A method to construct fruit maturity color scales based on support machines for regression: Application to olives and grape seeds

Avila, Felipe; Mora, Marco; Oyarce, Miguel; Zuñiga, Alex; Fredes, Claudio

doi:10.1016/j.jfoodeng.2015.03.035

Cited by 44 publications

(23 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Furthermore, computer vision technology requires some very expensive hardware, such as cameras with a diffuse-light anti-shadow chamber, as well as specific software to define the representative seed colour (Rodríguez-Pulido et al, 2012b). A recent series of studies developed at the Universidad Catolica del Maule (Chile) (Ávila et al, 2014;Oyarce, 2014;Zuñiga et al, 2014;Ávila et al, 2015) proposed a simple computing methodology to capture images with a simple scanner and a digital seed colour scale to define seed colours by image treatment and pattern recognition (Gevers & Smeulders, 1999). However, this methodology has not yet been associated with the chemical parameters usually employed in assessing grape ripening.…”

Section: Introductionmentioning

confidence: 99%

An Analysis of Seed Colour During Ripening of Cabernet Sauvignon Grapes

Fredes

Mora

Carrasco-Benavides

2017

SAJEV

View full text Add to dashboard Cite

This case study examines seed colour during ripening using an exhaustive colorimetric analysis. The aim was to associate the chemical ripening with the seed colour in order to estimate the ripening stages of Cabernet Sauvignon grapes. Cluster samples, obtained from a vertical shoot-positioned vineyard in central Chile, were monitored for their technological and phenolic maturity, as well as for the colorimetric and chemical characteristics of the seeds. The colours of the scanned seed images were determined by human as well as by computer vision. In the first case, an expert assigned a colour to each seed part. In the second case, a computer program estimated the colours of the scanned seed images. An exhaustive analysis of seed colour was proposed, instead of a general observation of seed browning. The seed colour presented a wide range of colours, from moss green to dark brown, depending on the maturity and the face observed. The ripening stages identified, along with the chemical and colorimetric information gathered, were under ripe seed (brown with green traces), ripe seed (dark brown with green traces) and overripe seed (dark brown without any green traces). A new way to quantify seed colour is shown in this paper.

show abstract

Section: Introductionmentioning

confidence: 99%

An Analysis of Seed Colour During Ripening of Cabernet Sauvignon Grapes

Fredes

Mora

Carrasco-Benavides

2017

SAJEV

View full text Add to dashboard Cite

show abstract

“…A support vector machine (SVM) is a supervised learning algorithm that is mostly used for visual pattern recognition and image classification [22,23]. The objective of SVM classifier [24] is hyper plane classifier, which determines an optimal line to separate the training set of the two classes.…”

Section: Model Constructionmentioning

confidence: 99%

Quality grading of soybean seeds using image analysis

Jitanan¹,

Chimlek²

2019

IJECE

View full text Add to dashboard Cite

<span>Image processing and machine learning technique are modified to use the quality grading of soybean seeds. Due to quality grading is a very important process for the soybean industry and soybean farmers. There are still some critical problems that need to be overcome. Therefore, the key contributions of this paper are first, a method to eliminate shadow noise for segment soybean seeds of high quality. Second, a novel approach for color feature which robust for illumination changes to reduces problem of color difference. Third, an approach to discover a set of feature and to form classifier model to strengthen the discrimination power for soybean classification. This study used background subtraction to reduce shadow appearing in the captured image and proposed a method to extract color feature based on robustness for illumination changes which was H components in HSI model. We proposed classifier model using combination of the color histogram of H components in HSI model and GLCM statistics to represent the color and texture features to strengthen the discrimination power of soybean grading and to solve shape variance in each soybean seeds class. SVM classifiers are generated to identify normal seeds, purple seeds, green seeds, wrinkled seeds, and other seed types. We conducted experiments on a dataset composed of 1,320 soybean seeds and 6,600 seed images with varies in brightness levels. The experimental results achieved accuracies of 99.2%, 97.9%, 100%, 100%, 98.1%, and 100% for overall seeds, normal seeds, purple seeds, green seeds, wrinkled seeds, and other seeds, respectively</span>

show abstract

“…With the focus on fruit quality and shelf life, a number of methods have been developed to analyze fruit maturity, mainly divided into computer vision and biological characteristics measurement. The computer vision is an intuitive detection method for fruit maturity (Avila, Mora, Oyarce, Zuniga, & Fredes, 2015; Siswantoro, Arwoko, & Widiasri, 2020). Through image collection and processing, the surface information of fruit, such as size, color, volume, and shape can be extracted to evaluate the maturity (Chen et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Identification of tomato maturity based on multinomial logistic regression with kernel clustering by integrating color moments and physicochemical indices

Jiang

Bian

Wang

et al. 2020

J Food Process Engineering

View full text Add to dashboard Cite

The identification of tomato maturity is significant to extend the fruit shelf life and generate the scientific processing strategy. Tomato maturation is a gradual process, and the internal physicochemical characteristics are most related to maturity states. Merely choosing visual features to identify maturity would cause discriminant errors. This study designed a simple and effective identification method for tomato maturity by integrating color moments and physicochemical indices. The color moments were extracted by an adaptive K-means clustering image processing program, and firmness, soluble solid content and sensory evaluation were measured by professional techniques. The optimal multidimensional index set was formulated according to color moments and physicochemical indices simultaneously. To reduce the confusion between adjacent stages, a novel multinomial logistic regression with kernel clustering (MLRKC) method was designed to identify maturity, and the accuracy was 95.83% for tomato testing set. Moreover, the traditional image features set and some classic methods were applied to verify the performance of proposed method, respectively. Finally, the proposed method was applied to identify the tomatoes in the realistic circumstance. The identification results demonstrated satisfactory performances and promising applications of MLRKC method integrating color moments and physicochemical indices. Practical Applications Tomato is a climacteric fruit which could mature after harvesting. Identification tomato maturity stage is significant to decide the optimal transportation modes, inventory strategies and processing technology. Traditional methods for identifying tomato maturity were high-cost and complicated, which were inefficient for small-scale production. The method proposed in this study could simply the identification steps and reduce the operating cost, and also provide more accurate and valuable information. The investigated theoretical basis could be incorporated into the small farmers and small-scale food processing companies to achieve tomato precision processing with low additional costs. 1 | INTRODUCTION Tomato is rich in nutrients such as vitamins, carotenes, dietary fiber, and is an essential part of human daily diets (Feng, Zhang, Adhikari, & Guo, 2019). As one of the climacteric fruits, tomato has the postharvest maturity characteristics, that is, maturing after harvesting

show abstract

A method to construct fruit maturity color scales based on support machines for regression: Application to olives and grape seeds

Cited by 44 publications

References 26 publications

An Analysis of Seed Colour During Ripening of Cabernet Sauvignon Grapes

An Analysis of Seed Colour During Ripening of Cabernet Sauvignon Grapes

Quality grading of soybean seeds using image analysis

Identification of tomato maturity based on multinomial logistic regression with kernel clustering by integrating color moments and physicochemical indices

Contact Info

Product

Resources

About