Cashews whole and splits classification using a novel machine vision approach

Sunoj, S.; Igathinathane, C.; Jenicka, S.

doi:10.1016/j.postharvbio.2017.12.006

Cited by 14 publications

(8 citation statements)

References 14 publications

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“…Various profile features were calculated from the grayscale values of the profile to test their suitability to discriminate particles by surface texture. These features included the gray maximum (G max ), gray mean (G mean ), gray standard deviation (G SD ), length of profile curve (L curve ), and area under the curve (A curve ) of the ROI were evaluated following the methodology of Sunoj, Igathinathane, and Jenicka (2018a). In addition to these features, gray skewness (G skew ), gray kurtosis (G kurt ) were also calculated to test its suitability on finding surface profile.…”

Section: Profile Plot Features Extraction and Analysismentioning

confidence: 99%

Impact of corn stover particle size and C/N ratio on reactor performance in solid-state anaerobic co-digestion with dairy manure

Ajayi-Banji

Rahman

Sunoj

et al. 2020

Journal of the Air & Waste Management Association

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Green energy generation from agricultural waste has the potential to minimize dependency on fossil and reduce the resultant environmental impact of this fuel provided anaerobic reactor performance is optimized. Hence, the interactive impact of carbon to nitrogen (C/N) ratio, particle size, and codigestion of dairy manure (DM) and corn stover (CS) on solid-state anaerobic digester (SSAD) performance was investigated with four treatments (DMCS24S, DMCS24L, DMCS28L, and DMCS32L) in this solid-state study. Novel scanning electron microscope (SEM) image analysis utilized to describe the corn stover using ImageJ indicated that corn stover of particle size 0.18-0.42 mm had lower rough surface texture relative to the 0.42-0.84 mm size. This observation not only influenced the ingestate degradation, the bioconversion rate was negatively affected by 0.18-0.42 mm particle size of corn stover. Notably, increase in C/N ratio led to decrease in total ammonia nitrogen (TAN) and alkalinity concentration (Alk), hence, treatments with the lowest C/N ratio had better reactor performance in terms of suitable process parameters such as Alk, pH, ORP, and TAN. Furthermore, DMCS24L treatment had the highest methane yield (106 mL/g VS) and net methane energy (2.92 MJ/kg). Interestingly, modified Gompertz model gave the best kinetic description of the methane production. This SSAD mesophilic study suggests that corn stover, with particle size of 0.42-0.84 mm, co-digested with dairy manure under a C/N ratio of 24 has the potential to enhance methane yield and optimize reactor performance.Implications: The utilization of agricultural waste for bioenergy generation through solid-state anaerobic digestion could be enhanced through the interactive impact of substrate particle size, carbon-to-nitrogen (C/N) ratio and co-digestion, which has not been previously studied. These ternary factors significantly improved reactor performance and enhanced methane yield when corn stover of 0.42-0.84 mm particle size was co-digested with dairy manure to achieve a C/N ratio of 24.

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Section: Profile Plot Features Extraction and Analysismentioning

confidence: 99%

Impact of corn stover particle size and C/N ratio on reactor performance in solid-state anaerobic co-digestion with dairy manure

Ajayi-Banji

Rahman

Sunoj

et al. 2020

Journal of the Air & Waste Management Association

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“…In another study, Thakkar et al (2011) implemented fuzzy logic‐based computer vision system for classification of whole cashew kernels and achieved 89% classification accuracy. However, in another study, Sunoj, Igathinathane, and Jenicka (2017) implemented a machine vision methodology of capturing cashew shadows and associated image processing ImageJ plugin to classify whole and split cashews and achieved a 100% classification accuracy. Furthermore, to better understand the effectiveness of the models, the overall F1‐score of each model was calculated using their precision and sensitivity values.…”

Section: Resultsmentioning

confidence: 99%

Classification of first quality fancy cashew kernels using four deep convolutional neural network models

