Abnormal phenotypic defects detection of jujube using explainable machine learning enhanced computer vision.

Journal: Journal of food science
PMID: 39322960

Abstract

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.

Authors

  • Luwei Zhang
    College of Engineering, China Agricultural University, Beijing, China.
  • Yan Chen
    Department of Respiratory and Critical Care Medicine, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai, China.
  • Xiangyun Guo
    School of Information Management, Beijing Information Science & Technology University, Beijing, China.
  • You Li
    CFAR and I2R, Agency for Science, Technology and Research, Singapore.
  • Chao Chen
    Department of Neonatology, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.
  • Yu Zou
    Department of Gastrointestinal Surgery, The First Affiliated Hospital of Chongqing Medical University, Chongqing, China.
  • Xiaoshuan Zhang
    Smart Healthcare Big Data Engineering and Ubiquitous Computing Characteristic Laboratory, Universities of Shandong, Jinan 250103, China.
  • Jinyou Hu
    College of Engineering, China Agricultural University, Beijing, China.

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