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Disease Identification and Grading of Pomegranate Leaves Using Image Processing and Fuzzy Logic

  • Sanjeev S. Sannakki EMAIL logo , Vijay S. Rajpurohit , V. B. Nargund and R. Arunkumar
Published/Copyright: November 6, 2013
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International Journal of Food Engineering
From the journal International Journal of Food Engineering Volume 9 Issue 4

Abstract

Plant diseases cause major losses to several agricultural and horticultural crops around the World. Therefore, methods for proper diagnosis of diseases found in any parts of the plant body play a crucial role in disease management. In the past few decades, many methods and techniques of image processing and soft computing are applied on a number of plants to diagnose and treat variety of plant diseases. Hence, the present work is aimed to develop an automated system that results in three major outcomes for a leaf image. They are disease identification, disease grading and treatment advisory. The methodology begins with capturing of samples of healthy and diseased leaf images of Pomegranate plant. All the images are made to undergo pre-processing steps and different features are extracted and stored in the database. Analysis is done on the extracted features to determine those features that constitute a disease in the leaf. Later, a query image is taken and is tested to determine whether that image is healthy or diseased one. If the query image is found to be diseased, then the grade of the disease is determined. Finally, a treatment advisory module is built which ultimately helps agriculturists/farmers.

Keywords: plant pathology; color image segmentation; disease spots extraction; feature extraction; fuzzy logic; disease identification; disease grading; treatment advisory

Appendix

Annexure 1 Segmentation results for the query image. (a) LAB image, (b) Cluster index image, (c) Objects in Cluster 1, (d) Objects in Cluster 2, (e) Objects in Cluster 3, (f) Objects in Cluster 4, (g) Objects in Cluster 5, (h) Objects in Cluster 6, (i) Objects in Cluster 7, (j) Objects in Cluster 8, (k) Objects in Cluster 9, (l) Objects in Cluster 10.
Annexure 1

Segmentation results for the query image. (a) LAB image, (b) Cluster index image, (c) Objects in Cluster 1, (d) Objects in Cluster 2, (e) Objects in Cluster 3, (f) Objects in Cluster 4, (g) Objects in Cluster 5, (h) Objects in Cluster 6, (i) Objects in Cluster 7, (j) Objects in Cluster 8, (k) Objects in Cluster 9, (l) Objects in Cluster 10.

Annexure 2

Texture and color features for diseased samples

ContrastEnergyEntropyHueSaturationValue
0.02250.98020.3240.01110.02910.0085
0.12940.90921.20590.03510.1160.0362
0.02580.93380.76180.01910.06840.026
0.05470.92540.98680.02950.09440.0267
0.02530.98540.43510.01990.04880.0054
0.13540.92211.28580.06490.16520.0276
0.10610.9351.32260.07710.18520.0251
0.06320.96580.72010.03750.08970.0104
0.06160.96730.64950.03030.07480.013
0.01950.96930.51940.0170.05610.013
0.04140.93170.75940.02090.06690.0309
0.060.96150.57270.01820.05790.0156
0.15240.96490.84870.04840.1130.0115
0.14120.95941.04430.06070.15020.0145
0.01590.9420.69620.01640.06310.016
0.09640.9570.97190.04760.13340.0176
0.02560.96130.60880.01860.0610.015
0.00760.98480.29460.0090.02860.006
0.42240.812.13380.10350.24960.0719
0.02080.98460.39430.01670.04540.0056
0.07240.94130.83430.02750.08110.0246
0.16730.92191.41310.07670.18220.0283
0.14540.91051.1380.04510.11640.0362
0.13830.93781.15420.06510.15320.0183
0.13870.95060.85230.04810.1030.0166
0.01750.97670.38530.010.03580.0082
0.06370.94190.96810.03790.10420.0221
0.05390.88281.29670.04080.13750.0414
0.09220.94470.88510.03470.09250.0221
0.19090.94710.84510.03250.08910.0212
0.16220.89631.27320.05410.13330.0418
0.04310.97640.34610.01060.03250.0104
0.23040.92811.42570.08690.20130.0257
0.30040.83861.70210.04740.18310.0745
0.26170.86181.40620.05530.14540.0541
0.40260.84811.8520.05690.21180.0683
0.06980.94360.72670.02880.06870.0227
0.0460.96920.48740.01460.04430.013
0.43540.86441.82310.10010.23850.0407
0.0510.94860.87670.04020.09730.0131
Annexure 3

