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Image-based deep learning automated sorting of date fruit

Nasiri, Amin, Taheri-Garavand, Amin, Zhang, Yu-Dong
Postharvest biology and technology 2019 v.153 pp. 133-141
cameras, computer vision, data collection, dates (fruit), engineering, lighting, mobile telephones, models, ripening
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.