作者
Aiman Al-Sabaawi, Reem Ibrahim Hasan, Mohammed A. Fadhel, Omran Al-Shamma, Laith Alzubaidi
发表日期
2020/12/15
研讨会论文
20th International Conference Intelligent Systems Design and Applications (ISDA)
出版商
Springer, Cham.
简介
Classification of dates in an orchard environment is a challenging task due to various texture, color, shape, and size properties. Moreover, the date has various data types that have almost the same appearance and makes classification much more difficult. To overcome these limitations, deep learning offers effective models that automatically extract features better than traditional machine learning techniques. Although deep learning models have shown excellent performance in several tasks, they require a large amount of training data to perform well. To address this issue, and to attain effective models to classify dates in an orchard environment, we employed pre-trained deep learning models. These models have been trained with a large amount of data and they showed outstanding performance in image classification. We have fine-tuned four pre-trained models; GoogleNet, ResNet-50, DenseNet and AlexNet for …
学术搜索中的文章
A Al-Sabaawi, RI Hasan, MA Fadhel, O Al-Shamma… - International Conference on Intelligent Systems Design …, 2020