50220140501004 2

International Journal of Graphics and Multimedia (IJGM), ISSN 0976 – 6448(Print),
ISSN 0976 – 6456(Online) Volume 5, Issue 1, January-April (2014), © IAEME
36
IMAGE PROCESSING METHODOLOGIES FOR DISEASE
DETECTION AND RECOGNITION OF MANGO CROP: A SURVEY
Shivayogi B. Ullagaddi Dr. Vishwanadha Raju
Department of CSE Department of CSE
VTU Belgaum, Karnataka JNTUHCEJ, JNT University, Hyderabad
ABSTRACT
The advanced technology has created the new challenges in image processing to
handle complex images of agriculture or horticulture and its production. The disease
detection and diagnose from such images has become important research activity and is very
much useful in the development of several new systems. One such a application is ability to
identify disease affected area in captured images and diagnose it .such a system require an
automated method to detect and extract feature of lesion area prior to further image analysis.
In this paper, several challenges related to disease detection and recognition from complex
background images are discussed and comprehensive survey on various approaches for
disease identification from image is presented, and concludes with Future directions.
1. INTRODUCTION
The growth in the technologies has lead to the emergence of intelligent and automated
systems such as grading, and classification for agriculture products. These systems
advantages, flexibility and convenience of information technology within grasp of individual
and impact the quality of all aspects of life. The availability of such a systems and
advancement in the technologies has made hitherto unthinkable applications a reality. One
such an application is ability to detect and recognize the diseases on mango crop. As There is
no doubt that agriculture has been a key driving force of the economy, at all times. As the
development of agricultural technology advanced, the proportion of those depending on
farming declined the focus of agricultural research and development was mainly on
maximizing yields. When pests and diseases affect the crops, there will be a tremendous
decrease in production. When the production is good farmers are suffer while selling their
INTERNATIONAL JOURNAL OF GRAPHICS AND
MULTIMEDIA (IJGM)
ISSN 0976 - 6448 (Print)
ISSN 0976 -6456 (Online)
Volume 5, Issue 1, January - April 2014, pp. 36-45
© IAEME: www.iaeme.com/ijgm.asp
Journal Impact Factor (2014): 4.4531 (Calculated by GISI)
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IJGM
© I A E M E

37
production in market or to export due to quality and grade so there is a need to develop a
advanced techniques for diagnosis of diseases, classification and grading of mango crop.
Therefore identification or classification of mango plants, leaves, fruits and finding out the
pest or diseases, percentage of the pest or disease incidence , symptoms of the pest or disease
attack is an important and challenging task, machine based automatic detection of disease its
classification and diagnose plays a key role in successful cultivation, grading of and
marketing for crops.
2. CHALLENGES AND MOTIVATION
Mango is the popular delicious fruit and cash crop. When diseases affect the crop
there is significant decrease in the yield due to which Farmers suffer in selling their yield, this
problem motivated to develop the new techniques to detect and diagnose the diseases
affecting the mango crop and devise the expert system to prevent those. The main diseases of
mango crop and their symptoms are as below.
Powdery Mildew (Oidium mangiferae): Powdery mildew is one of the most serious diseases
of mango affecting almost all the varieties. The characteristic symptom of the disease is the
white superficial powdery fungal growth on leaves, stalk of panicles, flowers and young
fruits. The affected flowers and fruits drop pre-maturely reducing the crop load considerably
or might even prevent the fruit set.
Anthracnose (Colletotrichum gloeosporioides): It is of widespread occurrence in the field
and in storage. The disease causes serious losses to young shoots, flowers and fruits under
climatic conditions like high humidity, frequent rains and the temperature. The disease
produces leaf spot; blossom blight, withered tip, twig blight and fruit rot symptoms. Tender
shoots and foliage are easily affected which ultimately cause die back of young branches.
Older twigs may also be infected through wounds, which in severe cases may be fatal. Black
spots develop on panicles. Severe infection destroys the entire inflorescence resulting in
failure of fruit setting. Young infected fruits develop black spots, shrivel and drop off. Fruits
infected at mature stage carry the fungus into storage and cause considerable loss during
storage, transit and marketing.
Die Back (Botryodiplodia (Lasiodiplodia) theobromae): Die back is one of the serious
diseases of mango. The disease on the tree may be noticed at any time of the year but it is
most conspicuous during October-November. The disease is characterized by drying of twigs
and branches followed by complete defoliation, which gives the tree an appearance of
scorching by fire. Initially it is evident by discoloration and darkening of the bark. The dark
area advances and extends outward along the veins of leaves. The affected leaf turns brown
and its margins roll upwards. At this stage, the twig or branch dies, shrivels and leaf falls.
This may be accompanied by exudation of yellowish brown gum.
Phoma Blight (Phoma glomerata) : The symptoms of the disease are observed only on old
leaves. Initially, the lesions are angular, minute, irregular, yellow to light brown, scattered
over leaf lamina. As the lesions enlarge their color changes from brown to cinnamon and they
become almost irregular. In case of severe infection such spots coalesce forming patches
resulting in complete withering and defoliation of infected leaves.

