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Monitoring of impurities in green peppers based on convolutional neural networks

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Abstract

Impurities, which affect the commercial value of green peppers significantly, are a crucial index for evaluating the processing quality. Currently, impurities in green peppers are, mostly, recognized manually. Manual recognition is time-consuming, subjectivity, and error-prone; this study, thus, proposed an identification approach for the monitoring of impurities in green peppers based on convolutional neural networks (CNN). Green pepper image with impurities was randomly captured, divided, and then augmented, for the training of ResNet34, Xception, and MobileneV3. After testing, detection accuracy of the above models on test images was reported as 94.04%, 90.73%, and 94.04%, respectively, indicating that the CNNs were capable of the monitoring of green pepper impurities. Therefore, the trained models were, further, adopted to create a Green Peppers Impurity Monitoring System. Results of the monitoring system reported both the types of impurities and detection time. Based on the tested detection accuracy and efficiency of all the trained models, the MobilenetV3 was recommend as the first choice for green pepper impurity detection. Obviously, the present study provided a new approach for the detection of the quality of green peppers. Accordingly, the findings of this work may contribute to monitor the processing of green peppers, which would return available data for the adjustment and majorization of production equipment.

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Data availability

The datasets of this study may be made available from the corresponding author on reasonable request.

References

  1. Cao, M., Zhang, S., Li, M., Liu, Y., Dong, P., Li, S., Kuang, M., Li, R., Zhou, Y.: Discovery of four novel viruses associated with flower yellowing disease of green SichuanPepper (Zanthoxylum Armatum) by virome analysis. Viruses-basel. 11, 696 (2019). https://doi.org/10.3390/v11080696

    Article  Google Scholar 

  2. Zhang, J., Zhou, H., Luo, F., Wan, L., Li, C., Wang, L.: Determination of mechanical properties of Zanthoxylum armatum using the discrete element method. Food Qual. Saf. (2022). https://doi.org/10.1093/fqsafe/fyac043

    Article  Google Scholar 

  3. Shen, Y., Yin, Y., Zhao, C., Li, B., Wang, J., Li, G., Zhang, Z.: Image recognition method based on an improved convolutional neural network to detect impurities in wheat. IEEE Access. 7, 162206–162218 (2019). https://doi.org/10.1109/ACCESS.2019.2946589

    Article  Google Scholar 

  4. Wang, P., Luo, F., Wang, L., Li, C., Niu, Q., Li, H.: S-ResNet: an improved ResNet neural model capable of the identification of small insects. Front. Plant Sci. 13, 1066115 (2022). https://doi.org/10.3389/fpls.2022.1066115

    Article  Google Scholar 

  5. Ankam, P., Shankar, V., Harshini, P., Akash, A., Valusa, A.: Real time face identification for capturing the class attendance using convolutional neural networks. In: 2021 5th International Conference on Intelligent Computing and Control Systems (ICICCS) (2021)

  6. Pu, J., Zhu, S., Miao, Y., Huang, H.: Detection of dish waste degree based on image processing and convolutional neural networks. Environ. Progress Sustain. Energy 42(1), e13942 (2023). https://doi.org/10.1002/ep.13942

    Article  Google Scholar 

  7. Cheng, X., Zhang, Y., Chen, Y., Wu, Y., Yue, Y.: Pest identification via deep residual learning in complex background. Comput. Electron. Agric. 141, 351–356 (2017). https://doi.org/10.1016/j.compag.2017.08.005

    Article  Google Scholar 

  8. Li, Y., Wang, H., Dang, L.M., Sadeghi-Niaraki, A., Moon, H.: Crop pest recognition in natural scenes using convolutional neural networks. Comput. Electron. Agric. 169, 105174 (2020). https://doi.org/10.1016/j.compag.2019.105174

    Article  Google Scholar 

  9. Deng, L., Wang, Z., Wang, C., He, Y., Huang, T., Dong, Y., Zhang, X.: Application of agricultural insect pest detection and control map based on image processing analysis. IFS 38, 379–389 (2020). https://doi.org/10.3233/JIFS-179413

    Article  Google Scholar 

  10. Xiao, Z., Yin, K., Geng, L., Wu, J., Zhang, F., Liu, Y.: Pest identification via hyperspectral image and deep learning. SIViP 16, 873–880 (2022). https://doi.org/10.1007/s11760-021-02029-7

    Article  Google Scholar 

  11. Song, L., Liu, M., Liu, S., Wang, H., Luo, J.: Pest species identification algorithm based on improved YOLOv4 network. SIViP 17, 3127–3134 (2023). https://doi.org/10.1007/s11760-023-02534-x

