人工智能
卷积神经网络
计算机科学
辍学(神经网络)
联营
规范化(社会学)
深度学习
模式识别(心理学)
特征提取
上下文图像分类
特征(语言学)
特征工程
计算机视觉
图像(数学)
机器学习
语言学
哲学
社会学
人类学
作者
Amin Nasiri,Amin Taheri‐Garavand,Yudong Zhang
标识
DOI:10.1016/j.postharvbio.2019.04.003
摘要
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.
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