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Bacterial Canker (Xanthomonas campestris pv. mangiferaeindicae) : Canker is a serious
disease in India. The disease causes fruit drop (10-70%), yield loss (10-85%) and storage rot
(5-100%). Many commercial cultivars of mango including Langra, Dashehari, Arnrapali,
Mallika and Totapuri are susceptible to this disease. The disease is found on leaves, petioles,
twigs, branches and fruits. The disease first appears as minute water soaked irregular lesions
on any part of leaf or leaf lamina. Several lesions coalesce to form irregular necrotic
cankerous patches. In severe infections the leaves turn yellow and drop off. Cankerous
lesions also appear on petioles, twigs and young fruits. The water soaked lesions also develop
on fruits which later turn dark brown to black. They often burst open, releasing highly
contagious gummy ooze containing bacterial cells.
Red Rust (Cepbaleuros viruses) : The disease attack causes reduction in photosynthetic
activity and defoliation of leaves thereby reducing the vitality of the host plant. The disease is
evident by the rusty red spots mainly on leaves and sometimes on petioles and bark of young
twigs. . The spots are greenish grey in colour and velvety in texture. Later, they turn reddish
brown. The circular and slightly elevated spots sometimes coalesce to form larger and
irregular spots. The affected portion of stem cracks. In case of severe infection, the bark
becomes thick, twigs get enlarged but remain stunted and the foliage finally dries up.
Sooty Mould (Meliola mangiferae) : The disease is common in the orchards where mealy
bug, scale insects and hoppers are not controlled efficiently. The disease in the field is
recognized by the presence of a black sooty mould on the leaf surface. In severe cases, the
trees turn completely black due to the presence of mould over the entire surface of twigs and
leaves. The severity of infection depends on the honey dew secretion of the above insects.
Honey dews secretions from insects stick to the leaf surface and provide necessary medium
for fungal growth. Although the fungus causes no direct damage, the photosynthetic activity
of the leaf is adversely affected.
Diplodia Stem-end Rot (Lasiodiplodia theobromae) : The fungus enters through
mechanically injured areas on the stem or skin. The fungus grows from the pedicel into a
circular black lesionaround the pedicel.
The digital images captured by cameras may contain complex background and significant
degradations; hence disease detection activity from such a images is difficult task and poses
several challenges as discussed below
Identification or recognition of mango trees or plants fruits and leaf.
Shape Recognition
Color classification
Texture classification
Mango fruit grading in market for export or to maintain quality
Size and color classification
Evaluation of aging of fruit
Considering temperature and humidity
Detection and diagnosis of diseases like powdering mildew, anthracnose and spongy
tissue
Early Disease detection in leaf, flower, fruit and stem
Quantifying affected area of disease