    Article  Google Scholar 

  12. Qian, S., Du, J., Zhou, J., Xie, C., Jiao, L., Li, R.: An effective pest detection method with automatic data augmentation strategy in the agricultural field. SIViP 17, 563–571 (2023). https://doi.org/10.1007/s11760-022-02261-9

    Article  Google Scholar 

  13. Zhang, Z., Liu, H., Meng, Z., Chen, J.: Deep learning-based automatic recognition network of agricultural machinery images. Comput. Electron. Agric. 166, 104978 (2019). https://doi.org/10.1016/j.compag.2019.104978

    Article  Google Scholar 

  14. Yang, K., Hui, L., Pei, W., Meng, Z., Chen, J.: Convolutional neural network-based automatic image recognition for agricultural machinery. Int. J. Agric. Biol. Eng.. 11, 7 (2018). https://doi.org/10.25165/j.ijabe.20181103.3454

    Article  Google Scholar 

  15. Liu, X., Jia, W., Ruan, C., Zhao, D., Gu, Y., Chen, W.: The recognition of apple fruits in plastic bags based on block classification. Precision Agric. 19, 735–749 (2018). https://doi.org/10.1007/s11119-017-9553-2

    Article  Google Scholar 

  16. Tripathi, M.K., Maktedar, D.D.: A role of computer vision in fruits and vegetables among various horticulture products of agriculture fields: a survey. Inf. Process. Agric. 7, 183–203 (2020). https://doi.org/10.1016/j.inpa.2019.07.003

    Article  Google Scholar 

  17. Xiong, J., Liu, Z., Lin, R., Chen, S., Chen, W., Yang, Z.: Unmanned aerial vehicle vision detection technology of green mango on tree in natural environment. Trans. Chin. Soc. Agric. Mach. 49, 23–29 (2018). https://doi.org/10.6041/j.issn.1000-1298.2018.11.003

    Article  Google Scholar 

  18. Xiong, J., Zhen, L.: Visual detection technology of green citrus under natural environment. Trans. Chin. Soc. Agric. Mach. 49, 45–52 (2018). https://doi.org/10.6041/j.issn.1000-1298.2018.04.005

    Article  Google Scholar 

  19. Bora, R., Parasar, D., Charhate, S.: A detection of tomato plant diseases using deep learning MNDLNN classifier. SIViP (2023). https://doi.org/10.1007/s11760-023-02498-y

    Article  Google Scholar 

  20. Mun, J., Kim, J.: Universal super-resolution for face and non-face regions via a facial feature network. SIViP 14, 1601–1608 (2020). https://doi.org/10.1007/s11760-020-01706-3

    Article  Google Scholar 

  21. Alqaralleh, E., Afaneh, A., Toygar, Ö.: Masked face recognition using frontal and profile faces with multiple fusion levels. SIViP 17, 1375–1382 (2023). https://doi.org/10.1007/s11760-022-02345-6

    Article  Google Scholar 

  22. Ghielmetti, N., Loncar, V., Pierini, M., Roed, M., Summers, S., Aarrestad, T., Petersson, C., Linander, H., Ngadiuba, J., Lin, K., Harris, P.: Real-time semantic segmentation on FPGAs for autonomous vehicles with hls4ml. Mach. Learn. Sci. Technol. 3, 045011 (2022). https://doi.org/10.1088/2632-2153/ac9cb5

    Article  Google Scholar 

  23. Wan, J., Ding, W., Zhu, H., Xia, M., Huang, Z., Tian, L., Zhu, Y., Wang, H.: An efficient small traffic sign detection method based on YOLOv3. J Sign Process Syst. 93, 899–911 (2021). https://doi.org/10.1007/s11265-020-01614-2

    Article  Google Scholar 

  24. Li, Q., Jia, W., Sun, M., Hou, S., Zheng, Y.: A novel green apple segmentation algorithm based on ensemble U-Net under complex orchard environment. Comput. Electron. Agr. 180, 105900 (2021). https://doi.org/10.1016/j.compag.2020.105900

    Article  Google Scholar 

  25. Li, Y., Li, M., Qi, J., Zhou, D., Zou, Z., Liu, K.: Detection of typical obstacles in orchards based on deep convolutional neural network. Comput. Electron. Agr. 181, 105932 (2021). https://doi.org/10.1016/j.compag.2020.105932

