Classification of Pepper Seeds Using Data Mining Algorithms

Dioses, Jesusimo L.

doi:10.30534/ijatcse/2020/84922020

Cited by 2 publications

(4 citation statements)

References 7 publications

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“…A similar study [7] on bell pepper and pimiento seeds produced better results, as the differences between these two species are already visible to the eye. They achieved a score of 89.2 using the SVM and 100 using the KNeighbors and tree classifiers.…”

Section: Discussionmentioning

confidence: 66%

“…In any case, it can be very challenging to distinguish between different types of pepper based on their seeds, as they have similar morphologies [7,8]. This creates a problem of mislabeling on the market.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, the accuracy achieved was 85%. The same study was repeated in [7] using 23 different machine learning algorithms. The algorithms that achieved 100% accuracy were Fine KNN, Weighted KNN, Boosted Trees, Bagged Trees, and Subspace KNN.…”

Section: Introductionmentioning

confidence: 99%

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Classification of Pepper Seeds by Machine Learning Using Color Filter Array Images

Djoulde,

Ousman,

Hamadjam

et al. 2024

J. Imaging

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The purpose of this work is to classify pepper seeds using color filter array (CFA) images. This study focused specifically on Penja pepper, which is found in the Litoral region of Cameroon and is a type of Piper nigrum. India and Brazil are the largest producers of this variety of pepper, although the production of Penja pepper is not as significant in terms of quantity compared to other major producers. However, it is still highly sought after and one of the most expensive types of pepper on the market. It can be difficult for humans to distinguish between different types of peppers based solely on the appearance of their seeds. To address this challenge, we collected 5618 samples of white and black Penja pepper and other varieties for classification using image processing and a supervised machine learning method. We extracted 18 attributes from the images and trained them in four different models. The most successful model was the support vector machine (SVM), which achieved an accuracy of 0.87, a precision of 0.874, a recall of 0.873, and an F1-score of 0.874.

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Section: Discussionmentioning

confidence: 66%

Section: Introductionmentioning

confidence: 99%

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Classification of Pepper Seeds by Machine Learning Using Color Filter Array Images

Djoulde,

Ousman,

Hamadjam

et al. 2024

J. Imaging

View full text Add to dashboard Cite

show abstract

“…Methods based on optical sensors combined with machine learning algorithms can be used for the evaluation and classification of seeds (Medeiros et al, 2020). Machine vision can be an alternative for the human eye, which may have difficulty distinguishing between pepper seeds belonging to different species (Jesusimo & Dioses, 2020). So, the discrimination of cultivars of pepper seeds may be even more difficult.…”

Section: Introductionmentioning

confidence: 99%

A convolutional neural network‐based comparative study for pepper seed classification: Analysis of selected deep features with support vector machine

Sabancı

Aslan

Ropelewska

et al. 2021

J Food Process Engineering

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The seeds of high quality are very important for the cultivation of the pepper. The required cultivation practices and growing conditions may be affected by the cultivar. Also, the productivity and properties of pepper depend on the cultivar. The selection of appropriate seed cultivars may be necessary for the breeding programs. The cultivar differentiation of pepper seeds may be tested by the human eye. However, small sizes and visual similarities make it difficult to distinguish between seed cultivars. Computer vision and artificial intelligence can provide high cultivar discrimination accuracy and the procedures are objective and fast. This study aimed to classify pepper seeds belonging to different cultivars with convolutional neural network (CNN) models. The seeds were obtained from green, orange, red, and yellow pepper cultivars. A flatbed scanner was used to acquire the pepper seed images. After the image acquisition, the procedure applied was preprocessing of the images, data augmentation using different techniques and then deep learning‐based classification. Two approaches have been proposed for classification. In the first approach, CNN models (ResNet18 and ResNet50) were trained for pepper seeds. In the second approach, different from the first, the features of pretrained CNN models were fused, and feature selection was applied to the fused features. Classification using all features and selected features was performed with the support vector machine (SVM) with different kernel functions (Linear, Quadratic, Cubic, Gaussian). The accuracies in the first approximation were 98.05% and 97.07% for ResNet50 and ResNet18, respectively. In the second approach, CNN‐SVM‐Cubic achieved up to 99.02% accuracy with the selected features. Practical applications In precision agriculture, it is very important that the seeds be of the same type for the purification and standardization of the crop culture. Performing this classification manually with human assistance will result in subjective, slow, and low standard outcomes. To overcome such problems, classification supported by artificial intelligence and machine vision systems emerges as an important tool. In this study, a highly successful classification system is presented according to the visual characteristics of pepper seeds. The proposed models can be preferred in practice for identifying pepper seeds and detecting falsification or ensuring their reliability. It will prevent mixing of different pepper seeds with different attributes for processing.

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Classification of Pepper Seeds Using Data Mining Algorithms

Cited by 2 publications

References 7 publications

Classification of Pepper Seeds by Machine Learning Using Color Filter Array Images

Classification of Pepper Seeds by Machine Learning Using Color Filter Array Images

A convolutional neural network‐based comparative study for pepper seed classification: Analysis of selected deep features with support vector machine

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