Image processing technique to estimate geometric parameters and volume of selected dry beans

Kumar, Mahesh; Bora, Ganesh C.; Lin, Dongqing

doi:10.1007/s11694-013-9142-7

Cited by 16 publications

(8 citation statements)

References 11 publications

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“…The maximum value was selected as the diameter of fruit (D). The area, volume, and sphericity of the sweet lemon calculated as follows (Iqbal et al, 2015;Kumar et al, 2013):…”

Section: Image Processing (Shape Change)mentioning

confidence: 99%

Effect of active coating containing radish leaf extract with or without vacuum packaging on the postharvest changes of sweet lemon during cold storage

Zandi

Ganjloo

Bimakr

et al. 2021

J. Food Process. Preserv.

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Sweet lemon (Citrus limetta) is yellow and thin skin fruit cultivated in tropical countries (Karaminia et al., 2019). This fruit is high in vitamin C (50 mg/100 g juice) which is used to treat common colds, influenza, and hypertension. Sweet lemon is quite perishable due to postharvest losses such as weight loss, physiological deterioration, decay, and softening texture (Barreca et al., 2011). Storage conditions such as storage temperature affect sweet lemon quality, freshness, and shelf life. Furthermore, physicochemical changes can reduce storage time and the market value of sweet lemon (Singh et al., 2004). Various postharvest techniques have been evaluated for prolonging the shelf life of fresh agricultural products. These techniques control the biological and environmental factors and increase the quality of the fruits (de Jesús Salas-Méndez et al., 2019). Edible coatings were used to extend the shelf life of fruits and vegetables (Gomes et al., 2017; Ju et al., 2019; Kingwascharapong et al., 2020). The application of edible coatings as a new postharvest technique is simple, environmentally friendly and nontoxic. Edible coatings can minimize the natural physiological ripening of fruits through preventing moisture and gas transfer (Chen et al., 2019; Morsy & Rayan, 2019). In the past few decades, several studies

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“…The maximum value was selected as the diameter of fruit (D). The area, volume, and sphericity of the sweet lemon calculated as follows (Iqbal et al, 2015;Kumar et al, 2013):…”

Section: Image Processing (Shape Change)mentioning

confidence: 99%

Effect of active coating containing radish leaf extract with or without vacuum packaging on the postharvest changes of sweet lemon during cold storage

Zandi

Ganjloo

Bimakr

et al. 2021

J. Food Process. Preserv.

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“…object) inspection, classification, and sorting or grading (Sun, 2008). Computer vision systems have been effectively used to classify or to recognize quality parameters like color and size in several agricultural and food commodities including dry beans (Kumar, Bora, & Lin, 2013), coffee (Soedibyo et al, 2010), soya beans seeds (Namias et al, 2012), peanuts (Chen et al, 2011) andbrazilnuts (Castelo-Quispe et al, 2013) advancement in hardware and image processing makes computer vision a very popular technique for automatic cashew kernel quality inspection of parameters like geometrical and color.…”

Section: Introductionmentioning

confidence: 99%

<b>Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networks

Ganganagowdar

Siddaramappa

2016

Acta Sci. Agron.

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ABSTRACT.A novel intelligent automated model to recognize and classify a cashew kernels using Artificial Neural Network (ANN). The model primarily intends to work on two phases. The phase one, built with a proposed method to extract features, which includes 16 morphological features and also 24 color features from the input cashew kernel images. In phase two, a Multilayer Perceptron ANN is being used to recognize and classify the given white wholes grades using back propagation learning algorithm. The proposed method achieves a classification accuracy of 88.93%. This study also reveals that the combination of morphological and color features outperforms rather using any one set of features separately to grade cashew kernels.Keywords: White Wholes (WW) grade cashew kernel images, feature extraction, artificial neural networks, classification.Detectar e classificar castanhas de caju, tipo inteiro branco, através de rede neural artificial RESUMO. Analisa-se um novo modelo automatizado para detectar e classificar castanhas de caju por uma rede neural artificial. O modelo funciona em duas frases. Fase 1 é construída para detectar as características, as quais incluem 16 fatores morfológicos e 24 características de cor a partir de imagens da castanha do caju. Na Fase 2 emprega-se a rede neural artificial multicamada para detectar e classificar os graus inteiros brancos por algoritmo de propagação reversa. O método classifica com uma exatidão de 88,93%. Essa investigação também revela que a combinação das características morfológicas e de cor vai além de que quando se usa uma série de características separadas para classificar as castanhas de caju.Palavras-chave: imagens de castanhas de caju serie brancos inteiros, características de extração, rede neural artificial, classificação.

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“…Thousand grains weight was computed by weighing 100 kernels using electronic weighing scale (Citizen CY220, USA) with least count 0.001 and then estimated mass of 1000 kernels by factor of 10. This is usually denoted by gm per 1000 kernels 46 , 47 .…”

Section: Methodsmentioning

confidence: 99%

Image processing techniques to estimate weight and morphological parameters for selected wheat refractions

Sharma

Kumar

Alam

2021

Sci Rep

Self Cite

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The geometric and color features of agricultural material along with related physical properties are critical to characterize and express its physical quality. The experiments were conducted to classify the physical characteristics like size, shape, color and texture and then workout the relationship between manual observations and using image processing techniques for weight and volume of the four wheat refractions i.e. sound, damaged, shriveled and broken grains of wheat variety PBW 725. A flatbed scanner was used to acquire the images and digital image processing method was used to process the images and output of image analysis was compared with the actual measurements data using digital vernier caliper. A linear relationship was observed between the axial dimensions of refractions between manual measurement and image processing method with R2 in the range of 0.798–0.947. The individual kernel weight and thousand grain weight of the refractions were observed to be in the range of 0.021–0.045 and 12.56–46.32 g respectively. Another linear relationship was found between individual kernel weight and projected area estimated using image processing methodology with R2 in the range of 0.841–0.920. The sphericity of the refractions varied in the range of 0.52–0.71. Analyses of the captured images suggest ellipsoid shape with convex geometry while the same observation was recorded by physical measurements also. A linear relationship was observed between the volume of refractions derived from measured dimensions and calculated from image with R2 in the range of 0.845–0.945. Various color and grey level co-variance matrix texture features were extracted from acquired images using the open-source Python programming language and OpenCV library which can exploit different machine and deep learning algorithms to properly classify these refractions.

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Image processing technique to estimate geometric parameters and volume of selected dry beans

Cited by 16 publications

References 11 publications

Effect of active coating containing radish leaf extract with or without vacuum packaging on the postharvest changes of sweet lemon during cold storage

Effect of active coating containing radish leaf extract with or without vacuum packaging on the postharvest changes of sweet lemon during cold storage

<b>Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networks

Image processing techniques to estimate weight and morphological parameters for selected wheat refractions

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