The feature maps acquired from convolution and attention modules are fused using a simple concatenation feature fusion approach as suggested by Sitaula et al. The reported results show that RF classifier with an accuracy of 94.20% outperforms the remaining classifiers. The concatenation of these two feature map on third dimension, produce a feature map, F2(I) R(HW2). The new PMC design is here! A cucumber shape classification system based on shape features, such as area, perimeter, eccentricity, extent, roundness, compresses, width-non-homogeneity and centroid-non-homogeneity, was proposed by Kheiralipour et al. HHS Vulnerability Disclosure, Help arXiv preprint arXiv:201011929. However, existing DL-based methods still have two main limitations. Similarly, Saranya et al. For instance, authors in [36] investigated the DenseNet [37] for oil palm fruit ripeness classification and produced the highest classification accuracy (86%). International Conference on Image Analysis and Processing; 2017. p. 385393.

A local binary pattern based features were used in bark texture classification using multilayer neural network [26]. Herman H, Cenggoro TW, Susanto A, Pardamean B.

The reason for this might be the features captured from the pre-trained model on ImageNet dataset (which is the large image dataset with millions of images) while other three models are trained from scratch on the fruit-images only. Pacific-Rim Symposium on Image and Video Technology; 2019. p. 404415. In CBAM, it has two modules: channel attention and spatial attention modules. Also, a few works that employed transformers for image classification task reported that transformers outperform the CNN when they have enough training dataset [42]. Then, these feature were used in three classifiers: Partial least square (PLS), SVM and ANN. The VIS (visual wave-bands) part of spectra were the main contributors in ripeness classification in their method. In: Proc. Also, a strawberry ripeness classification using multispectral imaging of 17 bands was proposed in [29], where the PCA was applied to reduce the dimension of multispectral images features. Federal government websites often end in .gov or .mil. Rojas-Aranda JL, Nunez-Varela JI, Cuevas-Tello JC, Rangel-Ramirez G. Fruit Classification for Retail Stores Using Deep Learning. These spectra were then fed into two machine learning algorithms: support vector machine (SVM) and Artificial Neural Network (ANN). In the meantime, Femling et al. Five different random train/test splits are used for each dataset to report the final averaged performance.

Thus, convolutional operations are major contributors in computer vision tasks but they are computationally expensive when the network structure goes deeper and larger as in VGG-16 [32], and InceptionV3 [33]. Femling F, Olsson A, Alonso-Fernandez F. Fruit and vegetable identification using machine learning for retail applications. [2] implemented a Banana ripeness classification system using lightwight MobileNetV2 and compared it with NASNetMobile [38] model. Dataset 3 (D3) [44]: This is the largest fruit and vegetable dataset having classes at various levels: 53 classes at first level, 81 fine classes at second level and 125 classes at third level. The concatenation of these two features results in a single feature tensor of H W 1281 dimension. Authors in [24] used a Random Forest (RF) method for three kinds of fruits classification: Strawberry, Apple, and Oranges.

The deep learning (DL) methods are evolved along with the advancement of artificial neural network (ANN), which were inspired from human brain. [4] used the Fruit-360 dataset with the batch size of 128, epochs of 50, and Adam optimizer along with extensive data-augmentation. They achieved an accuracy of 95.23% using some data augmentation strategies such as flip, hue/saturation changes and gray-scale. Since this dataset has a balanced number of samples in each fruit class (300 samples in each class in test set), all four DL methods achieve more than 93% accuracy and F1-scores (both WAF1 and MAF1 are equal as number of samples in each class are same) on this dataset.

will also be available for a limited time. In: 2014 14th International Conference on Hybrid Intelligent Systems. Fruit recognition from images using deep learning, Acta Universitatis Sapientiae, Informatica.

Boldface indicates the highest performance. They evaluated the proposed model for the classification of Mango ripeness and size, which achieved an accuracy of 93.33% and 92.27% on the RGB image dataset and thermal dataset, respectively.

School of Engineering and Technology, Central Queensland University, North Rockhampton, QLD, Australia, 2 They used 925 fruit samples for model training and validation and reported that SVM produced the best F1-measure (70.14%) among twelve different machine learning classifiers. The reason for this is that we freeze all the layers in pre-trained MobileNetV2 and only train the few layers on the top of these layers (refer to Fig 1). Very Deep Convolutional Networks for Large-Scale Image Recognition.

Do Vision Transformers See Like Convolutional Neural Networks? Xiang Q, Wang X, Li R, Zhang G, Lai J, Hu Q.

In: 2021 International Conference on Applied and Engineering Mathematics (ICAEM). The classification accuracy is 92.04% if using convolution module only, whereas it imparts 95.75% by including the attention module. MobileNetV2+TL used the self-created fruit dataset with 3,670 images. This backbone network acts as convolutional feature extractor in our study. For this, they collected the grapevines leaves and took the NIR spectra measurement of individual leaf from 20 types of vines. Since, the transformer are based on pixel-wise attention mechanism rather than convolution operation as in CNN [43], their use in computer vision task is still not mature enough.

