International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 16, No. 5, 8 Oct. 2024
Cover page and Table of Contents: PDF (size: 748KB)
Optimizing VGG16 for Accurate Pest Identification in Oil Palm: A Comparative Study of Fine-Tuning Techniques
PDF (748KB), PP.63-74
Views: 0 Downloads: 0
Author(s)
Muhathir
1,*
Andre Hasudungan Lubis
1
Dwika Karima Wardani
2
Mahardika Gama Pradana
3
Ilham Sahputra
4
Mutammimul Ula
4
Index Terms
Classification, Grid Search, Pets Oil Palm, Random Search, VGG16
Abstract
Recent advancements in pest classification using deep learning models have shown promising results in various agricultural contexts. The VGG16 model, known for its robust performance in image classification, has been applied to the task of classifying pests in oil palm plants. This study aims to evaluate the effectiveness of the VGG16 model in identifying pests on oil palm, comparing the performance of default settings with models fine-tuned using grid search and random search techniques. We employed a quantitative approach, training the VGG16 model with three different configurations: default, fine-tuned with grid search, and fine-tuned with random search. Evaluation metrics including precision, recall, F1-Score, and overall accuracy were used to assess model performance across different pest categories: Metisa plana, Setora nitens, and Setothosea asigna. The default VGG16 model achieved precision, recall, and F1-Score values around 90% for Metisa plana, Setora nitens, and Setothosea asigna, with an overall accuracy of 91.00%. Fine-tuning with grid search improved these metrics, with precision, recall, and F1-Score reaching approximately 93.88%, 92%, and 92.93% respectively, and an overall accuracy of 93%. The random search fine-tuning resulted in even higher performance, with precision of about 95.92%, recall of 94%, and F1-Score of 94.95% for Metisa plana, and overall accuracy of 94.67%. The VGG16 model demonstrated strong performance in pest classification on oil palm, with significant improvements achieved through fine-tuning techniques. The study confirms that grid search and random search fine-tuning can substantially enhance model accuracy and efficacy. Future research should focus on expanding the dataset to include more diverse pest species, incorporating attention mechanisms, and leveraging automated control technologies like drones and the Internet of Things (IoT) to further improve pest management practices.
Cite This Paper
Muhathir, Andre Hasudungan Lubis, Dwika Karima Wardani, Mahardika Gama Pradana, Ilham Sahputra, Mutammimul Ula, "Optimizing VGG16 for Accurate Pest Identification in Oil Palm: A Comparative Study of Fine-Tuning Techniques", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.16, No.5, pp. 63-74, 2024. DOI:10.5815/ijieeb.2024.05.03
Reference
[1]J. A. Widians, M. Taruk, Y. Fauziah, and H. J. Setyadi, “Decision Support System on Potential Land Palm Oil Cultivation using Promethee with Geographical Visualization,” in Journal of Physics: Conference Series, Institute of Physics Publishing, Nov. 2019. doi: 10.1088/1742-6596/1341/4/042011.
[2]H. Hayata, Y. Nengsih, and H. A. Harahap, “KERAGAMAN JENIS SERANGGA HAMA KELAPA SAWIT SISTEM PENANAMAN SISIPAN DAN TUMBANG TOTAL DI DESA PANCA MULIA KECAMATAN SUNGAI BAHAR TENGAH KABUPATEN MUARO JAMBI,” Jurnal Media Pertanian, vol. 3, no. 1, pp. 39–46, 2018.
[3]H. J. Saragih and S. Afrianti, “TINGKAT SERANGAN HAMA ULAT KANTUNG (Mahasena corbetti) PADA AREAL TANAMAN MENGHASILKAN (TM) KELAPA SAWIT PT. INDO SEPADAN JAYA,” Perbal: Jurnal Pertanian Berkelanjutan, vol. 9, no. 2, pp. 88–93, 2021.
[4]L. A. Harahap, R. I. Fajri, M. F. Syahputra, R. F. Rahmat, and E. B. Nababan, “Identifikasi Penyakit Daun Tanaman Kelapa Sawit dengan Teknologi Image Processing Menggunakan Aplikasi Support Vector Machine,” Talenta Conference Series: Agricultural and Natural Resources (ANR), vol. 1, no. 1, pp. 53–59, Oct. 2018, doi: 10.32734/anr.v1i1.96.
