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

Sabanci, Kadir ; Aslan, Muhammet Fatih ; Ropelewska, Ewa ; Unlersen, Muhammed Fahri

Journal of food process engineering, 2022-06, Vol.45 (6), p.n/a [Periódico revisado por pares]

Hoboken, USA: John Wiley & Sons, Inc

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  • Título:
    A convolutional neural network‐based comparative study for pepper seed classification: Analysis of selected deep features with support vector machine
  • Autor: Sabanci, Kadir ; Aslan, Muhammet Fatih ; Ropelewska, Ewa ; Unlersen, Muhammed Fahri
  • É parte de: Journal of food process engineering, 2022-06, Vol.45 (6), p.n/a
  • Descrição: 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.
  • Editor: Hoboken, USA: John Wiley & Sons, Inc
  • Idioma: Inglês
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  • Realização: ABCD-USP USP   Logos de Redes Sociais: Freepik

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