Analysis of visual features and classifiers for Fruit classification problem

Ghazal, Sumaira; Qureshi, Waqar S.; Khan, Umar Shahbaz; Iqbal, Javaid; Rashid, Nasir; Tiwana, Mohsin I.

doi:10.1016/j.compag.2021.106267

Cited by 44 publications

(26 citation statements)

References 23 publications

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“…SE block only recalibrates channel-wise feature responses and enhances the network's attention to important channels. For the spatial distribution inside the feature map, the SE block does not make further processing; however, some subtle differences in fruit images may be reflected in the spatial dimension, such as the subtle texture and contour differences of different types of fruits [82]. Hence, it is necessary to add a spatial attention mechanism to the network.…”

Section: Lightweight Attention Networkmentioning

confidence: 99%

A cross-domain fruit classification method based on lightweight attention networks and unsupervised domain adaptation

Wang

Zhang

Yan

et al. 2022

Complex Intell. Syst.

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Image-based fruit classification offers many useful applications in industrial production and daily life, such as self-checkout in the supermarket, automatic fruit sorting and dietary guidance. However, fruit classification task will have different data distributions due to different application scenarios. One feasible solution to solve this problem is to use domain adaptation that adapts knowledge from the original training data (source domain) to the new testing data (target domain). In this paper, we propose a novel deep learning-based unsupervised domain adaptation method for cross-domain fruit classification. A hybrid attention module is proposed and added to MobileNet V3 to construct the HAM-MobileNet that can suppress the impact of complex backgrounds and extract more discriminative features. A hybrid loss function combining subdomain alignment and implicit distribution metrics is used to reduce domain discrepancy during model training and improve model classification performance. Two fruit classification datasets covering several domains are established to simulate common industrial and daily life application scenarios. We validate the proposed method on our constructed grape classification dataset and general fruit classification dataset. The experimental results show that the proposed method achieves an average accuracy of 95.0% and 93.2% on the two datasets, respectively. The classification model after domain adaptation can well overcome the domain discrepancy brought by different fruit classification scenarios. Meanwhile, the proposed datasets and method can serve as a benchmark for future cross-domain fruit classification research.

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Section: Lightweight Attention Networkmentioning

confidence: 99%

A cross-domain fruit classification method based on lightweight attention networks and unsupervised domain adaptation

Wang

Zhang

Yan

et al. 2022

Complex Intell. Syst.

View full text Add to dashboard Cite

show abstract

“…Some popular classifiers, including K-nearest neighbor (KNN), random forest (RF), artificial neural network (ANN), and SVM, were used for different kinds of tea quality identification and achieved excellent results [4][5][6]. It is known that achieving accurate identification also relies on effective hand-designed features, such as color, texture, and shape [7,8]. However, different qualities of tea tend to have minor differences in appearance, resulting in low identification accuracy of hand-designed features combined with classical machine learning methods [9].…”

Section: Introductionmentioning

confidence: 99%

“…Fig 8. The precision values of every single class for multiclass TrAdaBoost with different base learners…”

mentioning

confidence: 99%

An instance-based deep transfer learning method for quality identification of Longjing tea from multiple geographical origins

Zhang

Wang

Yan

et al. 2023

Complex Intell. Syst.

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For practitioners, it is very crucial to realize accurate and automatic vision-based quality identification of Longjing tea. Due to the high similarity between classes, the classification accuracy of traditional image processing combined with machine learning algorithm is not satisfactory. High-performance deep learning methods require large amounts of annotated data, but collecting and labeling massive amounts of data is very time consuming and monotonous. To gain as much useful knowledge as possible from related tasks, an instance-based deep transfer learning method for the quality identification of Longjing tea is proposed. The method mainly consists of two steps: (i) The MobileNet V2 model is trained using the hybrid training dataset containing all labeled samples from source and target domains. The trained MobileNet V2 model is used as a feature extractor, and (ii) the extracted features are input into the proposed multiclass TrAdaBoost algorithm for training and identification. Longjing tea images from three geographical origins, West Lake, Qiantang, and Yuezhou, are collected, and the tea from each geographical origin contains four grades. The Longjing tea from West Lake is regarded as the source domain, which contains more labeled samples. The Longjing tea from the other two geographical origins contains only limited labeled samples, which are regarded as the target domain. Comparative experimental results show that the method with the best performance is the MobileNet V2 feature extractor trained with a hybrid training dataset combined with multiclass TrAdaBoost with linear support vector machine (SVM). The overall Longjing tea quality identification accuracy is 93.6% and 91.5% on the two target domain datasets, respectively. The proposed method can achieve accurate quality identification of Longjing tea with limited samples. It can provide some heuristics for designing image-based tea quality identification systems.

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“…Computer vision can be used for analyzing an image, specifically identifying the quality of the fruit [2][3][4]. Instead of using human eyes to observe the fruit color, the computer can process an image and classify fruit quality by using certain algorithms.…”

Section: Introductionmentioning

confidence: 99%

Fruit Quality Classification using Convolutional Neural Network

Suhendar

Efelina

Ziveria

2022

J. Phys.: Conf. Ser.

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Fruit quality identification is very important in the food industry for maintaining product quality. The quality control in the food industry commonly conducted by human senses which is lack of objectivity and takes long time for real-time mass production quality control. The quality of the fruit can be identified through its color, smell, and texture. This study uses fruit image to classify the quality of the fruit. We trained artificial neural networks for classifying fruit quality from Indian Fruit Dataset with Quality (FruitNet). The dataset contains six classes of fruits with three categorical qualities (Good, Bad, and Mixed). The dataset features were extracted using several pre-trained deep learning networks trained on the ImageNet dataset. The convolutional networks for feature extraction used in this study are VGG16, MobileNetV2, EfficientNetB0, and ResNet50. The extracted features are forwarded to neural network for training the dataset. The result shown that f1-score for testing dataset reaches more than 90% except for MobileNetV2. The highest f1-score is obtained from ResNet50 feature extraction which is 95.7%.

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Analysis of visual features and classifiers for Fruit classification problem

Cited by 44 publications

References 23 publications

A cross-domain fruit classification method based on lightweight attention networks and unsupervised domain adaptation

A cross-domain fruit classification method based on lightweight attention networks and unsupervised domain adaptation

An instance-based deep transfer learning method for quality identification of Longjing tea from multiple geographical origins

Fruit Quality Classification using Convolutional Neural Network

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