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Citrus surface defect identification based on PCS-2D-Otsu and CGWO-DT-SVM

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Abstract

Aiming at the problems of poor image quality, susceptibility to interference from the external environment and difficulties in recognition due to high similarity between real defects and fruit stalks in citrus surface defect recognition, we proposed a citrus surface defect recognition method based on a combination of PCSA-2D-Otsu and CGWO-DT-SVM. Firstly, a partial differential equations (PDE) based variational model is used to denoise the captured citrus images, which reduces the blurring of the images while retaining the edge details and important texture information. Then, a 2D-Otsu threshold segmentation algorithm optimized by the plant cell swarm algorithm (PCSA) is used to segment the citrus surface defects and extract the image features to separate the citrus from the background, making full use of the relevant information and reducing the influence of complex backgrounds. Finally, the images are input to the chaos gray wolf optimizer (CGWO) algorithm optimized DT-SVM classifier for true defects and fruit stalks to identify the citrus into healthy and defective classes. We selected 2000 collected citrus images for the experiments and the application results showed an overall recognition accuracy of 96.71%. To verify the feasibility and effectiveness of the proposed model, we compared it with advanced deep learning methods and machine learning methods, among others. The experimental results show that the method is more accurate and less time consuming for the recognition of citrus surface defects, and can be effective for the recognition of citrus surface defects. It provides a solution and reference for the application of machine vision methods in the accurate and rapid diagnosis of fruit surface defects.

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  1. College of Computer & Information Engineering, Central South University of Forestry and Technology, Changsha, 410004, Hunan, China

    Chuang Cai, Guoxiong Zhou & Chao Lu

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  1. Chuang Cai
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  2. Guoxiong Zhou
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Correspondence to Guoxiong Zhou.

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Cai, C., Zhou, G. & Lu, C. Citrus surface defect identification based on PCS-2D-Otsu and CGWO-DT-SVM. Multimed Tools Appl 83, 43649–43672 (2024). https://doi.org/10.1007/s11042-023-17341-2

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