Automated cashew kernel grading using machine vision

Aran, M O; Nath, Aneesh G; Shyna, A

doi:10.1109/icngis.2016.7854063

Cited by 16 publications

(8 citation statements)

References 10 publications

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“…The overall minimum accuracy of all the models was 95.1%. A study was done by Aran et al, (2016) to find out the effect of different features in the grading of the cashew kernel. In this study, colour, texture, shape and size features were extracted and tested on five different classification algorithms (i.e., Random Forest, Multilayer Perception, Multi-class classifier, Regression and Backpropagation Neural Network (BPNN)).…”

Section: Figure 1 Cashew Production In Tanzania Between 2014 and 2020...mentioning

confidence: 99%

“…Different colour features were extracted from the captured images and the features that were extracted in this experiment include the means (μ), standard deviations (σ), and skewness (γ) of the red (R), green (G) and blue (B) channels from the RGB colour space, the hue (H), saturated (S) and value (V) channels from the HSV colour space, the excess blue (2B-G-R), excess green (2G-R-B) and excess red (2R-G-B). The mean (μ), standard deviation (σ), and skewness (γ) of the pixels in the image were calculated using the following equations, respectively (Aran et al, 2016).…”

Section: Feature Extraction and Feature Selectionmentioning

confidence: 99%

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Traditional Machine Learning-Based Classification of Cashew Kernels Using Colour Features

BAİTU

GADALLA

Öztekin

2023

Tekirdağ Ziraat Fakültesi Dergisi

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Cashew is one of the major commercial commodities contributing to the national economy of Tanzania as foreign revenue. And yet still the processing of cashew is run locally using manual labour for a big part. If processed well under ideal conditions, cashews kernels are expected to be white in colour. But due to various factors like prolonged roasting in the steam chambers or over-drying, some cashew kernels tend to have a slight brown colour, and these are referred to as scorched cashews. Despite sharing the same characteristics with white cashew kernels, including nutritional quality, these cashew kernels are supposed to be graded differently. In many places around the world, particularly in Tanzania, the sorting and grading process of cashew kernels is performed by hand. In international trade, cashew grading is very important and this means more effective and consistent methods need to be applied in this stage of production in order to increase the quality of the products. The objective of this study was to evaluate the use of traditional Machine Learning techniques in the classification of cashew kernels as white or scorched by using colour features. In this experiment, various colour features were extracted from the images. The extracted features include the means (μ), standard deviations (σ), and skewness (γ) of the channels in RGB and HSV colour spaces. The relevant features for this classification problem were selected by applying the wrapper approach using the Boruta Library in Python, and the irrelevant ones were removed. 5 models are studied and their efficiencies analysed. The studied models are Logistic Regression, Decision Tree, Random Forest, Support Vector Machine and K-Nearest Neighbour. The Decision Tree model recorded the least accuracy of 98.4%. The maximum accuracy of 99.8% was obtained in the Random Forest model with 100 trees. Due to simplicity in application and high accuracy, the Random Forest is recommended as the best model from this study.

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Section: Figure 1 Cashew Production In Tanzania Between 2014 and 2020...mentioning

confidence: 99%

Section: Feature Extraction and Feature Selectionmentioning

confidence: 99%

Traditional Machine Learning-Based Classification of Cashew Kernels Using Colour Features

BAİTU

GADALLA

Öztekin

2023

Tekirdağ Ziraat Fakültesi Dergisi

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“…Overall, higher accuracy and lesser execution time were found for the SGD model and SVM model respectively. In the study, 64 an automated system was proposed for cashew nuts grading according to their texture, color, size, and shape features. Five ML classifiers were deployed to compare the classification performance.…”

Section: Related Workmentioning

confidence: 99%

Contemporary machine learning applications in agriculture: Quo Vadis?

Mahmood

Tiwari

Singh

et al. 2022

Concurrency and Computation

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Agricultural automation is an emerging subject today to accomplish the food demands of individuals across the globe. Machine learning is one such agricultural automation tool that has been adopted briskly in the recent decade due to its ability to process countless input data and handle non-linear tasks. Availability and continuous development of agricultural data led the machine learning pervasive in multiple aspects of agriculture. This paper systematically analyses and summarizes the 81 quality research efforts published in the past decade dedicated to the various contemporary machine learning applications in agriculture and food production systems. We examined and categorized each agricultural problem under study into four categories and each category into its subcategories. The finding demonstrates the contemporary applications of machine learning in broad agricultural subcategories and determines where it is heading shortly; based upon contributions of researchers, utilization of machine learning models/algorithms, and the availability of agricultural datasets.Through the analysis, it is discovered that the current innovation can help the improvement of agricultural automation to accomplish the advantages of minimal cost, high efficiency, and better precision. This paper can serve as an investigatory guide for researchers, academicians, engineers, and manufacturers to understand and apply modern and upgraded cognitive technologies to each subcategory of the agricultural sector.

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“…The simplest methods of classifying cashew kernels are based on their color or texture and a single layer neural network [20,21]. Aran et al [22] extended this work by testing external features like color, texture, shape and size. In addition, they analyzed the impact of some image preprocessing methods on cashew nut classification.…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach to Cashew Nut Detection in Packaging and Quality Inspection Lines

Pham¹,

Nguyen²,

Pham³

2022

IJACSA

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YOLO standing for You Only Look Once is one of the most famous algorithms in computer vision used for detecting objects in a real-time environment. The newest version of this algorithm, namely YOLO with the seventh version or YOLOv7, is proposed in the present study for cashew nut detection (good, broken and not peeled) in packaging and quality inspection lines. Furthermore, this algorithm using an efficient convolutional neural network (CNN) to be able to successfully detect and identify unsatisfactory cashew nuts, such as chipped or burnt cashews. In order to deal with the quality inspection process, a new dataset called CASHEW dataset has been built at first by collecting cashew images in environments with different brightness and camera angles to ensure the model's effectiveness. The quality inspection of cashew nuts is tested with a huge number of YOLOv7 models and their effectiveness will also be evaluated. The experimental results show that all models are able to obtain high accuracy. Among them, the YOLOv7-tiny model employs the least number of parameters, i.e. 6.2M but has many output parameters with higher accuracy than that of some other YOLO models. As a result, the proposed approach should clearly be one of the most feasible solutions for the cashew's quality inspection.

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Automated cashew kernel grading using machine vision

Cited by 16 publications

References 10 publications

Traditional Machine Learning-Based Classification of Cashew Kernels Using Colour Features

Traditional Machine Learning-Based Classification of Cashew Kernels Using Colour Features

Contemporary machine learning applications in agriculture: Quo Vadis?

A Novel Approach to Cashew Nut Detection in Packaging and Quality Inspection Lines

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