Journal
POSTHARVEST BIOLOGY AND TECHNOLOGY
Volume 153, Issue -, Pages 133-141Publisher
ELSEVIER
DOI: 10.1016/j.postharvbio.2019.04.003
Keywords
Date fruit; Classification; Maturity stages; Defective date; Deep learning; Convolutional neural network
Categories
Ask authors/readers for more resources
Deep Convolutional Neural Network (CNN) with a unique structure for combining the feature extraction and classification stages, has been considered to be a state-of-the-art computer vision technique for classification tasks. This study presents a novel and accurate method for discriminating healthy date fruit (cv. Shahani), from defective ones. Furthermore, owing to the use of deep CNN, this method is able to predict the ripening stage of the healthy dates. The proposed CNN model was constructed from VGG-16 architecture which was followed by max-pooling, dropout, batch normalization, and dense layers. This model was trained and tested on an image dataset containing four classes, namely Khalal, Rutab, Tamar, and defective date. This dataset was collected by a smartphone under uncontrolled conditions with respect to illumination and camera parameters such as focus and camera stabilization. The CNN model was able to achieve an overall classification accuracy of 96.98%. The experimental results on the suggested model demonstrated that the CNN model outperforms the traditional classification methods that rely on feature engineering for discrimination of date fruit images.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available