[5]A. N. Amiri and A. Bakhsh, “An effective pest management approach in potato to combat insect pests and herbicide,” 3 Biotech, vol. 9, no. 1, p. 16, 2019, doi: 10.1007/s13205-018-1536-0.
[6]R. Mateos Fernández et al., “Insect pest management in the age of synthetic biology,” Plant Biotechnol J, vol. 20, no. 1, pp. 25–36, 2022, doi: https://doi.org/10.1111/pbi.13685.
[7]S. Habib, I. Khan, S. Aladhadh, M. Islam, and S. Khan, “External Features-Based Approach to Date Grading and Analysis with Image Processing,” Emerging Science Journal, vol. 6, no. 4, pp. 694–704, Aug. 2022, doi: 10.28991/ESJ-2022-06-04-03.
[8]J. Zhou, J. Li, C. Wang, H. Wu, C. Zhao, and G. Teng, “Crop disease identification and interpretation method based on multimodal deep learning,” Comput Electron Agric, vol. 189, p. 106408, 2021, doi: https://doi.org/10.1016/j.compag.2021.106408.
[9]Z. A. Khan, W. Ullah, A. Ullah, S. Rho, M. Y. Lee, and S. W. Baik, “An Adaptive Filtering Technique for Segmentation of Tuberculosis in Microscopic Images,” in Proceedings of the 4th International Conference on Natural Language Processing and Information Retrieval, in NLPIR ’20. New York, NY, USA: Association for Computing Machinery, 2021, pp. 184–187. doi: 10.1145/3443279.3443283.
[10]R. Ullah et al., “A Real-Time Framework for Human Face Detection and Recognition in CCTV Images,” Math Probl Eng, vol. 2022, p. 3276704, 2022, doi: 10.1155/2022/3276704.
[11]H. Al-Hiary, S. Bani-Ahmad, M. Reyalat, M. Braik, and Z. Alrahamneh, “Fast and Accurate Detection and Classification of Plant Diseases,” 2011.
[12]T. N. Nguyen, S. Lee, H. Nguyen-Xuan, and J. Lee, “A novel analysis-prediction approach for geometrically nonlinear problems using group method of data handling,” Comput Methods Appl Mech Eng, vol. 354, pp. 506–526, 2019, doi: https://doi.org/10.1016/j.cma.2019.05.052.
[13]Faithpraise, Fina, and C. Chatwin, “Automatic plant pest detection and recognition using k-means clustering algorithm and correspondence filters) Automatic plant pest detection and recognition using k-means clustering algorithm and correspondence filters,” Int J Adv Biotechnol Res, vol. 4, no. 2, pp. 189–199, 2013, [Online]. Available: http://sro.sussex.ac.uk
[14]T. Rumpf, A.-K. Mahlein, U. Steiner, E.-C. Oerke, H.-W. Dehne, and L. Plümer, “Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance,” Comput Electron Agric, vol. 74, no. 1, pp. 91–99, 2010, doi: https://doi.org/10.1016/j.compag.2010.06.009.
[15]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, 2022, doi: 10.3390/s22249749.
[16]S. Azfar, A. Nadeem, A. Hassan, and A. B. Shaikh, “Pest Detection and Control Techniques Using Wireless Sensor Network: A Review,” J Entomol Zool Stud, vol. 3, no. 2, 2015, [Online]. Available: https://www.researchgate.net/publication/275155897
[17]T. Kasinathan, D. Singaraju, and S. R. Uyyala, “Insect classification and detection in field crops using modern machine learning techniques,” Information Processing in Agriculture, vol. 8, no. 3, pp. 446–457, 2021, doi: https://doi.org/10.1016/j.inpa.2020.09.006.
[18]S. H. Chiwamba et al., “An Application of Machine Learning Algorithms in Automated Identification and Capturing of Fall Armyworm (FAW) Moths in the Field,” in PROCEEDINGS OF THE ICTSZ INTERNATIONAL CONFERENCE IN ICTs, 2018. [Online]. Available: https://www.researchgate.net/publication/331935302
[19]A. Tageldin, D. Adly, H. Mostafa, and H. S. Mohammed, “Applying Machine Learning Technology in the Prediction of Crop Infestation with Cotton Leafworm in Greenhouse,” bioRxiv, 2020, doi: 10.1101/2020.09.17.301168.
