Non-destructive method to detect artificially ripened banana using hyperspectral sensing and RGB imaging

Mithun, B S; Shinde, Swati; Bhavsar, Karan; Chowdhury, Arijit; Mukhopadhyay, Shalini; Gupta, Kavya; Bhowmick, Brojeshwar; Kimbahune, Sanjay

doi:10.1117/12.2306367

Cited by 8 publications

(4 citation statements)

References 7 publications

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“…These findings can help risk managers identify the principal factors that influence food fraud, and thus, enhance their ability to risk management and food fraud risk mitigation. ELM and newly introduced CNNs are currently commonly used supervised learning algorithms in food fraud classification, such as the extreme learning machine regression (ELMR) model for identifying adulterated edible animal blood food (EABF) 67 , the CNN for classifying natural and artificially ripened bananas 68 and the improved CNN for classification of turmeric powder images to detect fraud 69 . These studies suggest that DL has emerged as an effective method for assessing food quality and identifying fraud.…”

Section: Data Analysis Methods In Food Safetymentioning

confidence: 99%

Review of visual analytics methods for food safety risks

Chen,

Wu,

Zhang

et al. 2023

npj Sci Food

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With the availability of big data for food safety, more and more advanced data analysis methods are being applied to risk analysis and prewarning (RAPW). Visual analytics, which has emerged in recent years, integrates human and machine intelligence into the data analysis process in a visually interactive manner, helping researchers gain insights into large-scale data and providing new solutions for RAPW. This review presents the developments in visual analytics for food safety RAPW in the past decade. Firstly, the data sources, data characteristics, and analysis tasks in the food safety field are summarized. Then, data analysis methods for four types of analysis tasks: association analysis, risk assessment, risk prediction, and fraud identification, are reviewed. After that, the visualization and interaction techniques are reviewed for four types of characteristic data: multidimensional, hierarchical, associative, and spatial-temporal data. Finally, opportunities and challenges in this area are proposed, such as the visual analysis of multimodal food safety data, the application of artificial intelligence techniques in the visual analysis pipeline, etc.

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Section: Data Analysis Methods In Food Safetymentioning

confidence: 99%

Review of visual analytics methods for food safety risks

Chen,

Wu,

Zhang

et al. 2023

npj Sci Food

View full text Add to dashboard Cite

show abstract

“…In [13], the authors proposed an automated method to distinguish between naturally and artificially ripened bananas using spectral and RGB data. They used a neural network on RGB data and achieved an accuracy of up to 90%.…”

Section: Related Workmentioning

confidence: 99%

A General Machine Learning Model for Assessing Fruit Quality Using Deep Image Features

Apostolopoulos¹,

Tzani²,

Aznaouridis³

2023

Preprint

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Fruit quality is a critical factor in the produce industry, affecting producers, distributors, consumers, and the economy. High-quality fruits are more appealing, nutritious, and safe, boosting consumer satisfaction and revenue for producers. Artificial Intelligence can aid in assessing the quality of the fruit using images. This paper presents a general machine-learning model for assessing fruit quality using deep image features. The model leverages the learning capabilities of the recent successful networks for image classification called Vision Transformers (ViT). The ViT model is built and trained with a combination of various fruit datasets and learned to distinguish between good and rotten fruit images. The general model demonstrated impressive results in accurately identifying the quality of various fruits such as Apples (with a 99.50% accuracy), Cucumbers (99%), Grapes (100%), Kakis (99.50%), Oranges (99.50%), Papayas (98%), Peaches (98%), Tomatoes (99.50%), and Watermelons (98%). However, it showed slightly lower performance in identifying Guavas (97%), Lemons (97%), Limes (97.50%), mangoes (97.50%), Pears (97%), and Pomegranates (97%).

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“…The nutritional value of any fruit is high when it is allowed to ripen by its natural process. Essentially, ripening occurs due to the production of ethylene after harvesting and responds to an external ripening agent such as ethylene and acetylene [3], [4]. Now, with rising demand of banana in the consumer market, it is deliberately resorted to ripen artificially using industrial grade Calcium Carbide (CaC2).…”

Section: Introductionmentioning

confidence: 99%

Multilevel Fusion of Multispectral Images to Detect the Artificially Ripened Banana

2023

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Automatic detection of artificially ripened fruits based on a non-destructive approach has recently gained significant attention. This work explores the inherent properties of multi-spectral imaging to distinguish between natural and artificially ripened bananas. The proposed method combines the prediction scores computed from the Support Vector Machine (SVM) on the individual and fused spectral bands images to detect the artificially ripened banana. Extensive analyses are performed on 5760 banana images captured in eight different spectrum bands covering Visible and Near-Infra-Red ranges. Obtained results indicate the average detection accuracy of 97.1±3.6%, thereby illustrating our proposed work's applicability.

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Non-destructive method to detect artificially ripened banana using hyperspectral sensing and RGB imaging

Cited by 8 publications

References 7 publications

Review of visual analytics methods for food safety risks

Review of visual analytics methods for food safety risks

A General Machine Learning Model for Assessing Fruit Quality Using Deep Image Features

Multilevel Fusion of Multispectral Images to Detect the Artificially Ripened Banana

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