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نتیجه جستجو - طبقه بندی میوه ها

تعداد مقالات یافته شده: 2
ردیف عنوان نوع
1 An integrated approach using CNN-RNN-LSTM for classification of fruit images
یک رویکرد یکپارچه با استفاده از CNN-RNN-LSTM برای طبقه بندی تصاویر میوه-2021
With the advancement in technology, Computer and machine vision system is getting involved in the agriculture sector for the last few years. Deep Learning is a recent advancement in the Artificial Intelligence field. In the present era, many researchers have used deep learning applications for the classification of images, and is found to be one of the emerging areas in computer vision. In the classification of fruit images, the main goal is to improve the accuracy of the classification system. The accuracy of the classifier depends on various factors like the nature of acquired images, the number of features, types of features, selection of optimal features from extracted features, and type of classifiers used. In the pro- posed article, integration of CNN, RNN, and LSTM for the classification of fruit images are defined. In this approach, CNN and RNN are employed for the development of discriminative characteristics and sequential-labels respectively. LSTM presents an explanation by integrating a memory cell to encode learning at each interval of classification. Key parameters: accuracy, F-measure, sensitivity, and specificity are applied to assess the achievement of the proposed scheme. From empirical results, it has been declared that the offered classification method provides efficient results.© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the 1st International Conference on Computations in Materials and Applied Engineering – 2021.
Keywords: CNN | RNN | LSTM | Integrated Approach | Fruit classification
مقاله انگلیسی
2 Deep learning for noninvasive classification of clustered horticultural crops – A case for banana fruit tiers
یادگیری عمیق برای طبقه بندی غیر تهاجمی محصولات باغی خوشه ای - موردی برای رده های میوه موز-2019
Practical classification of some horticultural crops such as banana tiers, lanzones and grapes come into clusters instead of individual classification. Unlike most of classification studies, clustered crops are rarely studied due to their complex physical structure. A noninvasive deep learning classification of clustered banana given only a single image feature has been developed as a pioneering deep learning study for clustered horticultural crops. In recent deep learning developments, mask region-based convolution neural networks, also known as Mask RCNN, show unique applications in image recognition by detecting objects within an image while simultaneously generating segmentation masks. With Mask R-CNN, detection of the complex banana fruit within an image predicts the banana class while at the same time generating a mask separating the fruit from its background. A real dataset is used based on banana tiers and the developed model discriminates normal from abnormal tiers. Unlike the previous general machine learning study, which discriminates reject class from normal class with classification accuracy of 79%, our deep learning model obtained a better averaged accuracy of 92.5%. The previous average weighted accuracy of 94.2% also improved to 96.1% with only a single image feature instead of tedious multiple image and size features. With data augmentation, the model slightly improved into 93.8% accuracy on classifying reject class and 96.5% for overall accuracy. Having successfully implemented in banana tiers, this deep learning classification can also serve as basis for other clustered horticultural crops.
Keywords: Banana | Deep learning| | Fruit classification | Horticultural crop
مقاله انگلیسی
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