Non-destructive Fruit Quality Control Using Radioelectronics: a Review

Semenov, Viktor; Mitelman, Yuriy E.

doi:10.1109/usbereit48449.2020.9117644

Cited by 3 publications

(2 citation statements)

References 38 publications

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“…Different sensors have been employed for nondestructive maturity classification of fruits, including electronic noses [8], radio frequencies [9], and spectral systems [10][11][12][13][14]. Since most robotic systems incorporate a vision system on the end effector (eye-in-hand configuration) for fruit detection and localization [4,15,16], using vision for maturity classification as well is preferable.…”

Section: Maturity Classificationmentioning

confidence: 99%

Dynamic Viewpoint Selection for Sweet Pepper Maturity Classification Using Online Economic Decisions

et al. 2022

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This paper presents a rule-based methodology for dynamic viewpoint selection for maturity classification of red and yellow sweet peppers. The method makes an online decision to capture an additional next-best viewpoint based on an economic analysis that considers potential misclassification and robot operational costs. The next-best viewpoint is selected based on color variations on the pepper. Peppers were classified into mature and immature using a random forest classifier based on principle components of various color features derived from an RGB-D camera. The method first attempts to classify maturity based on a single viewpoint. An additional viewpoint is acquired and added to the point cloud only when it is deemed profitable. The methodology was evaluated using leave-one-out cross-validation on datasets of 69 red and 70 yellow sweet peppers from three different maturity stages. Classification accuracy was increased by 6% and 5% using dynamic viewpoint selection along with 52% and 12% decrease in economic costs for red and yellow peppers, respectively, compared to using a single viewpoint. Sensitivity analyses were performed for misclassification and robot operational costs.

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Section: Maturity Classificationmentioning

confidence: 99%

Dynamic Viewpoint Selection for Sweet Pepper Maturity Classification Using Online Economic Decisions

et al. 2022

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“…These techniques' application has proven results in accurately evaluating fruit quality and their contribution to reducing the time, cost, and food losses associated with destructive methods of fruit evaluation Pathmanaban et al (2019). X-ray technology is superior in fruit imaging evaluation because it provides detailed internal information, enabling precise detection of defects and abnormalities Semenov and Mitelman (2020). Using X-rays CT in fruit quality evaluation is a potential application of machine vision technology Khan et al (2022b).…”

Section: Introductionmentioning

confidence: 99%

Improving Citrus Fruit Classification Accuracy in Xray Images Using Features Enhanced Vision Transformer Architecture

Raza,

Babeker

et al. 2024

Preprint

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Quality assessment is a cornerstone of fruit production and distribution, particularly regarding storage conditions and duration. Citrus fruits, a staple in global consumption patterns, serve as aultimate example. This study employs a non-destructive analytical technique, X-ray Computed Tomography (CT) scanning to meticulously analyze a substantial sample of 300 citrus fruits, specifically satsuma, subjected to both ambient (20-22°C, 50-60% humidity) and refrigeration conditions (6-8°C, 65-75% humidity). Through a methodologically rigorous approach, we conduct Stratified Dataset Splitting, allocating 60% of the X-ray datasets for training, with 20% dedicated to validation and testing, respectively. Our research introduces a pioneering methodology termed Feature Enhancement Vision Transformers (FEViT), meticulously designed to augment precision in 1 citrus fruit classification via X-ray image analysis. By leveraging the transforma-tive potential of Vision Transformer architecture, FEViT integrates convolutional layers and expanded input dimensions to enhance classification performance substantially. Our empirical findings unequivocally demonstrate the superior efficacy of FEViT models vis-à-vis conventional ViT counterparts across diverse datasets. Particularly noteworthy are the marked performance gains exhibited by FEViT-Large variants, evidenced by notable increases in precision (5.08%), accuracy (5.47%), recall (4.55%), and F1 scores (5.28%). This underscores the distinguish-able enhanced discriminatory prowess of FEViT models. Extensive validation through rigorous experimentation ratifies FEViT’s supremacy over traditional deep learning architectures, affirming heightened accuracy, precision, recall, and F1 scores. Our study heralds the advent of FEViT architecture as a milepost in citrus fruit image classification, promising augmented accuracy and robustness vis-à-vis extant methodologies. This research holds profound implications for the agricultural sector, especially in domains such as citrus and broader fruit classification , where nuanced image analysis is indispensable for quality assessment and informed decision-making.

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Improving Citrus Fruit Classification with X-ray Images Using Features Enhanced Vision Transformer Architecture

Raza,

Babeker

et al. 2024

Food Anal. Methods

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Non-destructive Fruit Quality Control Using Radioelectronics: a Review

Cited by 3 publications

References 38 publications

Dynamic Viewpoint Selection for Sweet Pepper Maturity Classification Using Online Economic Decisions

Dynamic Viewpoint Selection for Sweet Pepper Maturity Classification Using Online Economic Decisions

Improving Citrus Fruit Classification Accuracy in Xray Images Using Features Enhanced Vision Transformer Architecture

Improving Citrus Fruit Classification with X-ray Images Using Features Enhanced Vision Transformer Architecture

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