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Finding the shape of affected area
Determining the feature of affected area (color change)
Rate of growth of disease
Growth of crop after chemical spray etc.
3. METHODS FOR DISEASE RECOGNITION
A number of methods for disease identification and diagnoses have been published in
recent years and are categorized into various methods like, texture, neural network, fuzzy and
web based methods, the performance of methods found to be inefficient and expensive due to
several challenges. Hence techniques based on texture analysis have become good choice for
analyzing such images. The techniques based on Gabor filter, Wavelet, spatial variance, etc
can be used to detect properties in diseased area of image.
Automatic recognition of quarantine citrus diseases has been discussed in [1]. This work,
presents a model capable of automatic recognize the quarantine diseases. It is based on the
combination of a feature selection method and a classifier that has been trained on quarantine
illness symptoms. Citrus samples with citrus canker, black spot, scab and other diseases were
evaluated. Experimental work was performed on 212 samples of mandarins from a Nova
cultivar. The proposed approach achieved a classification rate of quarantine/not-quarantine
samples of over 83% for all classes, even when using a small subset (14) of all the available
features (90). The results obtained show that the proposed method can be suitable for helping
the task of citrus visual diagnosis, in particular, quarantine diseases recognition in fruits.
Methods for disease detection and recognition Detection of unhealthy region of plant
leaves and classification of plant leaf diseases using texture features is proposed in [2]. The
proposed system is a software solution for automatic detection and classification of plant leaf
diseases. The developed processing scheme consists of four main steps, first a color
transformation structure for the input RGB image is created, and then the green pixels are
masked and removed using specific threshold value followed by segmentation process, the
texture statistics are computed for the useful segments, finally the extracted features are
passed through the classifier. The proposed algorithm’s efficiency can successfully detect and
classify the examined diseases with an accuracy of 94%. Experimental results on a database
of about 500 plant leaves confirm the robustness of the proposed approach.
Grading and Classification of Anthracnose Fungal Disease of Fruits based on
Statistical Texture Features is described in [3]. They have considered three types of fruit
namely mango, grape and pomegranate for their work. The developed processing scheme
consists of two phases. In the first phase, segmentation techniques namely thresholding,
region growing, K-means clustering and watershed are employed for separating anthracnose
affected lesion areas from normal area. Then these affected areas are graded by calculating
the percentage of affected area. In the second phase texture features are extracted using
Runlength Matrix. These features are then used for classification purpose using ANN
classifier. We have conducted experimentation on a dataset of 600 fruits’ image samples. The
classification accuracies for normal and affected anthracnose fruit types are 84.65% and
76.6% respectively.
A web-based tool for visual plant disease identification, a proof of concept with a case
study on strawberry is given in [4]. The system is based on a multi-access key of
identification and specifically on the selection of pictures by the user and can be used
remotely from a desktop as well as from a smart phone or personal digital assistant. The

40
system was developed following a simple approach: visual identification where images
and/or short descriptions are used to uniquely identify diseases when possible and suggest
refining the visual identification process in cases of ambiguous identification. It has been
designed in a way that allows easy definition of additional diseases by uploading the correct
images and defining the identification rules and diseases. The system may aid growers in
identifying various diseases when using the system remotely while the system is developed
and maintained centrally. The system is tested for visual identification of strawberry diseases
using a computer and samples of infected plants. The evaluation showed that it is effective
and accurate in enabling its users to identify strawberry diseases.
An intelligent system for the assessment of crop disorders has been discussed in [5].
The paper reported Isacrodi, a web based system designed to assist farmers in assessing
disorders in their crops, and in protecting their crops. it uses a controlled vocabulary to
describe key components such as crops and crop disorders. Crop disorders are described in
the form of CDRs which can be labelled if the disorder has been determined by an expert, or
unlabelled if the disorder is unidentified. Isacrodi uses a diagnosis provider which based on a
multi-class support vector machine. They evaluated the performance of the SVM diagnosis
provider with CDR data generated by a random process that that captures key features of the
envisaged usage of the Isacrodi system. The results of the test showed to that even in
favourable conditions, diagnoses are not perfectly accurate. However, even where the
diagnosed disorder, i.e. the disorder withthe highest score, was not correct, the second or
third highest scoring disorder could be the correct diagnosis.
Plant species identification using digital marphometric is described in [6]. They
reviewed the main computational, morphometric and image processing methods that have
been used in recent years to analyze images of plants, introducing readers to relevant
botanical concepts along the way. it discuss the measurement of leaf outlines, flower shape,
vein structures and leaf textures, and describe a wide range of analytical methods in use. also
discussed a number of systems that apply this research, including prototypes of hand-held
digital field guides and various robotic systems used in agriculture.
Rice diseases classification using feature selection and rule generation techniques is
given in [7]. This paper aims at classifying different types of rice diseases by extracting
features from the infected regions of the rice plant images. Fermi energy based segmentation
method has been proposed in the paper to isolate the infected region of the image from its
background, symptoms of the diseases are characterized using features like color, shape and
position of the infected portion and extracted by developing novel algorithms. To reduce
complexity of the classifier, important features are selected using rough set theory (RST) to
minimize the loss of information. Finally using selected features, a rule base classifier has
been built that cover all the diseased rice plant images and provides superior result compare
to traditional classifiers.
Applying image processing technique to detect plant diseases is described in [8]. The
work proposes a methodology for detecting plant diseases early and accurately, using image
processing techniques and artificial neural network (ANN). The presented work is aimed to
develop a simple disease detection system for plant diseases. The work begins with capturing
the images. Filtered and segmented using Gabor filter. Then, texture and color features are
extracted from the result of segmentation and Artificial neural network (ANN ) is then trained
by choosing the feature values that could distinguish the healthy and diseased samples
appropriately. Experimental results showed that classification performance by ANN taking
feature set is better with an accuracy of 91%.

