A novel approach to detect and classify fruits using ShuffleNet V2

Ghosh, Sourodip; Mondal, Md. Jashim; Sen, Sourish; Chatterjee, Soham; Roy, Nilanjan; Patnaik, Suprava

doi:10.1109/aspcon49795.2020.9276669

Cited by 23 publications

(12 citation statements)

References 9 publications

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“…In addition, the attempts presented in the year 2020 have noticeable accuracy reduction of 4-5% (96.25% [22] and 95% [23]). This could be due to the growth of the dataset.…”

Section: The Accuracy Evaluation Of the Confusion Matrixmentioning

confidence: 98%

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An Efficient Automatic Fruit-360 Image Identification and Recognition Using a Novel Modified Cascaded-ANFIS Algorithm

Rathnayake

Dang

et al. 2022

Sensors

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Automated fruit identification is always challenging due to its complex nature. Usually, the fruit types and sub-types are location-dependent; thus, manual fruit categorization is also still a challenging problem. Literature showcases several recent studies incorporating the Convolutional Neural Network-based algorithms (VGG16, Inception V3, MobileNet, and ResNet18) to classify the Fruit-360 dataset. However, none of them are comprehensive and have not been utilized for the total 131 fruit classes. In addition, the computational efficiency was not the best in these models. A novel, robust but comprehensive study is presented here in identifying and predicting the whole Fruit-360 dataset, including 131 fruit classes with 90,483 sample images. An algorithm based on the Cascaded Adaptive Network-based Fuzzy Inference System (Cascaded-ANFIS) was effectively utilized to achieve the research gap. Color Structure, Region Shape, Edge Histogram, Column Layout, Gray-Level Co-Occurrence Matrix, Scale-Invariant Feature Transform, Speeded Up Robust Features, Histogram of Oriented Gradients, and Oriented FAST and rotated BRIEF features are used in this study as the features descriptors in identifying fruit images. The algorithm was validated using two methods: iterations and confusion matrix. The results showcase that the proposed method gives a relative accuracy of 98.36%. The Fruit-360 dataset is unbalanced; therefore, the weighted precision, recall, and FScore were calculated as 0.9843, 0.9841, and 0.9840, respectively. In addition, the developed system was tested and compared against the literature-found state-of-the-art algorithms for the purpose. Comparison studies present the acceptability of the newly developed algorithm handling the whole Fruit-360 dataset and achieving high computational efficiency.

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“…In addition, the attempts presented in the year 2020 have noticeable accuracy reduction of 4-5% (96.25% [22] and 95% [23]). This could be due to the growth of the dataset.…”

Section: The Accuracy Evaluation Of the Confusion Matrixmentioning

confidence: 98%

“…This created the problem of robustness. In addition, Ghosh et al [23] have introduced an image classification model using ShufleNet V2 that is based on the CNN algorithm. They have obtained an accuracy of 96.24% for 40 classes in the Fruit-360 dataset.…”

Section: Related Workmentioning

confidence: 99%

An Efficient Automatic Fruit-360 Image Identification and Recognition Using a Novel Modified Cascaded-ANFIS Algorithm

Rathnayake

Dang

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…To better meet particular requirements, certain studies [33] [34] leverage the newly created backbone for feature extraction. People may select between highly linked backbones, such as ResNet [35], ResNeXt [36], AmoebaNet [37], and lighter backbones, such as SqueezeNet [38], MobileNet [39], ShuffleNet [40], MobileNetV2 and Xception [41], depending on their needs for accuracy vs. efficiency. Lightweight backbones can suit the needs of mobile devices.…”

Section: Backbone Network In Object Detectionmentioning

confidence: 99%

An Evaluation of Deep Learning-Based Object Identification

Kolluri

Das

2022

IJRITCC

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Identification of instances of semantic objects of a particular class, which has been heavily incorporated in people's lives through applications like autonomous driving and security monitoring, is one of the most crucial and challenging areas of computer vision. Recent developments in deep learning networks for detection have improved object detector accuracy. To provide a detailed review of the current state of object detection pipelines, we begin by analyzing the methodologies employed by classical detection models and providing the benchmark datasets used in this study. After that, we'll have a look at the one- and two-stage detectors in detail, before concluding with a summary of several object detection approaches. In addition, we provide a list of both old and new apps. It's not just a single branch of object detection that is examined. Finally, we look at how to utilize various object detection algorithms to create a system that is both efficient and effective. and identify a number of emerging patterns in order to better understand the using the most recent algorithms and doing more study.

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“…Furthermore, DL is also very effective for grading the severity of plants with the same disease (Verma et al, 2020). Liang et al (2019) combined ResNet50 (Wen et al, 2020 model and Shufflenet-V2 (Ghosh et al, 2020) to build a PD 2 SE-Net network model, which realized the classification of plant diseases and the prediction of disease severity. Yu et al (2006) applied VGG16 model on diseased leaf images for grading the severity of apple black rot (Wang et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

A Novel Computational Framework for Precision Diagnosis and Subtype Discovery of Plant With Lesion

et al. 2022

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Plants are often attacked by various pathogens during their growth, which may cause environmental pollution, food shortages, or economic losses in a certain area. Integration of high throughput phenomics data and computer vision (CV) provides a great opportunity to realize plant disease diagnosis in the early stage and uncover the subtype or stage patterns in the disease progression. In this study, we proposed a novel computational framework for plant disease identification and subtype discovery through a deep-embedding image-clustering strategy, Weighted Distance Metric and the t-stochastic neighbor embedding algorithm (WDM-tSNE). To verify the effectiveness, we applied our method on four public datasets of images. The results demonstrated that the newly developed tool is capable of identifying the plant disease and further uncover the underlying subtypes associated with pathogenic resistance. In summary, the current framework provides great clustering performance for the root or leave images of diseased plants with pronounced disease spots or symptoms.

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A novel approach to detect and classify fruits using ShuffleNet V2

Cited by 23 publications

References 9 publications

An Efficient Automatic Fruit-360 Image Identification and Recognition Using a Novel Modified Cascaded-ANFIS Algorithm

An Efficient Automatic Fruit-360 Image Identification and Recognition Using a Novel Modified Cascaded-ANFIS Algorithm

An Evaluation of Deep Learning-Based Object Identification

A Novel Computational Framework for Precision Diagnosis and Subtype Discovery of Plant With Lesion

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