CLASSIFICATION OF INSECT PESTS IN AGRICULTURE USING INCEPTION-RESNET-V2 ARCHITECTURE
DOI:
https://doi.org/10.35457/antivirus.v19i1.4107Keywords:
CNN, Inception-Resnet-V2, Hama Serangga, Deep LearningAbstract
Object recognition in images is a major challenge in digital image processing with wide applications, including agriculture. This research aims to develop a Convolutional Neural Network (CNN) model based on the Inception-ResNet-V2 architecture for insect pest classification in agriculture. The dataset contains 1,591 images from 13 pest classes, which were processed through preprocessing stages such as resizing, normalization, and augmentation to enhance data quality and variation. The model training process was conducted for 10 epochs, resulting in an accuracy of 89.52% with a loss of 0.4024. The research results indicate that the CNN model can be used to detect and classify insect pests with a high level of accuracy across several classes. This system is expected to help farmers identify pests more efficiently, support decision-making in pest control, and improve agricultural yields.
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Samuel, K. R. Prilianti, H. Setiawan, P. Mimboro, and P. Korespondensi, “Perkebunan Sawit Menggunakan Model Convolutional Neural Network ( CNN ) pada Perangkat Lunak Sistem Informasi the Method of Automatic Tree Detection on Oil Palm Plantation Images Using Convolutional Neural Network ( Cnn ) Model Using,” J. Teknol. Inf. dan Ilmu Komput., vol. 9, no. 7, pp. 1689–1698, 2022, doi: 10.25126/jtiik.202296772.
B. E. Freeman, Ecological and Economic Entomology: A Global Synthesis. Wallingford, UK: CABI, pp. 35-37, 2020.
A. Hasby Bik, F. Tri Anggraeny, and E. Yulia Puspaningrum, “Klasifikasi Penyakit Ginjal Menggunakan Algoritma Hibrida Cnn-Elm,” JATI (Jurnal Mhs. Tek. Inform., vol. 8, no. 3, pp. 3836–3844, 2024, doi: 10.36040/jati.v8i3.9807.
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R. Magdalena, S. Saidah, N. K. C. Pratiwi, and A. T. Putra, “Klasifikasi Tutupan Lahan Melalui Citra Satelit SPOT-6 dengan Metode Convolutional Neural Network (CNN),” J. Edukasi dan Penelit. Inform., vol. 7, no. 3, p. 335, 2021, doi: 10.26418/jp.v7i3.48195.
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M. Fahmy Amin, “Confusion Matrix in Three-class Classification Problems: A Step-by-Step Tutorial,” J. Eng. Res., vol. 7, no. 1, pp. 0–0, 2023, doi: 10.21608/erjeng.2023.296718.
M. Luthfi Bangun Permadi and R. Gumilang, “Penerapan Algoritma CNN (Convolutional Neural Network) Untuk Deteksi Dan Klasifikasi Target Militer Berdasarkan Citra Satelit,” J. Sos. Teknol., vol. 4, no. 2, pp. 134–143, 2024, doi: 10.59188/jurnalsostech.v4i2.1138.
Dian, Purnawansyah, H. Darwis, and L. Nurhayati, “Klasifikasi Penyakit Bawang Merah Menggunakan Naïve Bayes dan Convolutional Neural Network,” Indones. J. Comput. Sci., vol. 12, no. 4, pp. 1932–1943, 2023, doi: 10.33022/ijcs.v12i4.3265.
G. A. Rakhmat and A. M. Yudantira, “Implementation and Optimization Of Inception Resnet-v2 with Data Balancing (Case Study Of Lung Disease Classification),” E3S Web Conf., vol. 484, 2024, doi: 10.1051/e3sconf/202448402010.
A. Sanampudi and S. Srinivasan, “Local search enhanced optimal Inception-ResNet-v2 for classification of long-term lung diseases in post-COVID-19 patients,” Automatika, vol. 65, no. 2, pp. 473–482, 2024, doi: 10.1080/00051144.2023.2295142.
J. Wang, X. He, S. Faming, G. Lu, H. Cong, and Q. Jiang, “A Real-Time Bridge Crack Detection Method Based on an Improved Inception-Resnet-v2 Structure,” IEEE Access, vol. 9, pp. 93209–93223, 2021, doi: 10.1109/ACCESS.2021.3093210.
Tarun R Jain · RahulJayyn, “Dangerous Farm Insects Dataset.” [Online]. Available: https://www.kaggle.com/datasets/tarundalal/dangerous-insects-dataset
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Published
2025-05-30
How to Cite
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Dimas Saputra, Archamul Fajar Pratama, Muhammad Dawam Fakhri, Muhammad Ahsanur Rafi, and Fetty Tri Anggraeny, “CLASSIFICATION OF INSECT PESTS IN AGRICULTURE USING INCEPTION-RESNET-V2 ARCHITECTURE”, antivirus, vol. 19, no. 1, pp. 41–51, May 2025.
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