Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review

Zhang, Baohua; Huang, Wenqian; Li, Jiangbo; Zhao, Chunjiang; Fan, Shuxiang; Wu, Jitao; Liu, Chengliang

doi:10.1016/j.foodres.2014.03.012

Cited by 372 publications

(202 citation statements)

References 125 publications

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“…Region oriented segmented algorithm identify the peel defects of citrus fruit using computer vision [28] Automatic classification of fruit defect based on cooccurrence matrix and Neural Network (2015) by Giacomo [29] reported by using radial Basis probabilistic neural network classify the external defect of mango using hue and saturation histogram for ground region identification. Gray level co-occurrence matrixes for the quality of orange .there are 400 samples of different defects like stabbing wounds, bruise, abrasion; sunburn, injury, and hail to damage are identified.…”

Section: Defect Detectionmentioning

confidence: 99%

A Literature Survey on Methodologies for Classification, Maturity Detection, Defect Identification and Grading of Fruits

Reshma¹,

Sreekumar²

2018

IJCA

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The agricultural sector is the primary and unavoidable sector in Kerala .Kerala is a green state .in here wide variety of trees and plants are present. There are different species of trees and few of them are dig for fruits. Fruit has been accepted as a good source of vitamins, minerals and fibers. Most commonly used fruits are mongo, jack fruit, banana etc.This work gives as the review of the fruit Classification, grading, maturity identification and defect detection. Image acquisition performed with digital camera. Fruits are classified based on different features like size, color, texture, etc … the presence of defects on the fruits affects the market value of the product. Now fruit quality estimation using machine vision in Kerala is ongoing.

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Section: Defect Detectionmentioning

confidence: 99%

A Literature Survey on Methodologies for Classification, Maturity Detection, Defect Identification and Grading of Fruits

Reshma¹,

Sreekumar²

2018

IJCA

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“…Furthermore the computational time to develop the prediction model increases considerably [99]. The development of an MSI system can reduce these drawbacks, mainly due to its possibility to select the most significative wavelengths (from 3-15) in order to predict the physicochemical attributes of interest [137]. MSI has several advantages compared to HSI (i.e., faster scan rate, feasibility of on-line application in the food processing industry, less computer memory required to acquire and process the images) [138].…”

Section: Hyper-and Multi-spectral Imagingmentioning

confidence: 99%

Monitoring and Optimization of the Process of Drying Fruits and Vegetables Using Computer Vision: A Review

et al. 2017

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An overview is given regarding the most recent use of non-destructive techniques during drying used to monitor quality changes in fruits and vegetables. Quality changes were commonly investigated in order to improve the sensory properties (i.e., appearance, texture, flavor and aroma), nutritive values, chemical constituents and mechanical properties of drying products. The application of single-point spectroscopy coupled with drying was discussed by virtue of its potentiality to improve the overall efficiency of the process. With a similar purpose, the implementation of a machine vision (MV) system used to inspect foods during drying was investigated; MV, indeed, can easily monitor physical changes (e.g., color, size, texture and shape) in fruits and vegetables during the drying process. Hyperspectral imaging spectroscopy is a sophisticated technology since it is able to combine the advantages of spectroscopy and machine vision. As a consequence, its application to drying of fruits and vegetables was reviewed. Finally, attention was focused on the implementation of sensors in an on-line process based on the technologies mentioned above. This is a necessary step in order to turn the conventional dryer into a smart dryer, which is a more sustainable way to produce high quality dried fruits and vegetables.

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“…Several methods have been commonly applied in the field of the evaluation of preservation quality, such as wavelet domain, analytic hierarchy process and principal component analysis, quantum genetic fuzzy neural network, and particle clustering [15][16][17][18][19][20][21][22][23] . They were used to detect agricultural and livestock products.…”

Section: Introductionmentioning

confidence: 99%

Cucumber appearance quality detection under complex background based on image processing

Liu

Niu

2018

International Journal of Agricultural and Biological Engineering

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Abstract:Cucumber fruit appearance quality is an important basis of growth status. In order to improve the quality detection accuracy and processing efficiency of cucumber color image under complicated background, an improved GrabCut algorithm was proposed to extract the cucumber boundary. Firstly, including pixel size normalization, rectangular box set and scale image resolution, pretreatments of cucumber image were adopted to reduce the iteration times and operation time of GrabCut algorithm. Then, the Gaussian mixture model was chosen to find out the possible prospect of target region and background region in the preprocessed rectangular frame on the preliminary modeling. Meanwhile, by the optimization of K-means cluster to the initial GMM model, the effective target area was extracted. Finally, the whole image noise and serrated boundary was removed by morphological operations to segment the outline of the complete target prospects with appropriate structure size. And then the cucumber appearance quality detection instrument was designed to extract the texture and shape features exactly, so that it could obtain cucumber appearance quality and evaluate its growth effectively. With the segmentation experiments by almost 300 cucumber original images from greenhouse in Shandong Province, the results showed that the improved GrabCut algorithm could effectively extract the complete and smooth boundary of cucumber. With relatively high segmentation evaluation index, the precision was 93.88%, the recall rate was 99.35%, the F-Measure reached 96.53%, and the misclassification error was controlled at minimum 5.84%. The average running time was shortened to 1.4023 s. The comparison results showed that the improved GrabCut algorithm was the best, followed by the SLIC and traditional GrabCut method. Cucumber appearance quality detection instrument could also extract more accurate feature parameters. And it could meet the basic growth condition assessment by automatic image processing. Keywords: cucumber, complicated background, quality detection, image processing, GrabCut DOI: 10.25165/j.ijabe.20181104.3090Citation: Ye H J, Liu C Q, Niu P Y. Cucumber appearance quality detection under complex background based on image processing. Int J Agric & Biol Eng, 2018; 11(4): 193-199.

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Principles, developments and applications of computer vision for external quality inspection of fruits and vegetables: A review

Cited by 372 publications

References 125 publications

A Literature Survey on Methodologies for Classification, Maturity Detection, Defect Identification and Grading of Fruits

A Literature Survey on Methodologies for Classification, Maturity Detection, Defect Identification and Grading of Fruits

Monitoring and Optimization of the Process of Drying Fruits and Vegetables Using Computer Vision: A Review

Cucumber appearance quality detection under complex background based on image processing

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