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Identification of apple diseases in digital images by using the Gaining-sharing knowledge-based algorithm for multilevel thresholding

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Abstract

Identifying the defects in apples is commonly done with visual examination techniques. However, it is a slow and laborious process. Image processing techniques have begun to be used to help and make the diagnosis of fruit diseases more efficient. In image processing systems, the segmentation of regions in the scenes is a crucial step. Specifically for images from apples, disease segmentation is a complicated task due to the different elements that affect the acquisition of the images. In addition, apple diseases also have features that need to be segmented. In this work, an efficient approach that uses the Gaining-sharing Knowledge-based (GSK) algorithm is proposed to optimize the minimum cross-entropy thresholding (MCET) for the segmentation of apple images highlighting the diseases defects. The proposed MCET-GSK has been tested for experimental purposes over different images and compared with various metaheuristics. The experiments were conducted to provide evidence of the GSK’s optimization capabilities by performing the Wilcoxon test and applying a set of metrics to verify the quality of the segmented images. The experimental results validate the performance of the MCET-GSK in the segmentation of apple images by adequately separating the regions with damage produced by a disease. The quality of the segmentation is superior compared with other similar approaches.

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Authors and Affiliations

  1. División de Electrónica y Computación, Universidad de Guadalajara, CUCEI, Av. Revolución 1500, Guadalajara, Jal., Mexico

    Noé Ortega-Sánchez, Diego Oliva, Marco Pérez-Cisneros & Rosaura Hernández-Montelongo

  2. DeustoTech, Faculty of Engineering, University of Deusto, Av. Universidades 24, 48007, Bilbao, Spain

    Erick Rodríguez-Esparza

  3. School of Computer Science & Robotics, Tomsk Polytechnic University, Tomsk, Russia

    Diego Oliva

  4. Operations Research Department, Faculty of Graduate Studies for Statistical Research, Cairo University, Giza, 12613, Egypt

    Ali Wagdy Mohamed

  5. Department of Mathematics and Actuarial Science, School of Sciences and Engineering, The American University in Cairo, Cairo, Egypt

    Ali Wagdy Mohamed

  6. Department of Computer Science, Government Bikram College of Commerce, Patiala, Punjab, 147001, India

    Gaurav Dhiman

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  1. Noé Ortega-Sánchez
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  3. Diego Oliva
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Correspondence to Diego Oliva or Marco Pérez-Cisneros.

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Appendix

Appendix

See Fig. 7 and Tables 6, 7, 8, 9, 10 and 11.

Fig. 7
figure 7

Representatives benchmark images using for test algorithms

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Table 6 Comparison of fitness values obtained by using different optimization algorithms and the minimum cross-entropy to segment the benchmark images
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Table 7 The best thresholds values of all algorithms for the set of benchmark images
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Table 8 Comparison of SSIM values obtained by using different optimization algorithms and the minimum cross-entropy to segment the benchmark images
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Table 9 Comparison of FSIM values obtained by using different optimization algorithms and the minimum cross-entropy to segment the benchmark images
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Table 10 Comparison of PSNR values obtained by using different optimization algorithms and the minimum cross-entropy to segment the benchmark images
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Table 11 Comparison of the p-values obtained through the Wilcoxon signed-rank test between the pairs of PSO vs. GSK, FFO vs. GSK, DE vs. GSK, HS vs. GSK, ABC vs. GSK and SCA vs. GSK for minimum cross-entropy objective function
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Ortega-Sánchez, N., Rodríguez-Esparza, E., Oliva, D. et al. Identification of apple diseases in digital images by using the Gaining-sharing knowledge-based algorithm for multilevel thresholding. Soft Comput 26, 2587–2623 (2022). https://doi.org/10.1007/s00500-021-06418-5

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