Efficient image classification technique for weather degraded fruit images

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.