed method is
that it does not require any gain calculation of the rules and so involves
lesser computational complexity.
S. Phadikar, et al,”
Classification of Rice Leaf Diseases Based on Morphological Changes,”
International Journal of Information and Electronics Engineering (2012).
Proposed a method classify
the leaf brown spot and leaf blast diseases based on the morphological changes
caused by diseases. Here used SVM classifier. SVM and Bayes’ is applied best
approximates of the region. Colour distortion of leafs occur in mess
classification. Bayes’ classifier used in time complexity.
Radhika Deshmukh, et al,” Detection of Paddy Leaf Diseases,” International Conference
on Advances in Science and Technology (2015).
prototype for detection of paddy leaf disease using K-means clustering based
segmentation and neural network classifier to detect as well as classify the
disease affected leaf of paddy crop. The main purpose is the accurate and fast
detection of leaf disease. They test programs such as paddy blast, brown spot
paddy spot, and normal paddy, The proposed approach is image processing based.
They use a set of paddy leaf images as a dataset. Due to this experiment, the
paddy disease can be identified at the initial stage. This eliminates the
subjectivity of traditional methods and human induced errors.
Nikita Rishi, et al,” An Overview on
Detection and Classification of Plant Diseases in Image Processing,” International Journal of Scientific Engineering
and Research (2015)
Here discussed various
method and techniques; images cropping, compression, Otsu method; to detect the
diseases in the heterogeneous plant. They make use of neural networks
classifiers such as BPNN, RBF, GRNN and PNNs to diagnose wheat diseases. Canny
filters and feature extraction applied to recognize the diseases on cotton and
Detection Of Plant Leaf Disease Using Image Processing Technique,”
International Journal of Technology Enhancement and Emerging Engineering
Proposed the method to
analyse leaf diseases using different Image Processing technique. First, using
k-Means clustering to easily detection the disease. second, using GLCM for
feature extraction which is more efficient to extract the features. Third, the
result using SVM used for machine learning technique used for classification.
It was implemented for linear separation. Result from the SVM able to predict
the images accrately.
K. Jagan Mohan, et
al,” Detection and Recognition of Diseases from Paddy Plant Leaf Images,”
International Journal of Computer Application (2016)
Proposed Scale Invariant
Feature Transform(SIFT) is used to get features from the disease affected images.
Then these features are taken to recognize the image using Support Vector
Machine (SVM) and K-Nearest Neighbours. This work mainly concentrates on three
main diseases of paddy plant namely brown spot, leaf blast and bacterial
blight. It is useful to farmers. Experimental result showed that SVM and K-NN
is capable of predicting disease accuracy of 91.10% in SVM and 93.33% in K-NN.
Gayathri et al,” Effective Disease Detection for Plants”
International Journal of Advanced Research Methodology in Engineering & Technology
This survey concentrate on the image processing
techniques used to enhance the quality of the image and neural network
technique to classify the banana disease. The methodology involves image
acquisition, pre-processing and segmentation, analysis and classification of
the disease. All the banana sample will be passing through the RGB calculation
before it proceed to the binary conversion. If the range of normal Banana RGB,
then it is automatically, classify as type 4 which is Normal. Then, all the
segmented Banana disease sample will be convert into the binary data for
classification training and testing. Consequently, by employing the neural
network technique, the Banana diseases are recognized about 92.5 percent
accuracy rates. This prototype has a very great potential to be further
improved in the future to detect the plant related issues in the field of
agricultural analysis. This survey gradually decrease the effects of disease in
plants and the plants can be easily monitored via camera at less expenditure. Hence,
more plants were saved by the advent of the project 12.