An Overview of Various Computer Vision-based Grading System for Various Agricultural Products

Sivaranjani, A.; Senthilrani, S.; Kumar, B. Ashok; Murugan, A.

doi:10.1080/14620316.2021.1970631

Cited by 16 publications

(8 citation statements)

References 71 publications

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“…Zhang et al [ 53 ] present a detailed solution showed that it was possible to combine computer vision with other new imaging techniques, 3D technologies, etc., to develop more advanced intelligent equipment. The selection of good characteristics for classification is an important process of feature extraction [ 54 ]. Xu et al [ 48 ] pointed out that computer vision was a simple simulation or imitation of human vision and could not adapt to changes in food types as flexibly as the human eye.…”

Section: Intelligent Detection Equipment Based On Sensory Bionicsmentioning

confidence: 99%

Intelligent System/Equipment for Quality Deterioration Detection of Fresh Food: Recent Advances and Application

Wang,

Zhang,

Jiang

et al. 2024

Foods

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The quality of fresh foods tends to deteriorate rapidly during harvesting, storage, and transportation. Intelligent detection equipment is designed to monitor and ensure product quality in the supply chain, measure appropriate food quality parameters in real time, and thus minimize quality degradation and potential financial losses. Through various available tracking devices, consumers can obtain actionable information about fresh food products. This paper reviews the recent progress in intelligent detection equipment for sensing the quality deterioration of fresh foods, including computer vision equipment, electronic nose, smart colorimetric films, hyperspectral imaging (HSI), near-infrared spectroscopy (NIR), nuclear magnetic resonance (NMR), ultrasonic non-destructive testing, and intelligent tracing equipment. These devices offer the advantages of high speed, non-destructive operation, precision, and high sensitivity.

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Section: Intelligent Detection Equipment Based On Sensory Bionicsmentioning

confidence: 99%

Intelligent System/Equipment for Quality Deterioration Detection of Fresh Food: Recent Advances and Application

Wang,

Zhang,

Jiang

et al. 2024

Foods

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“…(2022), and Sivaranjani et al. (2021). However, these reviews mostly focused on the application of imaging techniques in the food industry in general without an emphasis on the bakery industry.…”

Section: Introductionmentioning

confidence: 97%

“…In the same vein, several review papers have been published detailing the application of different imaging techniques/computer vision systems in the food/agricultural industry. Examples are Meenu et al (2021), Zhu et al (2021), Zhou et al (2019), Amani et al (2020), Saha and Manickavasagan (2021), Dhanya et al (2022), Wang et al (2022), andSivaranjani et al (2021). However, these reviews mostly focused on the application of imaging techniques in the food industry in general without an emphasis on the bakery industry.…”

Section: Introductionmentioning

confidence: 99%

Applications of imaging systems for the assessment of quality characteristics of bread and other baked goods: A review

Olakanmi

Jayas

Paliwal

2023

Comp Rev Food Sci Food Safe

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One of the most widely researched topics in the food industry is bread quality analysis. Different techniques have been developed to assess the quality characteristics of bakery products. However, in the last few decades, the advancement in sensor and computational technologies has increased the use of computer vision to analyze food quality (e.g., bakery products). Despite a large number of publications on the application of imaging methods in the bakery industry, comprehensive reviews detailing the use of conventional analytical techniques and imaging methods for the quality analysis of baked goods are limited. Therefore, this review aims to critically analyze the conventional methods and explore the potential of imaging techniques for the quality assessment of baked products. This review provides an in-depth assessment of the different conventional techniques used for the quality analysis of baked goods which include methods to record the physical characteristics of bread and analyze its quality, sensory-based methods, nutritional-based methods, and the use of dough rheological data for end-product quality prediction. Furthermore, an overview of the image processing stages is presented herein. We also discuss, comprehensively, the applications of imaging techniques for assessing the quality of bread and other baked goods.These applications include studying and predicting baked goods' quality characteristics (color, texture, size, and shape) and classifying them based on these features. The limitations of both conventional techniques (e.g., destructive, laborious, error-prone, and expensive) and imaging methods (e.g., illumination, humidity, and noise) and the future direction of the use of imaging methods for quality analysis of bakery products are discussed.

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“…In recent years, object detection, image segmentation, and classification are just a few of the computer vision-related tasks that convolutional neural networks (CNNs) and deep learning approaches have proven to be quite successful at Li et al (2021). Different deep learning vision methods have become powerful tools in various industries, including agriculture and food processing Sivaranjani et al (2022). Numerous fields have substantially increased interest in computer vision, including medical imaging and agricultural applications Zhuang et al (2018).…”

Section: Introductionmentioning

confidence: 99%

“…Applying the proposed model in citrus fruit classification allows for a more accurate and efficient analysis of fruit attributes, leading to quality in the citrus fruit industry Bhargava and Bansal (2021). This paper applies the feature enhancement ViT models in classifying citrus fruits using X-ray images Sivaranjani et al (2022). This study aims to handle variations in size, shape, and internal features across different citrus fruit varieties Naranjo-Torres et al (2020).…”

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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An Overview of Various Computer Vision-based Grading System for Various Agricultural Products

Cited by 16 publications

References 71 publications

Intelligent System/Equipment for Quality Deterioration Detection of Fresh Food: Recent Advances and Application

Intelligent System/Equipment for Quality Deterioration Detection of Fresh Food: Recent Advances and Application

Applications of imaging systems for the assessment of quality characteristics of bread and other baked goods: A review

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

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