Vidyarthi

Singh

Tiwari

et al. 2020

J Food Process Engineering

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In this study, we proposed deep convolutional neural networks (DCNNs) combined with image processing to classify cashew kernels in five categories based on the adulteration of first-class fancy whole cashew kernels with butts and pieces. Four DCNN models, including Inception-V3, ResNet50, VGG-16, and a custom model were implemented, and their performances were compared using model evaluators, such as sensitivity, specificity, precision, accuracy, and F1-score. Overall, all the models showed a high performance with a minimum accuracy of 95.1% and the training and validation data curves demonstrated a good fitting of the models. Although all the models demonstrated promising potential for cashew classification, Inception-V3 and ResNet50 neural networks delivered the most promising outcome with the highest accuracies (each 98.4%) and F1-scores (each 96%). Custom-built model showed the least accuracy (95.1%) and F1-score (87.9%). The findings of current work indicate that the developed DCNN models are capable of achieving automatic, fast, and accurate cashew classification. Practical application Currently, the sorting and grading of cashew kernel are mostly manual and timeconsuming process and the deep convolutional neural networks (DCNN) or Deep Learning implemented in this study can facilitate a speedy and automated cashew classification in cashew industry worldwide. 1 | INTRODUCTION 1.1 | General overview of cashew nuts and their geographical production/distribution Cashew (Anacardium occidentale L.) nuts belong to a tropical evergreen tree from the Anacardiaceae family of plants, which also includes mango, pistachio, poison oak, and poison ivy. They contain about 35-45% seeds and 55-65% shells by weight (Mgaya et al., 2019). Cashew nuts are kidney-shaped nuts which are rich in unsaturated fatty acids, high protein, and low levels of saturated fats and sugars (Rao, 1998). Cashew nuts are an important ingredient in snack and confectionary industries and are generally consumed in raw, roasted, salted or flavored forms (Papadenetriou, 1998). About 60% of the cashew kernels are consumed in the form of snacks and remaining 40% are included in other food preparations, mainly confectionary (Azam-Ali & Judge, 2001). In 2017, the worldwide production of raw cashew nuts was about 4 million metric tons and it is expected that cashew nuts will take over 29% of total nut market by 2021 (Yun, 2018). The main cashew production regions are Vietnam, India, Africa, and Philippines. Vietnam is considered the largest cashew exporter, followed by India, Netherlands, and African countries (Yun, 2018). Cashew nuts have an established market in the US with a wide range of uses and is considered the largest importer in the world accounting for about 35% of the total import (Azam-Ali & Judge, 2001).

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“…Consumers rated the white color, nut flavor, homogeneity and body in the middle of the scales (Figure 2). A low quality of cashew kernels is usually related to brown color and black spots in the kernels due to fungi or diseases (Sunoj et al, 2018), which gives a gray coloration to nut-based milks. Therefore, the small value observed for gray color (1.6) shows that although the kernels used for the formulation were broken, they still had good quality.…”

Section: Attribute Diagnosismentioning

confidence: 99%

Cashew nut-based beverage: development, characteristics and stability during refrigerated storage

et al. 2021

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A cashew nut-based beverage was obtained using broken cashew kernels and its composition and stability were evaluated during refrigerated storage for 60 days. Sensory tests were performed to define the formulation, testing the best proportion between kernels and water, the effect of kernel roasting, the ideal sweetness, the beverage attributes and the sensory acceptance. A cashew nut beverage can be obtained by grinding the kernel with water in the proportion of 1:10 (m/m) with added sugar at 3%, followed by heat treatment at 140ºC for four seconds. The proximate composition was 11.49% total solids, 0.26% ashes, 1.83% proteins, 3.97% lipids and 5.43% total carbohydrates. The pH remained above 6.49 and the color darkened during storage. The product was considered microbiologically safe and shelf-stable for at least 2 months, and sensory acceptance remained at an average value of 7.1 on a 9-point hedonic scale.

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Cashews whole and splits classification using a novel machine vision approach

Cited by 14 publications

References 14 publications

Impact of corn stover particle size and C/N ratio on reactor performance in solid-state anaerobic co-digestion with dairy manure

Impact of corn stover particle size and C/N ratio on reactor performance in solid-state anaerobic co-digestion with dairy manure

Classification of first quality fancy cashew kernels using four deep convolutional neural network models

Cashew nut-based beverage: development, characteristics and stability during refrigerated storage

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