Yan Chen’s research while affiliated with China Agricultural University and other places

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Publications (1)

Classification survey of jujube defects. (a) Common phenotypic defects; (b) detection methods; (c) necessity of jujube defect detection; (d) directions for optimizing classification models.
Architectural diagram of the abnormal phenotype detection system.
Principles of image processing and modeling. (a) Structure of image acquisition device. (b) Physical implementation of the image acquisition device. (c) Schematic of RGB color space. (d) Schematic of HSI color space. (e) Schematic diagram of the improved segmented linear transformation method. (f) Schematic diagram of the minimum outer circle of the maximum horizontal diameter surface.
Color space conversion results.
Results of image preprocessing. (a) Image denoising. (b) S‐component grayscale histogram. (c) Image of dried jujube after background removal. (d) S‐component grayscale image after background removal. (e) S‐component grayscale image after segmented linear transformation. (f) Simple threshold segmentation. (g) Edge detection and morphological processing. (h) Binary image of the defective area. (i) Color images of defective areas generated using image synthesis.

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Abnormal phenotypic defects detection of jujube using explainable machine learning enhanced computer vision
  • Article
  • Publisher preview available

September 2024

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48 Reads

Luwei Zhang

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Yan Chen

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Xiangyun Guo

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[...]

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Jinyou Hu

Jujube is susceptible to biotic and abiotic adversity stresses resulting in abnormal phenotypic defects. Therefore, abnormal phenotype fruits should be removed during postharvest sorting to increase added value. An improved maximum horizontal diameter linear regression (MHD‐LR) method for size grading of jujube prior to detection of abnormal phenotypic defects was developed. The accuracy of the MHD‐LR model is 95%, with an error of only 0.95 mm. In addition, a method for detecting abnormal phenotypic defects in jujube was established. It can effectively and accurately classify seven kinds of jujube phenotypes (regular, irregular, wrinkled, moldy, hole‐broken, skin‐broken, and scarred). The data augmentation method based on linear interpolation can effectively expand the dataset with a variance of only 0.0006. Support vector machine‐decision tree (SVMDT), logistic regression, back propagation neural network, and long short‐term memory network models were established to classify jujube samples with different phenotypes, with accuracies of 99.57%, 99.00%, 99.14%, and 99.29%, respectively. The results showed that the SVMDT model had higher accuracy and explainability. This research is expected to provide a new method to improve the precise classification of abnormal phenotypic defects in postharvest jujube.

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