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Plants
Volume 14
Issue 21
10.3390/plants14213352
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Detecting Escherichia coli Contamination on Plant Leaf Surfaces Using UV-C Fluorescence Imaging and Deep Learning

by
Snehit Vaddi
1,
Thomas F. Burks
2,
Zafar Iqbal
2,*,
Pappu Kumar Yadav
3,
Quentin Frederick
2,
Satya Aakash Chowdary Obellaneni
1,
Jianwei Qin
4,
Moon Kim
4,
Mark A. Ritenour
5,
Jiuxu Zhang
5 and
Fartash Vasefi
6
1
Department of Computer and Information Science and Engineering, University of Florida, Gainesville, FL 32611, USA
2
Department of Agricultural and Biological Engineering, University of Florida, Gainesville, FL 32611, USA
3
Department of Agricultural and Biosystems Engineering, South Dakota State University, Brookings, SD 57007, USA
4
USDA/ARS Environmental Microbial and Food Safety Laboratory, Beltsville Agricultural Research Center, Beltsville, MD 20705, USA
5
Department of Horticultural Sciences, University of Florida, 2199 South Rock Road, Fort Pierce, FL 34945, USA
6
SafetySpect Inc., 4200 James Ray Dr., Grand Forks, ND 58202, USA
*
Author to whom correspondence should be addressed.
Plants 2025, 14(21), 3352; https://doi.org/10.3390/plants14213352 (registering DOI)
Submission received: 12 September 2025 / Revised: 28 October 2025 / Accepted: 28 October 2025 / Published: 31 October 2025
(This article belongs to the Special Issue Application of Optical and Imaging Systems to Plants)
Versions Notes

Abstract

The transmission of Escherichia coli through contaminated fruits and vegetables poses serious public health risks and has led to several national outbreaks in the USA. To enhance food safety, rapid and reliable detection of E. coli on produce is essential. This study evaluated the performance of the CSI-D+ system combined with deep learning for detecting varying concentrations of E. coli on citrus and spinach leaves. Eight levels of E. coli contamination, ranging from 0 to 108 colony-forming units (CFU)/mL, were inoculated onto the leaf surfaces. For each concentration level, 10 droplets were applied to 8 citrus and 12 spinach leaf samples (2 cm in diameter), and fluorescence images were captured. The images were then subdivided into quadrants, and several post-processing operations were applied to generate the final dataset, ensuring that each sample contained at least 2–3 droplets. Using this dataset, multiple deep learning (DL) models, including EfficientNetB7, ConvNeXtBase, and five YOLO11 variants (n, s, m, l, x), were trained to classify E. coli concentration levels. Additionally, Eigen-CAM heatmaps were used to visualize the spatial responses of the models to bacterial presence. All YOLO11 models outperformed EfficientNetB7 and ConvNeXtBase. In particular, YOLO11s-cls was identified as the best-performing model, achieving average validation accuracies of 88.43% (citrus) and 92.03% (spinach), and average test accuracies of 85.93% (citrus) and 92.00% (spinach) at a 0.5 confidence threshold. This model demonstrated an inference speed of 0.011 s per image with a size of 11 MB. These findings indicate that fluorescence-based imaging combined with deep learning for rapid E. coli detection could support timely interventions to prevent contaminated produce from reaching consumers.
Keywords: E. coli; food safety; fluorescence imaging; CSI-D+; deep learning; YOLO11; Eigen-CAM E. coli; food safety; fluorescence imaging; CSI-D+; deep learning; YOLO11; Eigen-CAM

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MDPI and ACS Style

Vaddi, S.; Burks, T.F.; Iqbal, Z.; Yadav, P.K.; Frederick, Q.; Obellaneni, S.A.C.; Qin, J.; Kim, M.; Ritenour, M.A.; Zhang, J.; et al. Detecting Escherichia coli Contamination on Plant Leaf Surfaces Using UV-C Fluorescence Imaging and Deep Learning. Plants 2025, 14, 3352. https://doi.org/10.3390/plants14213352

AMA Style

Vaddi S, Burks TF, Iqbal Z, Yadav PK, Frederick Q, Obellaneni SAC, Qin J, Kim M, Ritenour MA, Zhang J, et al. Detecting Escherichia coli Contamination on Plant Leaf Surfaces Using UV-C Fluorescence Imaging and Deep Learning. Plants. 2025; 14(21):3352. https://doi.org/10.3390/plants14213352

Chicago/Turabian Style

Vaddi, Snehit, Thomas F. Burks, Zafar Iqbal, Pappu Kumar Yadav, Quentin Frederick, Satya Aakash Chowdary Obellaneni, Jianwei Qin, Moon Kim, Mark A. Ritenour, Jiuxu Zhang, and et al. 2025. "Detecting Escherichia coli Contamination on Plant Leaf Surfaces Using UV-C Fluorescence Imaging and Deep Learning" Plants 14, no. 21: 3352. https://doi.org/10.3390/plants14213352

APA Style

Vaddi, S., Burks, T. F., Iqbal, Z., Yadav, P. K., Frederick, Q., Obellaneni, S. A. C., Qin, J., Kim, M., Ritenour, M. A., Zhang, J., & Vasefi, F. (2025). Detecting Escherichia coli Contamination on Plant Leaf Surfaces Using UV-C Fluorescence Imaging and Deep Learning. Plants, 14(21), 3352. https://doi.org/10.3390/plants14213352

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