Investigating Maturity State and Internal Properties of Fruits Using Non-destructive Techniques-a Review

Shaib, Mohd Fadzli Abd; Rahim, Ruzairi Abdul; Muji, Siti Zarina Mohd; Ahmad, Azian Abd Aziz

doi:10.12928/telkomnika.v15i4.7236

Cited by 5 publications

(3 citation statements)

References 30 publications

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“…X-ray radiography is a non-destructive technology that offers valuable insights into the internal properties of fruits by displaying density differences using grayscale levels. This enables the identification of healthy fruits and those with pest damages while classifying them without the need for destructive sampling (Abdshaib et al 2017;Diels et al 2017). The varying densities inside fruits, influenced by factors such as water content, hard tissues, insect pest holes, hollowness, and corruption, are represented by different grayscale levels.…”

Section: X-raymentioning

confidence: 99%

State-of-the-art non-destructive approaches for maturity index determination in fruits and vegetables: principles, applications, and future directions

Anjali,

Jena,

Bamola

et al. 2024

Food Prod Process and Nutr

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Recent advancements in signal processing and computational power have revolutionized computer vision applications in diverse industries such as agriculture, food processing, biomedical, and the military. These developments are propelling efforts to automate processes and enhance efficiency. Notably, computational techniques are replacing labor-intensive manual methods for assessing the maturity indices of fruits and vegetables during critical growth stages.This review paper focuses on recent advancements in computer vision techniques specifically applied to determine the maturity indices of fruits and vegetables within the food processing sector. It highlights successful applications of Nuclear Magnetic Resonance (NMR), Near-Infrared Spectroscopy (NIR), thermal imaging, and image scanning. By examining these techniques, their underlying principles, and practical feasibility, it offers valuable insights into their effectiveness and potential widespread adoption. Additionally, integrating biosensors and AI techniques further improves accuracy and efficiency in maturity index determination.In summary, this review underscores the significant role of computational techniques in advancing maturity index assessment and provides insights into their principles and effective utilization. Looking ahead, the future of computer vision techniques holds immense potential. Collaborative efforts among experts from various fields will be crucial to address challenges, ensure standardization, and safeguard data privacy. Embracing these advancements can lead to sustainable practices, optimized resource management, and progress across industries. Graphical Abstract

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Section: X-raymentioning

confidence: 99%

State-of-the-art non-destructive approaches for maturity index determination in fruits and vegetables: principles, applications, and future directions

Anjali,

Jena,

Bamola

et al. 2024

Food Prod Process and Nutr

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“…One of the essential steps in spectral image processing is to correct each spectral image using a standard white reference image and a dark image. The (1) shows the relation of the corrected intensity (𝐼 𝑐 ) as the function of 𝐼 𝑟 , 𝐼 𝑑 , and 𝐼 𝑤 were the raw hyperspectral image intensity, the dark image intensity, and the white reference image intensity, respectively [26].…”

Section: Image Preprocessing and Spectral Mean Conversionmentioning

confidence: 99%

“…It has flourished in the last two decades for evaluating the quality of fruits and vegetables. Ripeness, external or internal damages, and chemical contents are among the quality attributes often used for classifications and predictions of fruits and vegetables [1]. Machine vision uses computer vision with other instruments to perform automatic tasks, especially for non-destructive and fast sorting and grading of fruits and vegetables.…”

Section: Introductionmentioning

confidence: 99%

Neural network with k-fold cross validation for oil palm fruit ripeness prediction

Shiddiq,

Candra,

Anand

et al. 2023

TELKOMNIKA

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The combination of hyperspectral imaging and artificial neural network (ANN) can predict fruit ripeness. This work investigated the application of hyperspectral imaging and ANN models with the k-fold cross-validation method for ripeness prediction of oil palm fresh fruit bunches (FFB) for inline sorting and grading machine vision. Crude palm oil (CPO) is an exporting commodity for countries such as Indonesia and Malaysia. Oil palm FFB ripeness determines the quality of CPO. The unique shapes and colors of FFBs need innovative methods to substitute tedious and cumbersome manual sorting and grading. The oil palm FFB samples used in this study were categorized previously based on color and loosed fruits. We applied the Savitzky-Golay (SG) smoothing filter and 7-fold cross-validation for hyperspectral datasets before being used for the ANN models and a confusion matrix to find the ANN model accuracies. We obtained 72 data points after SG filter and data selection from 523 data points. The prediction results showed an average accuracy of 79.48%, in which three folds with k of 2, 5, and 7 gave the highest accuracy of 90%. The results confirmed the potential use of hyperspectral imaging, with k-fold cross-validation and ANN models for ripeness prediction of oil palm FFBs.

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Sensors for Non‐Destructive Quality Evaluation of Food

et al. 2021

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Investigating Maturity State and Internal Properties of Fruits Using Non-destructive Techniques-a Review

Abstract: Abstract

Cited by 5 publications

References 30 publications

State-of-the-art non-destructive approaches for maturity index determination in fruits and vegetables: principles, applications, and future directions

State-of-the-art non-destructive approaches for maturity index determination in fruits and vegetables: principles, applications, and future directions

Neural network with k-fold cross validation for oil palm fruit ripeness prediction

Sensors for Non‐Destructive Quality Evaluation of Food

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