Identification of Potato Plant Pests Using the Convolutional Neural Network VGG16 Method

Sri Hadianti; Faruq Aziz; Daning Nur Sulistyowati; Dwiza Riana; Ridwan Saputra; Kurniawantoro

doi:10.37034/medinftech.v2i2.37

Authors

Sri Hadianti Universitas Nusa Mandiri
Faruq Aziz Universitas Nusa Mandiri
Daning Nur Sulistyowati Universitas Nusa Mandiri
Dwiza Riana Universitas Nusa Mandiri
Ridwan Saputra Universitas Nusa Mandiri
Kurniawantoro Universitas Nusa Mandiri

DOI:

https://doi.org/10.37034/medinftech.v2i2.37

Keywords:

Potato, Plant Pests, CNN, VGG16, Identification

Abstract

Pests are one of the main challenges in potato cultivation that can significantly reduce crop yields. Therefore, quick and accurate pest identification is crucial for effective pest control. This research aims to develop a pest identification system for potato plants using the Convolutional Neural Network (CNN) method with the VGG16 architecture. The dataset used consists of images of pests commonly found on potato plants. After the labeling process, these images were used to train the CNN VGG16 model. The research results show that the CNN VGG16 method can identify types of pests with an accuracy rate of 73%. The results serve as a reference to help farmers and agricultural practitioners detect the presence of pests earlier and take the necessary actions to reduce crop losses.

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References

J. M. Al-Khayri, S. M. Jain, and D. V. Johnson, Advances in Plant Breeding Strategies: Vegetable Crops: Volume 8: Bulbs, Roots and Tubers, vol. 8. 2021.

and R. A. C. J. J. F. Kreuze, J. A. C. Souza-Dias, A. Jeevalatha, A. R. Figueira, J. P. T. Valkonen, “Viral Diseases in Potato,” in The Potato Crop, Springer, 2020.

A. Devaux, J. Goffart, and P. Kromann, “The Potato of the Future : Opportunities and Challenges in Sustainable Agri-food Systems Introduction : The Current Situation of Global Food Security,” Potato Res., vol. 64, pp. 681–720, 2021.

T. Domingues, T. Brandão, and J. C. Ferreira, “Machine Learning for Detection and Prediction of Crop Diseases and Pests: A Comprehensive Survey,” Agric., vol. 12, no. 9, pp. 1–23, 2022, doi: 10.3390/agriculture12091350.

Z. Li, F. Liu, W. Yang, S. Peng, and J. Zhou, “A Survey of Convolutional Neural Networks: Analysis, Applications, and Prospects,” IEEE Trans. Neural Networks Learn. Syst., vol. 33, no. 12, pp. 6999–7019, 2022, doi: 10.1109/TNNLS.2021.3084827.

R. A. Sholihati, I. A. Sulistijono, A. Risnumawan, and E. Kusumawati, “Potato Leaf Disease Classification Using Deep Learning Approach,” IES 2020 - Int. Electron. Symp. Role Auton. Intell. Syst. Hum. Life Comf., pp. 392–397, 2020, doi: 10.1109/IES50839.2020.9231784.

H. Tian, T. Wang, Y. Liu, X. Qiao, and Y. Li, “Computer vision technology in agricultural automation —A review,” Inf. Process. Agric., vol. 7, no. 1, pp. 1–19, 2020, doi: 10.1016/j.inpa.2019.09.006.

C. R. Rahman et al., “Identification and recognition of rice diseases and pests using convolutional neural networks,” Biosyst. Eng., vol. 194, pp. 112–120, 2020, doi: 10.1016/j.biosystemseng.2020.03.020.

L. C. Ngugi, M. Abelwahab, and M. Abo-Zahhad, “Recent advances in image processing techniques for automated leaf pest and disease recognition – A review,” Inf. Process. Agric., vol. 8, no. 1, pp. 27–51, 2021, doi: 10.1016/j.inpa.2020.04.004.

L. Li, S. Zhang, and B. Wang, “Plant Disease Detection and Classification by Deep Learning - A Review,” IEEE Access, vol. 9, no. Ccv, pp. 56683–56698, 2021, doi: 10.1109/ACCESS.2021.3069646.

A. Villa-Henriksen, G. T. C. Edwards, L. A. Pesonen, O. Green, and C. A. G. Sørensen, “Internet of Things in arable farming: Implementation, applications, challenges and potential,” Biosyst. Eng., vol. 191, pp. 60–84, 2020, doi: 10.1016/j.biosystemseng.2019.12.013.

M. S. H. Talukder, R. Bin Sulaiman, M. R. Chowdhury, M. S. Nipun, and T. Islam, “PotatoPestNet: A CTInceptionV3-RS-based neural network for accurate identification of potato pests,” Smart Agric. Technol., vol. 5, no. July, p. 100297, 2023, doi: 10.1016/j.atech.2023.100297.

Z. Yang, R. O. Sinnott, J. Bailey, and Q. Ke, A survey of automated data augmentation algorithms for deep learning-based image classification tasks, vol. 65, no. 7. Springer London, 2023.

Z. P. Jiang, Y. Y. Liu, Z. E. Shao, and K. W. Huang, “An improved VGG16 model for pneumonia image classification,” Appl. Sci., vol. 11, no. 23, 2021, doi: 10.3390/app112311185.

N. Tilahun, “Potato’S Leaves Disease Detection Model Using Convolutional Neural Network,” no. November, 2020.

S. Aladhadh, S. Habib, M. Islam, M. Aloraini, M. Aladhadh, and H. S. Al-Rawashdeh, “An Efficient Pest Detection Framework with a Medium-Scale Benchmark to Increase the Agricultural Productivity,” Sensors, vol. 22, no. 24, pp. 1–17, 2022, doi: 10.3390/s22249749.