A Grading Method for Mangoes on the  Basis of Peel Color Measurement  Using a Computer Vision System

Makino, Yoshio; Goto, Kenjiro; Oshita, Seiichi; Sato, Akari; Tsukada, M.

doi:10.4236/as.2016.76033

Cited by 10 publications

(7 citation statements)

References 13 publications

(18 reference statements)

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“…The experimental setup is designed to capture the mango images without background and a pixel count based red area share has been estimated. However, the method is limited to the particular fruit pattern and cannot work with the complex fruit peel properties [196]. A citrus crop estimation is performed by using water shedding segmentation and distance transform and marker controller.…”

Section: Light Colour and Shift Invariantmentioning

confidence: 99%

A comprehensive review of fruit and vegetable classification techniques

Hameed

Chai

Rassau

2018

Image and Vision Computing

150

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Recent advancements in computer vision have enabled wide-ranging applications in every field of life. One such application area is fresh produce classification, but the classification of fruit and vegetable has proven to be a complex problem and needs to be further developed. Fruit and vegetable classification presents significant challenges due to interclass similarities and irregular intraclass characteristics. Selection of appropriate data acquisition sensors and feature representation approach is also crucial due to the huge diversity of the field. Fruit and vegetable classification methods have been developed for quality assessment and robotic harvesting but the current state-ofthe-art has been developed for limited classes and small datasets. The problem is of a multi-dimensional nature and offers significantly hyperdimensional features, which is one of the major challenges with current machine learning approaches. Substantial research has been conducted for the design and analysis of classifiers for hyperdimensional features which require significant computational power to optimise with such features. In recent years numerous machine learning techniques for example, Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Decision Trees, Artificial Neural Networks (ANN) and Convolutional Neural Networks (CNN) have been exploited with many different feature description methods for fruit and vegetable classification in many real-life applications. This paper presents a critical comparison of different state-of-the-art computer vision methods proposed by researchers for classifying fruit and vegetable.

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Section: Light Colour and Shift Invariantmentioning

confidence: 99%

A comprehensive review of fruit and vegetable classification techniques

Hameed

Chai

Rassau

2018

Image and Vision Computing

150

View full text Add to dashboard Cite

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“…The value of the h o with red at 0 o , yellow at 60 o , and green at 120 o was used to express the color of the vegetable soybeans according to the method published by Makino et al (2016). A computer vision system (FMVU-13S2C-CS, Point Grey Research Inc., Richmond, British Columbia, Canada) was used to capture photographs for the vegetable soybeans, and the data were calculated using the MATLAB (2018a) (Mathworks, Inc., Natick, MA, U.S.A.) software (12 samples for vegetable soybeans and 10 for seeds).…”

Section: Materials Preparationmentioning

confidence: 99%

Nondestructive Detection of Decay in Vegetable Soybeans Stored at Different Temperatures Using Chlorophyll Fluorescence Imaging

Makino

Duan

et al. 2020

Environmental Control in Biology

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To achieve nondestructive evaluation of the quality of vegetable soybeans, we have applied chlorophyll fluorescence imaging, which has been used as a non-invasive technology to investigate the ripening and senescence of horticultural products. This study measured changes in chlorophyll content, color (h o), and chlorophyll fluorescence of vegetable soybean pods and seeds and rarely used a nondestructive approach to explore interior quality L-ascorbic acid content. The relationship between chlorophyll fluorescence parameters and visual quality attribute h o , and that between chlorophyll fluorescence parameters and L-ascorbic acid content were performed using partial least square regression (PLSR). Thus, the fluorescence parameters Fm, Fv, Fq_Lss, QY_Lss, and qL_Lss, which make larger contributions to evaluating h o in both whole vegetable soybeans and seeds were selected from 98 chlorophyll fluorescence parameters. Moreover, parameters Fv, Fq_Lss, QY_Lss, qP_Lss, and qL_Lss in seeds, which are important for estimating L-ascorbic acid of seeds were also determined. Most importantly, parameters Fv_Lss and Ft_Lss in whole vegetable soybeans, which may be possible to predict L-ascorbic acid content of seeds by a nondestructive detection. The chlorophyll fluorescence technique presents great potential in examining freshness and estimating the decay in vegetable soybeans.

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“…Mung beans sprouts were shot on 0 and 6 d to identify the form of the samples using the same computer vision system (FMVU-13S2C-CS, Point Grey Research Inc., Richmond, British Columbia, Canada) as the previous report [23]. A clear outline of a sample was traced utilizing the outline detection filter of Adobe Photoshop ver.…”

Section: Shooting Of Mung Bean Sprouts Using Computer Vision Systemmentioning

confidence: 99%

Efficient preservation of sprouting vegetables under simulated microgravity conditions

et al. 2020

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The effectiveness of a simulated microgravity environment as a novel method for preserving the freshness of vegetables was investigated. Three types of vegetables were selected: vegetable soybean, mung bean sprouts, and white radish sprouts. These selected vegetables were fixed on a three-dimensional rotary gravity controller, rotated slowly. The selected vegetables were stored at 25˚C and 66% of relative humidity for 9, 6, or 5 d while undergoing this process. The simulated microgravity was controlled utilizing a gravity controller around 0 m s −2. The mung bean sprouts stored for 6 d under simulated microgravity conditions maintained higher thickness levels than the vegetable samples stored under normal gravity conditions (9.8 m s −2) for the same duration. The mass of all three items decreased with time without regard to the gravity environment, though the samples stored within the simulated microgravity environment displayed significant mass retention on and after 3 d for mung bean sprout samples and 1 d for white radish sprout samples. In contrast, the mass retention effect was not observed in the vegetable soybean samples. Hence, it was confirmed that the mass retention effect of microgravity was limited to sprout vegetables. As a result of analysis harnessing a mathematical model, assuming that the majority of the mass loss is due to moisture loss, a significant difference in mass reduction coefficient occurs among mung bean sprouts and white radish sprouts due to the microgravity environment, and the mass retention effect of simulated microgravity is quantitatively evaluated utilizing mathematical models. Simulated microgravity, which varies significantly from conventional refrigeration, ethylene control, and modified atmosphere, was demonstrated effective as a novel method for preserving and maintaining the freshness of sprout vegetables. This founding will support long-term space flight missions by prolonging shelf life of sprout vegetables.

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A Grading Method for Mangoes on the Basis of Peel Color Measurement Using a Computer Vision System

Cited by 10 publications

References 13 publications

A comprehensive review of fruit and vegetable classification techniques

A comprehensive review of fruit and vegetable classification techniques

Nondestructive Detection of Decay in Vegetable Soybeans Stored at Different Temperatures Using Chlorophyll Fluorescence Imaging

Efficient preservation of sprouting vegetables under simulated microgravity conditions

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