Their results show that it achieved a higher classification accuracy of 100% when using the SVM classifier. In: Proc. For D3, our method is the only outstanding performer, bettered all other DL methods by large margins in all evaluation metrics. [4] proposed a CNN model for fruit classification on the Fruit-360 dataset, which achieved an classification accuracy of 94.35%. 1 Note that the fruit image used here is Republished from [44] under CC BY license, with permission from [Georg Waltner], original copyright[2022]. Firstly, such methods mostly require a large number of trainable parameters although they claim that their models to be lightweight architectures. For D1, the DenseNet-121 [37] is the second-best performing model with an accuracy of 94.53 while MobileNetV1 [34] has the least classification accuracy (86.69%) being lower by 9.06% with our method. where TPa, TNa, FPa and FNa represent true positive, true negative, false positive and false negative for class a. It is interesting to note that our method being a lightweight method has comparable performance to those large and deeper CNNs (VGG-16, InceptionV3, DenseNet-121).

arXiv preprint arXiv:160207360. The overall classification accuracy produced by their method was 87.25%. The combination of the convolution and attention modules is expected to work complementarily to each other to achieve better performance in fruit classification. (iv) We validate our model utilizing three different fruit datasets to confirm the robustness of our model.

Woo S, Park J, Lee JY, Kweon IS. Secondly, the performances of these models are dependent on the number of classes and the number of available datasets.

[16] used the publicly available dataset Fruit-360 and evaluated with various input settings such as grayscale image, RGB images, HSV images, batch size of 60, epochs of 50, fixed train/test split, and so on. The convolution and attention modules are then combined together to fuse both the high-level object-based information and the interesting semantic information, which is followed by the fully connected layers and the softmax layer.

In: 2021 International Conference on Information Management and Technology (ICIMTech).

The combination of convolution and attention modules helps discriminate the diverse classes of fruits as they are complementary to each other. Furthermore, to make comparison on the level ground, we also implemented four latest DL methods on the same computer with Tesla-P100 GPU with 16GB RAM. Here, we are more interested in attention mechanism that not only points us where to focus on but also represents the spatial relationship of visual clues in the fruit images. Learn more Amsterdam, The Netherlands; 1995. The statistics of all experimental results are presented in Table 3. Boldface represent the highest performance. For example, Rojas et al. Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T, et al. With the rise of deep learning (DL)-based methods in various disciplines, such as stock market [7], scene images [8], biomedical [9], and privacy [10], researchers have been working towards the development of deep learning-based methods for the fruits classification problem, using the transfer learning (TL) approach mostly [11, 12]. Residual attention network for image classification. The output of this distribution is defined as in Eq (5). However, a low Precision and a high Recall are observed for fruit class Orange. Their method is limited to the PCA-based feature fusion and have not considered the other feature fusion methods such as fusion of score or decisions. This dataset, also known as Fruit-360 dataset, which consists of 28,736 training images and 9,673 testing images. Note that the fruit image used here is Republished from [44] under CC BY license, with permission from [Georg Waltner], original copyright[2022].

The https:// ensures that you are connecting to the Dataset 2 (D2) [51]: This is an Indian fruit dataset, which contains 12 classes. This might be due to the uniform textures present in this image which dont have any specific information to be captured by attention module.

There is a necessity to explore lightweight deep learning models without compromising the classification accuracy. Note that (a), (b), and (c) denote potato, diamond peach and watermelon fruits classes, respectively. CNN+Augmentation by Joshep et al. We also need to explore how our method can efficiently operate in a mobile environment, or on a cutting-edge computing platform, particularly in an Internet of Things (IoT) environment. In a similar study, Xiang et al. In: Proceedings of the IEEE international conference on computer vision; 2017. p. 618626. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2017. p. 31563164. Because of the different experimental setup with different datasets in these existing studies, it is difficult to make a fair comparison between these methods and ours. In other words, the use of CNN for image recognition and classification is not only becomes a trend in fruit classification [3] but also in other domains such as biomedical image analysis [9], scene images recognition [8], remote sensing image analysis [45] and so on. Gutirrez S, Tardaguila J, Fernndez-Novales J, Diago MP.

Support vector machine and artificial neural network models for the classification of grapevine varieties using a portable NIR spectrophotometer.

The confusion matrix tabulates the actual classes versus predicted classes.

For example, Muhammad et al.

Fruit Classification Using Deep Learning.

Their results show that the VGG-16 model has the highest accuracy of 99.01% during the fruit classification. Sitaula C, Xiang Y, Aryal S, Lu X. Unsupervised deep features for privacy image classification. This is likely due to the fact that the images in this dataset contain the heterogeneous background that require more useful features extracted from the image so as to properly separate the fruits from the backgrounds.

Shook J, Gangopadhyay T, Wu L, Ganapathysubramanian B, Sarkar S, Singh AK. Note that MAF1 and WAF1, denote macro averaged F1-score, weighted averaged F1 score respectively. The overall steps for attention module are summarized in Eqs (2) and (3). Each image is taken with different angles, backgrounds and lighting conditions. As a result, this may be infeasible to deploy on lightweight environments such as mobile/edge computing platforms. The residual connection helps the flow of gradients through the network. Rossum G. Python Reference Manual. While looking at Table 4, our methods has the least trainable parameters compared to all four latest DL methods even though the total parameters in our model are more than the other two CNNs (Light-CNN and CNN+Augmentation). [3] proposed a lightweight CNN model and compared it with fine-tuned VGG-16 model [32] for fruit classification on two datasets.

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