Class distribution-aware adaptive margins and cluster embedding for classification of fruit and vegetables at supermarket self-checkouts

Hameed, Khurram; Chai, Douglas; Rassau, Alexander

doi:10.1016/j.neucom.2021.07.040

Cited by 20 publications

(7 citation statements)

References 33 publications

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“…The category "Other Fruits" corresponds to fruits that are analyzed in a single article, usually fruits of local origin. Agrivita 1 [115] Applied Intelligence 1 [116] Computational Intelligence and Neuroscience 1 [117] Computer Systems Science and Engineering 1 [118] Computers Electrical Engineering 1 [119] Data in Brief 1 [42] Ecological Informatics 1 [120] Electronics 1 [121] Energy Reports 1 [122] European Food Research and Technology 1 [123] Expert Systems With Applications 1 [124] Frontiers in Robotics and AI 1 [125] Horticulturae 1 [126] Journal of Robotics and Mechatronics 1 [135] Jurnal Kejuruteraan 1 [136] Mathematical Problems in Engineering 1 [137] Continued on next page Mechanical Systems and Signal Processing 1 [139] Multimedia Systems 1 [140] Neural Computing Applications 1 [141] Neural Network World 1 [142] Neural Networks 1 [143] Neurocomputing 1 [144] Plant and Cell Physiology 1 [145] Plants 1 [146] Postharvest Biology and Technology 1 [147] Procedia Computer Science 1 [148] Remote Sensing 1 [149] Results in Engineering 1 [150] Scientia Horticulturae 1 [151] Scientific African 1 [152] Scientific Programming 1 [153] Scientific Reports 1 [154] Sn Applied Sciences 1 [50] Sustainability 1 [155] Traitement du Signal 1 [156] Visual Computer 1 [52]…”

Section: F Publication Metadatamentioning

confidence: 99%

Analysis of Fruit Images With Deep Learning: A Systematic Literature Review and Future Directions

Espinoza,

Aguilera,

Rojas

et al. 2024

IEEE Access

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The application of Deep Learning models in fruit analysis has garnered significant attention due to its potential to revolutionize the agricultural sector and enhance crop monitoring. This paper presents a comprehensive review of recent research efforts in fruit analysis using Deep Learning techniques. The study delves into model selection, dataset characteristics, evaluation metrics, challenges, and future directions in this domain. Various model architectures, including classical Convolutional Neural Networks (CNNs) and advanced detection models like R-CNN and YOLO, have been explored for tasks ranging from fruit classification to detection. Evaluation metrics such as precision, recall, F1-score, and mean Average Precision (mAP) have been commonly used to assess model performance. Challenges, including data scarcity, labeling complexities, variations in fruit characteristics, and computational efficiency, have been identified and discussed. The paper also presents an overview of available datasets, encompassing both proprietary and publicly accessible sources. Future research directions involve exploring enhanced data augmentation, multi-modal integration, knowledge transfer across species, robustness in dynamic environments, improved computational efficiency, and practical integration of models into real-world agricultural systems. This review provides valuable insights for researchers and practitioners aiming to leverage Deep Learning for fruit analysis in the pursuit of sustainable agriculture and food production.

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Section: F Publication Metadatamentioning

confidence: 99%

Analysis of Fruit Images With Deep Learning: A Systematic Literature Review and Future Directions

Espinoza,

Aguilera,

Rojas

et al. 2024

IEEE Access

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“…In addition, the CNN can automatically extract features, and the extracted features can be clearly displayed by visualizing the neural network. Visualization of intermediate activation refers to displaying the output feature maps of each convolutional layer and pooling layer in the network for a given input [32]. As the number of network layers deepens, the image details gradually disappear from the feature map and become more and more abstract.…”

Section: A Im-resnet Visual Evaluationmentioning

confidence: 99%

Improved Residual Network for Automatic Classification Grading of Lettuce Freshness

Zhai

Chen

et al. 2022

IEEE Access

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To solve the problem of the low efficiency of traditional lettuce freshness classification methods and sample damage, we proposed an automatic lettuce freshness classification method based on improved deep residuals convolutional neural network (Im-ResNet). We built an image acquisition system to obtain the freshness classification dataset of lettuce leaves. For improving the classification accuracy, we developed an image acquisition system for curating the freshness of lettuce leaves. Then, we proposed a novel method that was derived from the existing ResNet-50 (which uses ReLU activation function) known as Improved Residual Networks (Im-ResNet): the new method factored extra convolutional layer, pooling layer, fullyconnected layers, and a random ReLU (RReLU) activation function. We also performed the corresponding experiments using the Im-ResNet network compared with four network architectures (AlexNet, GoogleNet, VGG16 and ResNet50). The experimental results showed that the proposed network had more significant advantages in the recognition accuracy and loss value of lettuce freshness compared with the traditional deep networks. The recognition accuracy of the validation set of the proposed model can reach to 95.60%. Different from the physical and chemical methods, our scheme can automatically and non-destructively classify the freshness of lettuce.

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“…Data collection condition varies for each species, depending on thepopulation of plants and public interests. The distribution of uploaded images is highly imbalanced from species to species [8].Since the generalization of discriminative features is hard to obtain with just a few training images, the performance of classification is adversely affected both by imbalanced distribution of plant images and the overlap of clusters in the feature space [9,10,11].…”

Section: Introductionmentioning

confidence: 99%

Deep Attention-based Classification of Plant Images with Hierarchical Similarity and Imbalanced Distribution

Kim¹,

Kim²

2023

IJETAE

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—Standard datasets in deep learning-basedimage classification usually provide two favorable conditions for design: preservation of visual homogeneity of each category and uniform distribution of sample images. These conditions are not assured in a dataset of plant images. Two different species of plants under the same genus look very similar and the number of collectible images has a large variation over species.Visual similarity, however, can be turned into advantage in hierarchical approach, by assigning two-fold labels of species and genus, in two phases ofrough classification of genera and fine classification of species. We propose a hierarchical classification in which the concatenation scheme is augmented with a channel attention which focuses on the sibling relation of species. We compared our method with flat classification and conventional hierarchical classification. The test was on a PlantNet-300K dataset 300k images, composed of 303 genera and 1081 species. In experimental results, the channel attention layers lead to stable discerningof the minute difference among visually similar species. The proposed hierarchical classification method outperforms both the flat classification and the conventional hierarchical classification.

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Class distribution-aware adaptive margins and cluster embedding for classification of fruit and vegetables at supermarket self-checkouts

Cited by 20 publications

References 33 publications

Analysis of Fruit Images With Deep Learning: A Systematic Literature Review and Future Directions

Analysis of Fruit Images With Deep Learning: A Systematic Literature Review and Future Directions

Improved Residual Network for Automatic Classification Grading of Lettuce Freshness

Deep Attention-based Classification of Plant Images with Hierarchical Similarity and Imbalanced Distribution

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