Image processing based technique for classification of fish quality after cypermethrine exposure
| Autor: | Malay Kishore Dutta, Kaushik Banerjee, Narayan Kamble, Biplab Sarkar, Navroj Minhas, Namita Sengar |
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| Rok vydání: | 2016 |
| Předmět: |
Pesticide residue
Computer science business.industry 05 social sciences Feature extraction Wavelet transform Pattern recognition Image processing 04 agricultural and veterinary sciences Filter (signal processing) Pesticide 040401 food science Support vector machine 0404 agricultural biotechnology Wavelet 0502 economics and business Artificial intelligence business 050203 business & management Food Science |
| Zdroj: | LWT - Food Science and Technology. 68:408-417 |
| ISSN: | 0023-6438 |
| DOI: | 10.1016/j.lwt.2015.11.059 |
| Popis: | The quality of fish is primarily dependent on its handling, processing, storage, exposure to contaminants and on climatic variability. Fishes nurtured at fresh and contaminated water exhibit marked differences in quality. Among different contaminants, pesticide is reported as a predominant non-specific menace to fish health and quality. Detection and identification of pesticide residues in fish is a challenging task and requires costly sophisticated instruments. This paper proposes an image processing based non-destructive technique for identifying quality differences between pesticide treated and untreated (control) fish. To evaluate the quality variability, rohu (Labeorohita) fishes were treated with mild dose of cypermethrin for seven days and bio-accumulation status was recorded through GC–MS at post-harvested condition followed by imaging at two days interval. Gill tissue was selected as focal tissue for image processing which was segmented and different features were extracted in wavelet domain using Haar filter. Features were selected up to the third level of decomposition in wavelet domain and analysed for discriminatory features. The discriminatory variations in the different features of images were related to the difference between pesticide treated and untreated fish using strategic image processing techniques. Supervised classification was performed on the extracted features using support vector machine (SVM) classifier. The experimental results indicate that the proposed method is efficient for identification of pesticide treated and untreated fish from the features of the images. The accuracy of identification is high and the computation time is faster enough to make this method efficient as a real time application. |
| Databáze: | OpenAIRE |
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