Automatic Disease Detection In Citrus Trees Using Machine Vision

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Automatic Disease Detection In Citrus Trees Using
Machine Vision

• Rajesh Pydipati
• Research Assistant
• Agricultural Robotics Mechatronics Group (ARMg)
• Agricultural Biological Engineering

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Introduction

• Citrus industry is an important constituent of
  Floridas overall agricultural economy
• Florida is the worlds leading producing region
  for grapefruit and second only to Brazil in
  orange production
• The state produces over 80 percent of the United
  States supply of citrus

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Research Justification

• Citrus diseases cause economic loss in citrus
  production due to long term tree damage and due
  to fruit defects that reduce crop size, quality
  and marketability.
• Early detection systems that might detect and
  possibly treat citrus for observed diseases or
  nutrient deficiency could significantly reduce
  annual losses.

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Objectives

• Collect image data set of various common citrus
  diseases.
• Evaluate the Color Co-occurrence Method, for
  disease detection in citrus trees.
• Develop various strategies and algorithms for
  classification of the citrus leaves based on the
  features obtained from the color co-occurrence
  method.
• Compare the classification accuracies from the
  algorithms.

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Vision based classification
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Sample Collection and Image Acquisition

• Leaf sample sets were collected from a typical
  Florida grape fruit grove for three common citrus
  diseases and from normal leaves
• Specimens were separated according to
  classification in plastic ziploc bags and stored
  in a environmental chamber maintained at 10
  degrees centigrade
• Forty digital RGB format images were collected
  for each classification and stored to disk in
  uncompressed JPEG format. Alternating image
  selection was used to build the test and training
  data sets

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Leaf sample images
Greasy spot diseased leaf
Melanose diseased leaf
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Leaf sample images
Scab diseased leaf
Normal leaf
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Ambient vs Laboratory Conditions

• Initial tests were conducted in a laboratory to
  minimize uncertainty created by ambient lighting
  variation.
• An effort was made to select an artificial light
  source which would closely represent ambient
  light.
• Leaf samples were analyzed individually to
  identify variations between leaf fronts and backs.

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Spectrum Comparison with NaturaLight Filter
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Image Acquisition Specifications

• Four 16W Cool White Fluorescent bulbs (4500K)
  with NaturaLight filters and reflectors.
• JAI MV90, 3 CCD Color Camera with 28-90 mm Zoom
  lens.
• Coreco PC-RGB 24 bit color frame grabber with 480
  by 640 pixels.
• MV Tools Image capture software
• Matlab Image Processing Toolbox
• SAS Statistical Analysis Package

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Image Acquisition System
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Camera Calibration

• The camera was calibrated under the artificial
  light source using a calibration grey-card.
• An RGB digital image was taken of the grey-card
  and each color channel was evaluated using
  histograms, mean and standard deviation
  statistics.
• Red and green channel gains were adjusted until
  the grey-card images had similar means in R, G,
  and B equal to approximately 128, which is
  mid-range for a scale from 0 to 255. Standard
  deviation of calibrated pixel values were
  approximately equal to 3.0.

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Image acquisition and classification flow chart
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Color cooccurence method

• Color Co-occurrence Method (CCM) uses HSI pixel
  maps to generate three unique Spatial Gray-level
  Dependence Matrices (SGDM)
• Each sub-image was converted from RGB (red,
  green, blue) to HSI (hue, saturation, intensity)
  color format
• The SGDM is a measure of the probability that a
  given pixel at one particular gray-level will
  occur at a distinct distance and orientation
  angle from another pixel, given that pixel has a
  second particular gray-level

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CCM TEXTURE STATISTICS

• CCM texture statistics were generated from the
  SGDM of each HSI color feature.
• Each of the three matrices is evaluated by
  thirteen texture statistic measures resulting in
  39 texture features per image.
• CCM Texture statistics were used to build four
  data models. The data models used different
  combinations of the HSI color co-occurrence
  texture features. STEPDISC was used to reduce
  data models through a stepwise variable
  elimination procedure

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Intensity Texture Features

• I9 - Difference Entropy
• I10 - Information Correlation Measures
  1
• I11 - Information Correlation Measures 2
• I12 - Contrast
• I13 - Modus

• I1 - Uniformity
• I2 - Mean
• I3 - Variance
• I4 - Correlation
• I5 - Product Moment
• I6 - Inverse Difference
• I7 - Entropy
• I8 - Sum Entropy

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Classification Models
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Classifier based on Mahalanobis distance

• The Mahalanobis distance is a very useful way of
  determining the similarity of a set of values
  from an unknown sample to a set of values
  measured from a collection of known samples
• Mahalanobis distance method is very sensitive to
  inter-variable changes in the training data

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Mahalanobis distance contd..