[20]S. P. Mohanty, D. P. Hughes, and M. Salathé, “Using deep learning for image-based plant disease detection,” Front Plant Sci, vol. 7, no. September, Sep. 2016, doi: 10.3389/fpls.2016.01419.
[21]A. Kaya, A. S. Keceli, C. Catal, H. Y. Yalic, H. Temucin, and B. Tekinerdogan, “Analysis of transfer learning for deep neural network based plant classification models,” Comput Electron Agric, vol. 158, pp. 20–29, 2019, doi: https://doi.org/10.1016/j.compag.2019.01.041.
[22]M. I. Dinata, S. Mardi Susiki Nugroho, and R. F. Rachmadi, “Classification of Strawberry Plant Diseases with Leaf Image Using CNN,” in 2021 International Conference on Artificial Intelligence and Computer Science Technology (ICAICST), 2021, pp. 68–72. doi: 10.1109/ICAICST53116.2021.9497830.
[23]M. Muhathir, M. H. Santoso, and R. Muliono, “Analysis Naïve Bayes In Classifying Fruit by Utilizing Hog Feature Extraction,” JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING, vol. 4, no. 1, pp. 151–160, Jul. 2020, doi: 10.31289/jite.v4i1.3860.
[24]C. T. Selvi, R. S. Sankara Subramanian, and R. Ramachandran, “Weed Detection in Agricultural fields using Deep Learning Process,” in 2021 7th International Conference on Advanced Computing and Communication Systems (ICACCS), 2021, pp. 1470–1473. doi: 10.1109/ICACCS51430.2021.9441683.
[25]S. N. A. M. Johari, S. Khairunniza-Bejo, A. R. M. Shariff, N. A. Husin, M. M. M. Masri, and N. Kamarudin, “Automatic Classification of Bagworm, Metisa plana (Walker) Instar Stages Using a Transfer Learning-Based Framework,” Agriculture, vol. 13, no. 2, 2023, doi: 10.3390/agriculture13020442.
[26]M. A. Malek, S. S. Reya, M. Z. Hasan, and S. Hossain, “A Crop Pest Classification Model Using Deep Learning Techniques,” in 2021 2nd International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), 2021, pp. 367–371. doi: 10.1109/ICREST51555.2021.9331154.
[27]A. Kamilaris and F. X. Prenafeta-Boldú, “Deep learning in agriculture: A survey,” Comput Electron Agric, vol. 147, pp. 70–90, 2018, doi: https://doi.org/10.1016/j.compag.2018.02.016.
[28]M. Tudi et al., “Agriculture development, pesticide application and its impact on the environment,” International Journal of Environmental Research and Public Health, vol. 18, no. 3. MDPI AG, pp. 1–24, Feb. 01, 2021. doi: 10.3390/ijerph18031112.
[29]N. Denan et al., “Predation of potential insect pests in oil palm plantations, rubber tree plantations, and fruit orchards,” Ecol Evol, vol. 10, no. 2, pp. 654–661, Jan. 2020, doi: 10.1002/ece3.5856.
[30]G. Chung, “Effect of Pests and Diseases on Oil Palm Yield,” 2012, pp. 163–210. doi: 10.1016/B978-0-9818936-9-3.50009-5.
[31]E. C. Tetila et al., “Detection and classification of soybean pests using deep learning with UAV images,” Comput Electron Agric, vol. 179, p. 105836, 2020, doi: https://doi.org/10.1016/j.compag.2020.105836.
[32]Y. Ai, C. Sun, J. Tie, and X. Cai, “Research on recognition model of crop diseases and insect pests based on deep learning in harsh environments,” IEEE Access, vol. 8, pp. 171686–171693, 2020, doi: 10.1109/ACCESS.2020.3025325.
[33]D. C. Amarathunga, J. Grundy, H. Parry, and A. Dorin, “Methods of insect image capture and classification: A Systematic literature review,” Smart Agricultural Technology, vol. 1. Elsevier B.V., Dec. 01, 2021. doi: 10.1016/j.atech.2021.100023.
