NDLI: Efficient image classification technique for weather degraded fruit images

Content Provider	IET Digital Library
Author	Gill, Harmandeep Singh Khehra, Baljit Singh
Abstract	Fruit image classification is an ill-posed problem. Many machine learning techniques have been developed until now to improve the classification problem of fruit images. However, the performance of these techniques depends upon the quality of acquired fruit images. Thus, the performance of competitive fruit classification techniques reduces for images captured under poor environmental conditions, such as haze, fog, smog etc. To overcome this issue, type-II fuzzy-based fruit image improvement approach is employed to improve the visibility of weather degraded fruit images. After that, fruit images will be classified using an integrated classification model. The integrated model combines two well-known models (i.e. CNN and RNN). CNN is utilised to evaluate the discriminative features of fruit images. RNN is utilised to asses sequential labels. Extensive analysis shows that the proposed integrated classification model outperforms competitive fruit image classification techniques in terms of accuracy and coefficient of correlation.
Starting Page	3463
Ending Page	3470
Page Count	8
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 14, Dec (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/14
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2018.5310
Journal	IET Image Processing
Publisher Date	2020-09-08
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Acquired Fruit Image Agricultural Products Agriculture Combinatorial Mathematics Competitive Fruit Classification Technique Competitive Fruit Image Classification Technique Computer Vision And Image Processing Technique Efficient Image Classification Technique Feature Extraction Fuzzy Set Theory Image Classification Integrated Classification Model Knowledge Engineering Technique Learning in AI Optical, Image And Video Signal Processing Pattern Classification Product And Commodities Statistics Type-II Fuzzy-based Fruit Image Improvement Approach
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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