    Article  Google Scholar 

  26. Qi, J., Liu, X., Liu, K., Xu, F., Guo, H., Tian, X., Li, M., Bao, Z., Li, Y.: An improved YOLOv5 model based on visual attention mechanism: Application to recognition of tomato virus disease. Comput. Electron. Agric. 194, 106780 (2022)

    Article  Google Scholar 

  27. Wang, P., Tang, Y., Luo, F., Wang, L., Li, C., Niu, Q., Li, H.: Weed25: a deep learning dataset for weed identification. Front. Plant Sci. 13, 1053329 (2022). https://doi.org/10.3389/fpls.2022.1053329

    Article  Google Scholar 

  28. González-Camejo, J., Ferrer, J., Seco, A., Barat, R.: Outdoor microalgae-based urban wastewater treatment: recent advances, applications, and future perspectives. Wiley Interdiscip. Rev. Water (2021). https://doi.org/10.1002/wat2.1518

    Article  Google Scholar 

  29. Rossi, S., Díez-Montero, R., Rueda, E., Cascino, F.C., Parati, K., García, J., Ficara, E.: Free ammonia inhibition in microalgae and cyanobacteria grown in wastewaters: Photo-respirometric evaluation and modelling. Bioresour. Technol. 305, 123046 (2020). https://doi.org/10.1016/j.biortech.2020.123046

    Article  Google Scholar 

  30. Chen, J., Ding, Q., Liu, L., Hou, L., Liu, Y., Shen, M.: Early detection of broilers respiratory diseases based on YOLO v5 and short time tracking. Trans. Chin. Soc. Agric. Mach. (2023). https://doi.org/10.6041/j.issn.1000-1298.2023.01.027

    Article  Google Scholar 

  31. Aggarwal, C.C.: Neural Networks and Deep Learning: A Textbook. Springer International Publishing, Cham (2018)

    Book  Google Scholar 

  32. Peng, H., Ji, Li., Xu, H., Chen, H., Xing, Z., He, H., Juntao, X.: Litchi detection based on multiple feature enhancement and feature fusion SSD. Trans. CSAE 38, 169–177 (2022). https://doi.org/10.11975/j.issn.1002-6819.2022.04.020

    Article  Google Scholar 

  33. Hachemi, A., Zeroual, A.: Computer-assisted program for water Calco-Carbonic equilibrium computation. Earth Sci Inform. 15, 689–704 (2022). https://doi.org/10.1007/s12145-021-00703-5

    Article  Google Scholar 

  34. Magalhães, T.E.C., Rebordão, J.M.: PyWolf: A PyOpenCL implementation for simulating the propagation of partially coherent light. Comput. Phys. Commun. 276, 108336 (2022). https://doi.org/10.1016/j.cpc.2022.108336

    Article  MathSciNet  Google Scholar 

  35. Shen, J., Zheng, J., Li, Z., Liu, Y., Jing, F., Wan, X., Yamaguchi, Y., Zhuang, S.: A rapid nucleic acid concentration measurement system with large field of view for a droplet digital PCR microfluidic chip. Lab Chip. 21, 3742–3747 (2021). https://doi.org/10.1039/D1LC00532D

    Article  Google Scholar 

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Funding

The proposed study was supported by the Fundamental Research Funds for the Central Universities (SWU019015).

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Authors and Affiliations

  1. College of Engineering and Technology, Southwest University, Chongqing, 400715, People’s Republic of China

    Jian Zhang, Jing Pu, Ting an, Pengxin Wu, Qi Niu, Chengsong Li & Lihong Wang

  2. Key Laboratory of Agricultural Equipment in Hilly and Mountainous Areas, Chongqing, 400715, People’s Republic of China

    Jian Zhang, Jing Pu, Ting an, Pengxin Wu, Qi Niu, Chengsong Li & Lihong Wang

  3. College of Plant Protection, Southwest University, Chongqing, 400715, People’s Republic of China

    Hong Zhou

Authors
  1. Jian Zhang
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Contributions

JZ helped in writing—original draft, writing—review & editing, methodology, formal analysis, investigation. JP contributed to image processing, experimentation, conceptualization, data curation. TA performed methodology and formal analysis. PW helped in experimentation and validation. HZ performed writing—review and editing and formal analysis. QN was involved in formal analysis, investigation, validation. CL helped in writing—review and editing, resources, supervision. LW was involved in writing—review and editing, resources, project administration, funding acquisition.

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Correspondence to Lihong Wang.

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Zhang, J., Pu, J., an, T. et al. Monitoring of impurities in green peppers based on convolutional neural networks. SIViP 18, 63–69 (2024). https://doi.org/10.1007/s11760-023-02711-y

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