[34]J. G. A. Barbedo, “Detecting and Classifying Pests in Crops Using Proximal Images and Machine Learning: A Review,” AI, vol. 1, no. 2, pp. 312–328, Jun. 2020, doi: 10.3390/ai1020021.
[35]M. Ula, M. Muhathir, and I. Sahputra, “Optimization of Multilayer Perceptron Hyperparameter in Classifying Pneumonia Disease Through X-Ray Images with Speeded-Up Robust Features Extraction Method,” IJACSA) International Journal of Advanced Computer Science and Applications, vol. 13, no. 10, 2022, [Online]. Available: www.ijacsa.thesai.org
[36]M. Muhathir, M. F. D. Ryandra, R. B. Y. Syah, N. Khairina, and R. Muliono, “Convolutional Neural Network (CNN) of Resnet-50 with Inceptionv3 Architecture in Classification on X-Ray Image,” in Artificial Intelligence Application in Networks and Systems, P. Silhavy Radek and Silhavy, Ed., Cham: Springer International Publishing, 2023, pp. 208–221.
[37]M. N. Ahmad, A. R. M. Shariff, I. Aris, and I. Abdul Halin, “A Four Stage Image Processing Algorithm for Detecting and Counting of Bagworm, Metisa plana Walker (Lepidoptera: Psychidae),” Agriculture, vol. 11, no. 12, 2021, doi: 10.3390/agriculture11121265.
[38]S. N. A. M. Johari, S. Khairunniza-Bejo, A. R. M. Shariff, N. A. Husin, M. M. M. Masri, and N. Kamarudin, “Detection of Bagworm Infestation Area in Oil Palm Plantation Based on UAV Remote Sensing Using Machine Learning Approach,” Agriculture, vol. 13, no. 10, 2023, doi: 10.3390/agriculture13101886.
[39]M. Muhathir, N. Khairina, R. Karenina Isabella Barus, M. Ula, and I. Sahputra, “Preserving Cultural Heritage Through AI: Developing LeNet Architecture for Wayang Image Classification,” IJACSA) International Journal of Advanced Computer Science and Applications, vol. 14, no. 9, 2023, [Online]. Available: www.ijacsa.thesai.org
[40]R. Syuhada, Muhathir, N. Khairina, R. Muliono, Susilawati and Z. Sembiring, "Analyzing the Effectiveness of VGG Deep Learning Architecture for Mushroom Type Classification," 2023 International Conference of Computer Science and Information Technology (ICOSNIKOM), Binjia, Indonesia, 2023, pp. 1-6, doi: 10.1109/ICoSNIKOM60230.2023.10364551.
[41]S. N, N. Emmanuel, K. Sri Phani Krishna, C. Chinnabbai, and K. Uma Krishna, “Artificial Intelligence for Classification and Detection of Major Insect Pests of Brinjal,” Indian Journal of Entomology, vol. 85, no. 3, pp. 563–566, Sep. 2023, doi: 10.55446/IJE.2023.1388.
[42]Z. Shanwen, X. Xinhua, Q. Guohong, and S. Yu, “Detecting the pest disease of field crops using deformable VGG-16 model,” Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), vol. 37, no. 18, pp. 188–194, 2021, doi: 10.11975/j.issn.1002-6819.2021.18.022
[43]Z. Liu, J. Gao, G. Yang, H. Zhang, and Y. He, “Localization and Classification of Paddy Field Pests using a Saliency Map and Deep Convolutional Neural Network,” Sci Rep, vol. 6, Feb. 2016, doi: 10.1038/srep20410.
[44]J. G. A. Barbedo and G. B. Castro, “Influence of image quality on the identification of psyllids using convolutional neural networks,” Biosyst Eng, vol. 182, pp. 151–158, 2019, doi: https://doi.org/10.1016/j.biosystemseng.2019.04.007.
[45]A. N. Alves, W. S. R. Souza, and D. L. Borges, “Cotton pests classification in field-based images using deep residual networks,” Comput Electron Agric, vol. 174, p. 105488, 2020, doi: https://doi.org/10.1016/j.compag.2020.105488.