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Infection Analysis Using Color Feature Texture Using Image processing is described
in [9]. In this paper, a new approach is used to automatically detect the infected
pomegranates. Color texture feature analysis is used for detection of surface defects on
pomegranates. Acquired image is initially cropped and then transformed into HSI color
space, which is further used for generating SGDM matrix. Total 18 texture features were
computed for hue (H), saturation (S) and intensity (I) images from each cropped samples.
Best features were used as an input to Support Vector Machine (SVM) classifier and tests
were performed to identify best classification model. Out of selected texture features, features
showing optimal results were cluster shade (99.8835%), product moment (99.8835%) and
mean intensity (99.8059%).
Investigation and monitoring for leaves disease detection and evaluation using image
processing is proposed in [10]. In this, system identifies leaves disease of plants and also
determines the stage in which the disease is. The system has various image processing
techniques. At first, the images are captured and processed for enhancement. Then image
segmentation is carried out to get disease regions. Later, image features such as shape, color
and texture are extracted for the disease regions. These resultant features are given as input to
disease classifier to appropriately identify and grade the diseases.
Classification of Rice Leaf Diseases Based on Morphological Changes is reported in
[11]. In this work, an automated system has been developed to classify the leaf brown spot
and the leaf blast diseases of rice plant based on the morphological changes of the plants
caused by the diseases. Radial distribution of the hue from the center to the boundary of the
spot images has been used as features to classify the diseases by Bayes’ and SVM Classifier.
The system has been validated using 1000 test spot images of infected rice leaves collected
from the field, gives 79.5% and 68.1% accuracies for Bayes’ and SVM Classifier based
system respectively.
A hybrid intelligent system for automated pomegranate disease detection and grading
is described in [12]. This paper proposes the systemthat encompasses various image
processing and soft computing techniques. The methodology begins with image acquisition.
Captured images are enhanced and segmented with appropriate algorithms. Further, feature
extraction is carried out and selected features are used as input to the disease classifier which
appropriately identifies and grades the disease. Once the disease and its stage are identified
accurately, a proper disease treatment advisory can be provided.
Remote Area Plant Disease Detection Using Image Processing is reported in [13].
They propose color and texture features are used to recognize and classify different
agriculture/horticulture produce into normal and affected regions. The combinations of
features prove to be very effective in disease detection. The experimental results indicate that
proposed approach significantly enhances accuracy in automatic detection of normal and
affected produce. This paper presents an effective method for detection of diseases in Malus
Domestica using methods like K-means clustering, color and texture analysis.
Image Processing Techniques for Diagnosing Paddy Disease is described in [14]. This
paper concentrates on extracting paddy features through off-line image. The methodology
involves image acquisition, converting the RGB images into a binary image using
thresholding based on local entropy threshold and Otsu method. A morphological algorithm
is used to remove noises by using region filling technique. Then, the image characteristics
consisting of lesion type, boundary color, spot color and broken paddy leaf color are
extracted from paddy leaf images. Consequently, by employing production rule technique,
the paddy diseases are recognized about 94.7 percent of accuracy rates.

