Artificial Vision Techniques to Optimize Strawberry's Industrial Classification

Constante, Patricia; Gordon, A.; Chang, Oscar; Pruna, Edwin P.; Acuna, Fausto; Escobar, Ivón

doi:10.1109/tla.2016.7555221

Cited by 31 publications

(9 citation statements)

References 7 publications

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“…This has the characteristic that emulates the functionality of human vision and allows spatial and optical information from the captured image of the sample. Different investigations have been reported with VC focused on determining the degrees of ripeness of various fruits such as persimmon, strawberries, pomegranate, and tomato [33][34][35]. The present work aims to implement an artificial vision system that automatically describes the ripeness levels of the bell pepper.…”

Section: Introductionmentioning

confidence: 99%

A Maturity Estimation of Bell Pepper (Capsicum annuum L.) by Artificial Vision System for Quality Control

et al. 2020

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Sweet bell peppers are a Solanaceous fruit belonging to the Capsicum annuum L. species whose consumption is popular in world gastronomy due to its wide variety of colors (ranging green, yellow, orange, red, and purple), shapes, and sizes and the absence of spicy flavor. In addition, these fruits have a characteristic flavor and nutritional attributes that include ascorbic acid, polyphenols, and carotenoids. A quality criterion for the harvest of this fruit is maturity; this attribute is visually determined by the consumer when verifying the color of the fruit’s pericarp. The present work proposes an artificial vision system that automatically describes ripeness levels of the bell pepper and compares the Fuzzy logic (FL) and Neuronal Networks for the classification stage. In this investigation, maturity stages of bell peppers were referenced by measuring total soluble solids (TSS), ° Brix, using refractometry. The proposed method was integrated in four stages. The first one consists in the image acquisition of five views using the Raspberry Pi 5 Megapixel camera. The second one is the segmentation of acquired image samples, where background and noise are removed from each image. The third phase is the segmentation of the regions of interest (green, yellow, orange and red) using the connect components algorithm to select areas. The last phase is the classification, which outputs the maturity stage. The classificatory was designed using Matlab’s Fuzzy Logic Toolbox and Deep Learning Toolbox. Its implementation was carried out onto Raspberry Pi platform. It tested the maturity classifier models using neural networks (RBF-ANN) and fuzzy logic models (ANFIS) with an accuracy of 100% and 88%, respectively. Finally, it was constructed with a content of ° Brix prediction model with small improvements regarding the state of art.

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Section: Introductionmentioning

confidence: 99%

A Maturity Estimation of Bell Pepper (Capsicum annuum L.) by Artificial Vision System for Quality Control

et al. 2020

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“…Usage of deep learning is also described in the research made by Constante et al (2016), who used a three-layer neural network with input through backpropagation. In this paper, this method was used to sort strawberries and obtained recognition results of 92.5% in the "Extra" category; 90% in the "Consumption" category; 90% in the "Raw material" category; and 100% in the "Alien objects" category.…”

Section: Related Workmentioning

confidence: 99%

Segmentation and detection of cattle branding images using CNN and SVM classification

Silva

Weber

Belloni

2019

ADCAIJ

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This article presents a hybrid method that uses Convolutional Neural Networks (CNN) to segmentation and Support Vector Machines (SVM) to detection the brandings. The experiments were performed using a cattle branding images. Metrics of Overall Accuracy, Recall, Precision, Kappa Coefficient, and Processing Time were used in order to assess the proposed tool. The results obtained here were satisfactory, reaching a Overall Accuracy of 93% in the first experiment with 39 brandings and 1,950 sample images, and 95% of accuracy in the second experiment, with the same 39 brandings, but with 2,730 sample images. The processing time attained in the experiments was 32s and 42s, respectively.

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“…There is no optimal methodology for the correct identification of the colors belonging to the images and/or videos captured with a camera sensor without knowledge of the characteristics of the lighting source in the scene to be captured. Color identification presents a challenge in artificial vision—the discipline that tries to emulate the human vision, simultaneously complementing it through artificial systems [1]—because, through the appropriate techniques, it allows the processing and analysis of the information obtained from a digital image. This is true in real‐world scenarios.…”

Section: Introductionmentioning

confidence: 99%

Lighting source classification applied in color images to contrast enhancement

Dehesa‐González

Rosales

Gallegos‐Funes

et al. 2020

Color Research & Application

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A novel methodology is proposed to identify the distribution of the intensity of the illuminant across a real‐world image; with this information, the image can be classified as a homogeneous or nonhomogeneous lighting source. A criterion is proposed for deciding whether the illumination in the tested image is homogeneous or nonhomogeneous. This criterion is based on well‐established methods that are dedicated to improving color characteristics and are commonly used to determine the color of the lighting source during a captured scene. The classification of homogeneous or nonhomogeneous illumination in real‐world images is implemented in the BSD500 database, and the results are compared with a manual classification of images—proposed under subjective criteria—by the authors; in this way, one can analyze how the lighting source affects the image. This procedure provides a classification methodology for real‐world image databases found in the scientific literature.

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Artificial Vision Techniques to Optimize Strawberry's Industrial Classification

Cited by 31 publications

References 7 publications

A Maturity Estimation of Bell Pepper (Capsicum annuum L.) by Artificial Vision System for Quality Control

A Maturity Estimation of Bell Pepper (Capsicum annuum L.) by Artificial Vision System for Quality Control

Segmentation and detection of cattle branding images using CNN and SVM classification

Lighting source classification applied in color images to contrast enhancement

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