International Journal of Applied Science and Engineering
Published by Chaoyang University of Technology

Jiachi Wang, Yongqi Fan, Hang Li*, Shoulin Yin

Software College of Shenyang Normal University, Shenyang 110034 China


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To solve the problem of pesticide misuse in judging common wheat diseases, we propose a wheat disease identification scheme combining the VGG convolutional neural network model with WeChat mini program technology. In the model training process, we continuously adjust the structure of the convolutional neural network VGG-16 to realize the real-time and accurate identification model of wheat disease. Specifically, the model parameters are optimized by locally adjusting the convolution layer of the VGG-16 network to achieve accurate maximization. Through the verification, the best accuracy rate of the wheat disease identification model is 85.1%. The mini program is compiled by the WeChat developer tool, which is developed based on WXML, WXSS and JavaScript. After building the wheat disease identification model, it is deployed on a cloud server that works continuously for working 24 h a day. In addition, the mini program posts HTTPS requests as the function of wheat disease identification. The implementation of this scheme can help users identify different types of wheat diseases and provide corresponding solutions according to the results, which is of great significance in underdeveloped agricultural areas.

Keywords: Wheat disease detection, Image identification, WeChat mini program, VGG-16, Convolutional neural network.

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  1. Ai, Y., Sun, C., Tie, J., Cai, X. 2020. Research on recognition model of crop diseases and insect pests based on deep learning in harsh environments. IEEE Access, 8, 171686–171693.

  2. Andrianto, H., Faizal, A., Armandika, F. 2020. smartphone application for deep learning-based rice plant disease detection. In 2020 International Conference on Information Technology Systems and Innovation (ICITSI), 387–392.

  3. Badrinarayanan, V., Kendall, A., Cipolla, R. 2017. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39, 2481–2495.

  4. Burhan, S.A., Minhas, S., Tariq, A., Hassan, M.N. 2020. Comparative study of deep learning algorithms for disease and pest detection in rice crops. In 2020 12th International Conference on Electronics, Computers and Artificial Intelligence (ECAI), 1–5.

  5. Chiranjeevi. M., Velmathi. G. 2022. Phototactic behavior of yellow stemborer and rice leaf folder moths to surface mount device-light emitting diodes of various wavelengths. International Journal of Applied Science and Engineering, 19, 1–9.

  6. Durmuş, H., Güneş, E.O., Kırcı, M., 2017. Disease detection on the leaves of the tomato plants by using deep learning. In 2017 6th International Conference on Agro-Geoinformatics, 1–5.

  7. Figueroa, M., Hammond‐Kosack, K.E., Solomon, P.S. 2018. A review of wheat diseases–A field perspective. Molecular Plant Pathology, 19, 1523–1536.

  8. Girshick, R. 2015. Fast r-cnn. In Proceedings of the IEEE International Conference on Computer Vision, 1440–1448.

  9. Hao, L., Wan, F., Ma, N., Wang, Y. 2018. Analysis of the development of WeChat mini program. Journal of Physics: Conference Series, 1087, 062040.

  10. Karim, S., Tong, G., Li, J., Qadir, A., Farooq, U., Yu, Y. 2023. Current advances and future perspectives of image fusion: A comprehensive review. Information Fusion, 90, 185–217.

  11. Karim, S., Zhang, Y., Asif, M.R., Ali, S. 2017. Comparative analysis of feature extraction methods in satellite imagery. Journal of Applied Remote Sensing, 11.

  12. Khakimov, A., Salakhutdinov, I., Omolikov, A., Utaganov, S. 2022. Traditional and current-prospective methods of agricultural plant diseases detection: A review. In IOP Conference Series: Earth and Environmental Science, 951, 012002.

  13. Khan, A.I., Quadri, S.M.K., Banday, S., Shah, J.L. 2022. Deep diagnosis: A real-time apple leaf disease detection system based on deep learning. Computers and Electronics in Agriculture, 198, 107093.

  14. Laghari, A.A., He, H., Shafiq, M., Khan, A. 2018. Assessment of quality of experience (QoE) of image compression in social cloud computing. Multiagent and Grid Systems, 14, 125–143.

  15. Lu, J., Hu, J., Zhao, G., Mei, F., Zhang, C. 2017. An in-field automatic wheat disease diagnosis system. Computers and Electronics in Agriculture, 142, 369–379.

  16. Nagasubramanian G., Sakthivel R.K., Patan R, Sankayya M., Daneshmand M., Gandomi H. 2021. Ensemble classification and IoT-based pattern recognition for crop disease monitoring system. IEEE Internet of Things Journal, 8, 12847–12854.

  17. Samson. A.A., Popoola. O., Mbey. V. 2022. An alternative framework for implementing generator coherency prediction and islanding detection scheme considering critical contingency in an interconnected power grid. International Journal of Applied Science and Engineering, 19, 1–12.

  18. Shah, J.P., Prajapati, H.B., Dabhi, V.K. 2016. A survey on detection and classification of rice plant diseases. In 2016 IEEE International Conference on Current Trends in Advanced Computing (ICCTAC), 1–8.

  19. Shruthi, U., Nagaveni, V., Raghavendra, B.K. 2019. A review on machine learning classification techniques for plant disease detection. In 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS), 281–284.

  20. Simonyan, K., Zisserman, A. 2014. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556.

  21. Singh, A., SV, H.J., Aishwarya, D., Jayasree, J.S. 2022. Plant disease detection and diagnosis using deep learning. In 2022 International Conference for Advancement in Technology (ICONAT), 1–6.

  22. Subramanian, M., Shanmugavadivel, K., Nandhini, P.S. 2022. On fine-tuning deep learning models using transfer learning and hyper-parameters optimization for disease identification in maize leaves. Neural Computing and Applications, 1–18.

  23. Vallabhajosyula, S., Sistla, V., Kolli, V.K.K. 2022. Transfer learning-based deep ensemble neural network for plant leaf disease detection. Journal of Plant Diseases and Protection, 129, 545–558.

  24. Wang, X., Yin, S., Liu, D., Li. H., Karim, S. 2020. Accurate playground localisation based on multi-feature extraction and cascade classifier in optical remote sensing images. International Journal of Image and Data Fusion, 11, 233–250.

  25. Yin, S., Li, H., Liu, D., Karim, S. 2020. Active contour modal based on density-oriented BIRCH clustering method for medical image segmentation. Multimedia Tools and Applications, 79, 31049–31068.

  26. Yin, S., Li, H. 2020. Hot region selection based on selective search and modified fuzzy C-means in remote sensing images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 13, 5862–5871.

  27. Yujia, L. 2017. System implementation and prospect analysis of wechat “small program” development [J]. Information Communication, 1, 1–2.

  28. Zhong, Y., Zhao, M. 2020. Research on deep learning in apple leaf disease recognition. Computers and Electronics in Agriculture, 168, 105146.


Received: 2023-04-16
Revised: 2023-05-12
Accepted: 2023-06-08
Available Online: 2023-07-13

Cite this article:

Wang, J., Fan, Y., Li, H., Yin, S. 2023. WeChat mini program for wheat diseases recognition based on VGG-16 convolutional neural network. International Journal of Applied Science and Engineering, 20, 2023067.

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