Texture and color features for healthy samples

ContrastEnergyEntropyHueSaturationValue
0.41010.32294.98160.17810.50730.3008
0.36530.32725.01080.17480.56060.3696
0.15230.37394.39420.14620.46830.2762
0.31840.35274.80840.17710.51440.3476
0.38390.43484.17040.15610.3980.3499
0.51850.46174.19950.18370.35420.2253
0.74480.34575.1180.22220.51250.2977
0.2960.43144.36990.19570.46110.2341
0.39780.37664.53070.18180.40860.3248
0.32240.5343.64960.16810.36130.1813
0.37720.4484.29540.17790.44350.2565
0.3530.27245.50370.19210.58160.442
0.41090.33685.10780.19820.50910.2737
0.31620.31255.34530.20850.55120.3664
0.35260.30115.38960.18730.61760.4157
0.28680.31715.05160.17380.56060.4258
0.32720.37424.60050.1840.51730.2952
0.38130.32615.04330.18550.5260.3664
0.32720.37424.60050.1840.51730.2952
0.38130.32615.04330.18550.5260.3664
0.17580.34944.78880.16860.51130.3867
0.37930.3165.15670.17980.51090.3909
0.20780.32994.90570.18970.4740.2368
0.22350.34324.78540.18370.47140.3823
0.28270.4144.52930.1950.46270.2107
0.20320.48943.57260.13480.38670.204
0.6750.32295.2030.20860.50770.3742
0.20140.43754.19010.15790.39220.3685
0.22680.38374.4480.17370.30180.2737
0.18460.35854.60210.17090.44830.4233
0.47570.54653.53610.16490.43940.1997
0.20210.41214.34330.17170.43950.2406
0.20920.3364.71280.16970.40690.4783
0.38470.34954.77320.16810.4620.419
0.35480.42094.38470.17650.42090.3435
0.17340.40184.27950.14960.49050.3111
0.20440.4144.08890.140.33940.3522
0.17630.33154.81280.17490.51540.4064
0.27250.42444.1140.14510.41550.3071
Annexure 4 Disease treatment advisory for bacterial blight disease. (a) Procurement of plants, (b) Before pruning, (c) At the time of pruning, (d) Immediately after pruning, (e) After every pruning, (f) From pruning at 10 days interval for 5 times, (g) Soon after antibiotic spray, (h) Sept–Oct month, (i) Throughout cropping period, (j) After the harvest.
Annexure 4

Disease treatment advisory for bacterial blight disease. (a) Procurement of plants, (b) Before pruning, (c) At the time of pruning, (d) Immediately after pruning, (e) After every pruning, (f) From pruning at 10 days interval for 5 times, (g) Soon after antibiotic spray, (h) Sept–Oct month, (i) Throughout cropping period, (j) After the harvest.

Acknowledgment

We express a graceful thanks to Visvesvaraya Technological University, Belgaum, India for funding and providing a platform to the proposed Research work.

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Published Online: 2013-11-06

©2013 by Walter de Gruyter Berlin / Boston

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https://doi.org/10.1515/ijfe-2012-0241
Keywords for this article
plant pathology; color image segmentation; disease spots extraction; feature extraction; fuzzy logic; disease identification; disease grading; treatment advisory

Articles in the same Issue

  1. Masthead
  2. Masthead
  4. Drying Kinetics, Biochemical and Functional Properties of Products in Convective Drying ofAnchovy (Engraulis anchoita) Fillets
  5. A Rheological Model for Cupuassu (Theobroma grandiflorum) Pulp at Different Concentrations and Temperatures
  6. Pear Drying: Thermodynamics Studies and Coefficients of Convective Heat and Mass Transfer
  7. Evaluation of Thin-Layer Drying Models and Artificial Neural Networks for Describing Drying Kinetics of Canola Seed in a Heat Pump Assisted Fluidized Bed Dryer
  8. Relating Rice Grain Quality to Conditions during Sun Drying
  9. Modeling of Basil Leaves Drying by GA–ANN
  10. Effect of Pulsed Vacuum Treatment on Mass Transfer and Mechanical Properties during Osmotic Dehydration of Pineapple Slices
  11. Raw Glycerol as Substrate for the Production of Yeast Biomass
  12. Effect of Aminoethoxyvinylglycine and Methyl Jasmonate on Individual Phenolics and Post-harvest Fruit Quality of Three Different Japanese Plums (Prunussalicina Lindell)
  13. Process Optimization for Foam Mat-Tray Drying of Passiflora edulis Flavicarpa Pulp and Characterization of the Dried Powder
  14. Evaluation of the Freezing and Thawing Cryoconcentration Process on Bioactive Compounds Present in Banana Juice from Three Different Cultivars
  15. Effects of Defatted Flaxseed Addition on Rheological Properties of Wheat Flour Slurry
  16. Disease Identification and Grading of Pomegranate Leaves Using Image Processing and Fuzzy Logic
  17. Calculation of the Effective Diffusion Coefficients in Drying of Chemical and Mechanical Pretreated Rosehip Fruits (Rosa eglanteria L.) with Selected Mass Transfer Models
  18. Assessment of the Physico-mechanical, Chemical and Colour Characteristics of Potatoes Depending on Tuber Size and Cultivar
  19. Moisture Sorption Characteristics of Dakuwa (Nigerian Cereal/Groundnut Snack)
  20. A New Alternative Real-Time Method to Monitoring Dough Behavior during Processing Using Wireless Sensor Technology
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