• Mahalanobis distance is measured in terms of
  standard deviations from the mean of the training
  samples
• The reported matching values give a statistical
  measure of how well the spectrum of the unknown
  sample matches (or does not match) the original
  training spectra

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Formula for calculating the squared Mahalanobis
distance metric




x is the N-dimensional test feature vector (N
is the number of features ) µ is the
N-dimensional mean vector for a particular class
of leaves ? is the N x N dimensional
co-variance matrix for a particular class of
leaves.
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Minimum distance principle

• The squared Mahalanobis distance was calculated
  from a test image to various classes of leaves
• The minimum distance was used as the criterion to
  make classification decisions

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Neural networks

• A neural network is a system composed of many
  simple processing elements operating in parallel
  whose function is determined by network
  structure, connection strengths, and the
  processing performed at computing elements or
  nodes .
• (According to the DARPA Neural Network Study
  (1988, AFCEA International Press, p. 60)

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Contd..

• A neural network is a massively parallel
  distributed processor that has a natural
  propensity for storing experiential knowledge and
  making it available for use. It resembles the
  brain in two respects
• 1. Knowledge is acquired by the network
  through a learning process. 2. Inter-neuron
  connection strengths known as synaptic weights
  are used to store the knowledge.
• According to Haykin, S. (1994), Neural
  Networks A Comprehensive Foundation, NY
  Macmillan, p. 2

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A Basic Neuron
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Multilayer Feed forward Neural Network
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Back propagation

• In the MFNN shown earlier the input layer of the
  BP network is generally fully connected to all
  nodes in the following hidden layer
• Input is generally normalized to values between
  -1 and 1
• Each node in the hidden layer acts as a summing
  node for all inputs as well as an activation
  function

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MFNN with Back propagation

• The hidden layer neuron first sums all the
  connection inputs and then sends this result to
  the activation function for output generation.
• The outputs are propagated through all the layers
  until final output is obtained

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Mathematical equations

• The governing equations are given below
• Where x1,x2 are the input signals,
• w1,w2. the synaptic weights,
  
• u is the activation potential
  of the neuron,
• is the threshold,
• y is the output signal of the
  neuron,
• and f (.) is the activation
  function.

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Back propagation

• The Back propagation algorithm is the most
  important algorithm for the supervised training
  of multilayer feed-forward ANNs
• The BP algorithm was originally developed using
  the gradient descent algorithm to train multi
  layered neural networks for performing desired
  tasks

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Back propagation algorithm

• BP training process begins by selecting a set of
  training input vectors along with corresponding
  output vectors.
• The outputs of the intermediate stages are
  forward propagated until the output layer nodes
  are activated.
• Actual outputs are compared with target outputs
  using an error criterion.

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Back propagation

• The connection weights are updated using the
  gradient descent approach by back propagating
  change in the network weights from the output
  layer to the input layer.
• The net changes to the network will be
  accomplished at the end of one training cycle.

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BP network architecture used in the research
Network Architecture 2 hidden layers with 10
processing elements each Output layer consisting
of 4 output neurons An input layer Tansig
activation function used at all layers
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Radial basis function networks

• A radial basis function network is a neural
  network approached by viewing the design as a
  curve-fitting (approximation) problem in a high
  dimensional space
• Learning is equivalent to finding a
  multidimensional function that provides a best
  fit to the training data

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An RBF network
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RBF contd

• The RBF front layer is the input layer where the
  input vector is applied to the network
• The hidden layer consist of radial basis function
  neurons, which perform a fixed non-linear
  transformation mapping the input space into a new
  space
• The output layer serves as a linear combiner for
  the new space.

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RBF network used in the research
Network architecture 80 radial basis functions
in the hidden layer 2 outputs in the output
layer
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Data Preparation

• 40 Images each, of the four classes of leaves
  were taken.
• The Images were divided into training and test
  data sets sequentially for all the classes.
• The feature extraction was performed for all the
  images by following the CCM method.

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Data Preparation

• Finally the data was divided in to two text
  files
• 1)Training texture feature data ( with all
  39 texture features) and
• 2)Test texture feature data ( with all
  39 texture features)
• The files had 80 rows each, representing 20
  samples from each of the four classes of leaves
  as discussed earlier. Each row had 39 columns
  representing the 39 texture features extracted
  for a particular sample image

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Data preparation

• Each row had a unique number (1, 2, 3 or 4) which
  represented the class the particular row of data
  belonged
• These basic files were used to select the
  appropriate input for various data models based
  on SAS analysis.

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Experimental methods

• The training data was used for training the
  various classifiers as discussed in the earlier
  slides.
• Once training was complete the test data was used
  to test the classification accuracies.
• Results for various classifiers are given in the
  following slides.

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Results
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Results
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Results
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Results
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Comparison of various classifiers for Model 1B
Classifier Greasy spot Melanose Normal Scab Overall
SAS 100 100 90 95 96.3
Mahalanobis 100 100 100 95 98.75
NNBP 100 90 95 95 95
RBF 100 100 85 60 86.25
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Summary

• It is concluded that model 1B consisting of
  features from hue and saturation is the best
  model for the task of citrus leaf classification.
  
• Elimination of intensity in texture feature
  calculation is the major advantage. It nullifies
  the effect of lighting variations in an outdoor
  environment

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Conclusion

• The research was a feasibility analysis to see
  whether the techniques investigated in this
  research can be implemented in future real time
  applications.
• Results show a positive step in that direction.
  Nevertheless, the real time system involves some
  modifications and tradeoffs to make it practical
  for outdoor applications

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Thank You

• May I answer any questions?