A review on fruit recognition and feature evaluation using CNN

Indira, D.N.V.S.L.S.; Goddu, Jyothi; Indraja, Baisani; Challa, Vijaya Madhavi Lakshmi; Manasa, B

doi:10.1016/j.matpr.2021.07.267

Cited by 21 publications

(8 citation statements)

References 13 publications

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“…Many academics propose deep learning-based fruit recognition techniques to address the issue of fruit detection accuracy [1]. Fruit recognition can assist fruit vendors in identifying and distinguishing various fruit varieties that share some characteristics [2]. This is consistent with the findings of Jun Lu and Nong Sang [3], who used robot media to detect citrus fruits.…”

Section: Introductionsupporting

confidence: 64%

Fruit Type Recognition Using Hybrid Method with Principal Component Analysis (PCA)

Sulthan¹,

Hartiansyah²

2023

Proceedings of Malikussaleh International Conference on Multidi

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This research concentrated on fruit image recognition. Fruit recognition in this study can be used to estimate the number of fruits that exist. Data testing is used to classify a fruit image that has been trained to recognize a variety of labels (fruit types). Until the classification process, several processes and methods are used in this research, one of which is the Gaussian filter to improve the quality of fruit image recognition. In addition, the Gabor filter is used in the feature extraction process, and the PCA technique is used in feature selection to select the best features. To classify the chosen feature, deep learning and the k-nearest neighbor (k-NN) method will be used. Furthermore, the processes used improved the accuracy of 92%, a root mean squared error (RMSE) of 0.323, a mean squared error (MSE) of 0.6278.

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Section: Introductionsupporting

confidence: 64%

Fruit Type Recognition Using Hybrid Method with Principal Component Analysis (PCA)

Sulthan¹,

Hartiansyah²

2023

Proceedings of Malikussaleh International Conference on Multidi

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“…Due to the diversity of agricultural products, a single type may encompass multiple varieties or subtypes, making fine-grained classification a complex and crucial undertaking. One primary challenge of existing models 28 in agricultural product classification is their poor performance in handling subtypes with similar features. To address this issue, we introduce an efficient multi-scale cross-space learning module with attention (EMA) and an Inception module to enhance the accuracy of recognizing subcategories within agricultural products.…”

Section: Related Workmentioning

confidence: 99%

Classification and identification of agricultural products based on improved MobileNetV2

Chen,

Zhou,

et al. 2024

Sci Rep

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With the advancement of technology, the demand for increased production efficiency has gradually risen, leading to the emergence of new trends in agricultural automation and intelligence. Precision classification models play a crucial role in helping farmers accurately identify, classify, and process various agricultural products, thereby enhancing production efficiency and maximizing the economic value of agricultural products. The current MobileNetV2 network model is capable of performing the aforementioned tasks. However, it tends to exhibit recognition biases when identifying different subcategories within agricultural product varieties. To address this challenge, this paper introduces an improved MobileNetV2 convolutional neural network model. Firstly, inspired by the Inception module in GoogLeNet, we combine the improved Inception module with the original residual module, innovatively proposing a new Res-Inception module. Additionally, to further enhance the model's accuracy in detection tasks, we introduce an efficient multi-scale cross-space learning module (EMA) and embed it into the backbone structure of the network. Experimental results on the Fruit-360 dataset demonstrate that the improved MobileNetV2 outperforms the original MobileNetV2 in agricultural product classification tasks, with an accuracy increase of 1.86%.

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“…The relationship between deep learning, machine learning, and artificial intelligence is shown in Figure 5. When applied to tasks such as image classification or segmentation using techniques like convolutional neural networks (CNNs) [63,64], deep learning models can identify subtle structural or functional abnormalities in brain scans. They excel at capturing complex relationships within the data, enabling the recognition of disease-specific patterns that may not be apparent through traditional methods [65].…”

Section: Deep Learningmentioning

confidence: 99%

Recent Computational Methods for Pathological Brain Detection

Chen

2023

Preprint

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Pathological Brain Detection (PBD) is a crucial field aimed at identifying and diagnosing structural or functional abnormalities in the brain related to neurological, neurodegenerative, or psychiatric disorders. This detection process typically employs medical imaging technologies such as MRI, CT, or PET scans, along with neurological evaluations, blood tests, and other diagnostic tools. Early detection is essential for effective treatment, improving prognosis, and enhancing the quality of life for affected individuals. Each common computational method provides unique insights into brain pathologies. Despite significant advancements in technology and methodology, PBD faces challenges such as variability in brain anatomy, the complexity of disorders, data quality, standardization issues, interpretability of models, and ethical concerns. Addressing these challenges necessitates collaboration among researchers, clinicians, and policymakers to develop robust and ethical methods for improving the detection and diagnosis of brain disorders.

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A review on fruit recognition and feature evaluation using CNN

Cited by 21 publications

References 13 publications

Fruit Type Recognition Using Hybrid Method with Principal Component Analysis (PCA)

Fruit Type Recognition Using Hybrid Method with Principal Component Analysis (PCA)

Classification and identification of agricultural products based on improved MobileNetV2

Recent Computational Methods for Pathological Brain Detection

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