42
Early Pest Identification in Greenhouse Crops using Image Processing Techniques is
given in [15]. The paper describes a software prototype system for pest detection on the
infected images of different leaves. Images of the infected leaf are captured by digital camera
and processed using image growing, image segmentation techniques to detect infected parts
of the particular plants. Then the detected part is been processed for further feature extraction
which gives general idea about pests. This proposes automatic detection and calculating area
of infection on leaves of a whitefly (Trialeurodes vaporariorum Westwood) at a mature
stage.
Scab Diseases Detection of Potato using Image Processing is described in [16]. This
paper proposes image processing methodology to detect scab disease of potato. In this paper
first, the captured images are collected from different potato field and are processed for
enhancement. Then image segmentation is carried out to get target regions (disease spots).
Finally, analysis of the target regions (disease spots) based on histogram approach to finding
the phase of the disease and then the treatment consultative module can be prepared by on the
lookout for agricultural experts.
Leaf Disease Severity Measurement Using Image Processing is given in [17]. This
describes Disease symptoms of the plant vary significantly under the different stages of the
disease so to the accuracy with which the severity of the disease measured is depends upon
segmentation of the image. Simple threshold segmentation is used to calculate the leaf area
but this method is not suitable to calculate the area of the lesion region because of varying
characteristics of the lesion region. Triangle method of the thresholding used here to segment
the lesion region. The average accuracy of the experiment is 98.60 %. So the image
processing technology to measure plant disease severity is convenient and accurate.
Classification of Herbs Plant Diseases via Hierarchical Dynamic Artificial Neural
Network after Image Removal Using Kernel Regression Framework is described in [18]. In
this work, image processing and pattern classification are going to be used to implement a
machine vision system that could identify and classify the visual symptoms of herb plants
diseases. The image processing is divided into four stages: Image Pre-Processing to remove
image noises (Fixed-Valued Impulse Noise, Random-Valued Impulse Noise and Gaussian
Noise), Image segmentation to identify regions in the image that were likely to qualify as
diseased region, Image Feature Extraction and Selection to extract and select important image
features and Image Classification to classify the image into different herbs diseases classes.
This paper is to propose an unsupervised diseases pattern recognition and classification
algorithm that is based on a modified Hierarchical Dynamic Artificial Neural Network which
provides an adjustable sensitivity-specificity herbs diseases detection and classification from
the analysis of noise-free colored herbs images. It is also to proposed diseases treatment
algorithm that is capable to provide a suitable treatment and control for each identified herbs
diseases.
Fast and Accurate Detection and Classification of Plant Diseases is given in [19]. In
this paper, the applications of K-means clustering and Neural Networks (NNs) have been
formulated for clustering and classification of diseases that affect on plant leaves.
Recognizing the disease is mainly the purpose of the proposed approach. The proposed
Algorithm was tested on five diseases which influence on the plants; they are: Early scorch,
Cottony mold, ashen mold, late scorch, tiny whiteness. The experimental results indicate that
the proposed approach is a valuable approach, which can significantly support an accurate
detection of leaf diseases in a little computational effort.

43
A Step towards Precision Farming of Rice Crop by Estimating Loss Caused by Leaf
Blast Disease Using Digital Image Processing and Fuzzy Clustering is proposed in [20]. This
paper describes the application of Fuzzy C-Mean Clustering algorithm to estimate the loss
caused by blast disease in rice crop. A digital image has been taken by digital camera of rice
crop, which is further analyzed by taking RGB feature of that image and then classified using
Fuzzy C Mean Clustering algorithm. That clustered information can beused for precision
farming by farmer for decision support system.
Image pattern classification for the identification of disease causing agents in plants is
described in [21]. This paper reports a machine vision system for the identification of the
visual symptoms of plant diseases, from colored images. Diseased regions shown in digital
pictures of cotton crops were enhanced, segmented, and a set of features were extracted from
each of them. Features were then used as inputs to a Support Vector Machine (SVM)
classifier and tests were performed to identify the best classification model. They
hypothesized that given the characteristics of the images; there should be a subset of features
more informative of the image domain. To test this hypothesis, several classification models
were assessed via cross-validation. The results of this study suggested that: texture-related
features might be used as discriminators when the target images do not follow a well defined
color or shape domain pattern; and that machine vision systems might lead to the successful
discrimination of targets when fed with appropriate information.
Multiple Classifier Combination for Recognition of Wheat Leaf Diseases is given in
[22]. This paper proposes a new strategy of Multi-Classifier System based on SVM for
pattern recognition of wheat leaf diseases for higher recognition accuracy. Diseased leaf
samples with Powdery Mildew, Rust Puccinia Triticina, Leaf Blight, Puccinia Striiformis
were collected in the field and images were captured before a uniform black background.
Three feature sets including color feature set, shape feature set and texture feature set were
created for classification analysis. The proposed combination strategy was based on stacked
generalization and included two-level structure: base-level was a module of three kinds of
SVM-based classifiers trained by three feature sets and meta-level was one module of SVM-
based decision classifier trained by meta-feature set which are generated through a new data
fusion mechanism. Compared with other single classifiers and other strategy of classifier
ensembles for wheat leaf diseases, this approach is more flexible and has higher success rate
of recognition.
4. CONCLUSION
The widespread availability of camera and camera embedded devices has created the
new challenges in image processing to handle images captured through such devices. And
process of detection and diagnose of disease from image has been an ongoing research area
useful in development of several applications such as automated grading system,
classification of product and diagnose and expert system of disease etc. one such a
application which can help formers to know about diseases affecting their yield and prevent
with expert suggestions. Such a systems require an automated method to extract diseased
portion of plant, leaf, flower or fruit prior to further image analysis. Hence in this paper, a
comprehensive survey of several image processing methods is presented. Despite of several
techniques available for disease diagnose of plant/crop, more scope exists to develop
computationally inexpensive, robust and high detection and recognition rate techniques. And

44
also scope exists to further investigate use of wavelets, DCT and other transformation
techniques for developing new methods.
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