CFP last date
20 January 2025
Reseach Article

Fruit Disease Categorization based on Color, Texture and Shape Features

by Ranjit K. N. Naveen C. Chethan H. K.
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 178 - Number 49
Year of Publication: 2019
Authors: Ranjit K. N. Naveen C. Chethan H. K.
10.5120/ijca2019919401

Ranjit K. N. Naveen C. Chethan H. K. . Fruit Disease Categorization based on Color, Texture and Shape Features. International Journal of Computer Applications. 178, 49 ( Sep 2019), 16-19. DOI=10.5120/ijca2019919401

@article{ 10.5120/ijca2019919401,
author = { Ranjit K. N. Naveen C. Chethan H. K. },
title = { Fruit Disease Categorization based on Color, Texture and Shape Features },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2019 },
volume = { 178 },
number = { 49 },
month = { Sep },
year = { 2019 },
issn = { 0975-8887 },
pages = { 16-19 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume178/number49/30883-2019919401/ },
doi = { 10.5120/ijca2019919401 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:54:06.071733+05:30
%A Ranjit K. N. Naveen C. Chethan H. K.
%T Fruit Disease Categorization based on Color, Texture and Shape Features
%J International Journal of Computer Applications
%@ 0975-8887
%V 178
%N 49
%P 16-19
%D 2019
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Nowadays digitization and automation of machine in agriculture field plays prominent role. In this paper, we have proposed method to classify fruit as diseased and non-diseased. Firstly, we used K means clustering method for segmentation of diseased regions. Later, we used to extract shape, color and texture features on segmented diseased regions. We have collected fruit diseased images from internet to create dataset and totally we have collect 2500 images from 10 fruit classes. We have conducted extensive experimentation using Artificial Neural Network and results shows that proposed method gives better performance compared to SVM and KNN.

References
  1. Safiri, S., M. Sani, et al., Fruit and Vegetable, Fat, and Sugar-Sweetened Beverage Intake Among Low-Income Mothers Living in Neighborhoods With Supplemental Nutrition Assistance ProgramEducation: Methodological Issues. Journal Of Nutrition Education And Behavior, 2017. 49(3): pp. 272-272
  2. Estevez-Santiago, R., B. Olmedilla-Alonso, et al., Lutein and zeaxanthin supplied by red/orange foods and fruits are more closely associated with macular pigment optical density than those from green vegetables in Spanish subjects. Nutrition Research, 2016. 36(11): pp. 1210-1221
  3. Seema, A. Kumar, et al. Automatic Fruit Grading and Classification System Using Computer Vision: A Review. in 2nd IEEE International Conference on Advances in Computing and Communication Engineering (ICACCE). 2015. Dehradun, INDIA: IEEE. pp. 598-603
  4. Lin, Y.F., M.Y. Chen, et al., DNP and ATP induced alteration in disease development of Phomopsis longanae Chi-inoculated longan fruit by acting on energy status and reactive oxygen species production-scavenging system. Food Chemistry, 2017. 228: pp. 497-505
  5. Erdenee, B., Ryutaro, T., Tana, G., 2010, Particular Agricultural Land CoverClassification Case Study Of Tsagaannuur, Mongolia. In: IEEE InternationalGeoscience & Remote Sensing Symposium, 3194-3197.
  6. Krishna, M., Jabert, G., 2013. Pest control in agriculture plantation using image processing. IOSR J. Electron. Commun. Eng. (IOSR-JECE) 6 (4), 68–74.
  7. Tewari, V.K., Arudra, A.K., Kumar, S.P., Pandey, V., Chandel, N.S., 2013. Estimation of plant nitrogen content using digital image processing. Int. Commission Agricu. Biosyst. Eng. 15 (2), 78–86.
  8. Patil, J.K., Kumar, R., 2011. Advances in image processing for detection of plant diseases. J. Adv. Bioinf. Appl. Res. ISSN 2 (2), 135–141.
  9. [Naik, S., Patel, B., 2017. A machine vision based fruit classification and grading: a review. Int. J. Comput. Appl. 170 (9), 22–34.
  10. Dubey, S.R., Jalal, A.S., 2015a. Application of image processing in fruits and vegetables analysis: a review. J. Intell. Syst. 24 (4), 405–424.
  11. Zhang, B., Huang, Z., Li, J., Zhao, C., Fan, S., Wu, J., Liu, C., 2014a. Principle, developments and applications of computer vision for external quality inspection of fruits and vegetables: a review. Food Res. Int., 326–343
  12. Wu, L., Classification of fruits using computer vision and a multiclass support vector machine. Sensors, 2012. 12(9): pp. 12489-12505
  13. Wang, S., Y. Zhang, et al., Fruit Classification by Wavelet-Entropy and Feedforward Neural Network Trained by Fitness-Scaled Chaotic ABC and Biogeography-Based Optimization. Entropy, 2015. 17(8): pp. 5711-5728
  14. Ji, G., Fruit classification using computer vision and feedforward neural network. Journal of Food Engineering, 2014. 143: pp. 167-177
  15. Wu, J., Fruit classification by biogeography-based optimization and feedforward neural network. Expert Systems, 2016. 33(3): pp. 239-253
  16. Wang, S., Y. Zhang, et al., Feed-forward neural network optimized by hybridization of PSO and ABC for abnormal brain detection. International Journal of Imaging Systems and Technology, 2015. 25(2): pp. 153-164
  17. Lu, S.Y., Z.H. Lu, et al. Fruit Classification by HPA-SLFN. in 8th International Conference on Wireless Communications & Signal Processing. 2016. Yangzhou, China: IEEE. pp. 11-17
  18. Kuang, H.L., L.L.H. Chan, et al., Fruit classification based on weighted score-level feature fusion. Journal of Electronic Imaging, 2016. 25(1), Article ID: 013009.
  19. [Garcia, F., J. Cervantes, et al., Fruit Classification by Extracting Color Chromaticity, Shape and Texture Features: Towards an Application for Supermarkets. IEEE Latin America Transactions, 2016. 14(7): pp. 3434-3443
  20. Zawbaa, H.M., M. Hazman, et al. Automatic fruit classification using random forest algorithm. in 14th International Conference on Hybrid Intelligent Systems (HIS). 2014. Kuwait, KUWAIT: IEEE. pp. 164-168
  21. Rocha, A., D.C. Hauagge, et al., Automatic fruit and vegetable classification from images. Computers and Electronics in Agriculture, 2010. 70(1): pp. 96-104
  22. Samajpati, B.J. and S.D. Degadwala. Hybrid Approach for Apple Fruit Diseases Detection and Classification Using Random Forest Classifier. in International Conference on Communication and Signal Processing (ICCSP). 2016. Melmaruvathur, INDIA: IEEE. pp. 1015-1019.
  23. Capizzi, G., G. Lo Sciuto, et al. Automatic Classification of Fruit Defects based on CoOccurrence Matrix and Neural Networks. in Federated Conference on Computer Science and Information Systems. 2015. New York: IEEE. pp. 861-867
Index Terms

Computer Science
Information Sciences

Keywords

Fruit Disease Color Shape Texture Categorization.