Exploring multiple optimization algorithms in transfer learning with EfficientNet models for agricultural insect classification
,
,
,
and
Article Sidebar
Full Text: 
PDF
Main Article Content
Abstract
Dangerous insects are a significant risk to the global agricultural industry, threatening food security, economic stability, and crop quality. This study investigates the impact of multiple optimization algorithms within transfer learning, employing EfficientNet models for the classification of agricultural insects. The explored optimization algorithms include Adam, Adamax, AdamW, RMSprop, and SGD, while utilizing the EfficientNetB0, EfficientNetB3, EfficientNetB5, and EfficientNetB7 architectures. Experimental results show notable performance differences between optimization algorithms across all EfficiencyNet models in the study. Among the measured metrics are precision, recall, f1-score, accuracy, and loss, the AdamW optimizer consistently demonstrates superior performance compared to other algorithms. The findings underscore the critical influence of optimization algorithms in enhancing classification accuracy and convergence within transfer learning scenarios. Additionally, the study employs various visualization techniques, such as Gradient-weighted Class Activation Mapping (Grad-CAM) to enhance the interpretation of the image classification model’s results. By focusing on these methodologies, this research aims to improve the model’s performance, optimize its capabilities, and ultimately contribute to effective pest management strategies in agriculture, safeguarding crop yields, farmer livelihoods, and global food security.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
References
Ahn, S., Kim, J., Park, S. Y., & Cho, S. (2020). Explaining deep learning-based traffic classification using a genetic algorithm. IEEE Access, 9, 4738-4751.
Bottou, L. (2010). Large-scale machine learning with stochastic gradient descent. In Proceedings of COMPSTAT'2010: 19th International Conference on Computational StatisticsParis France, August 22-27, 2010 Keynote, Invited and Contributed Papers (pp. 177-186). Physica-Verlag HD.
Gaur, L., Bhandari, M., Razdan, T., Mallik, S., & Zhao, Z. (2022). Explanation-driven deep learning model for prediction of brain tumour status using MRI image data. Frontiers in Genetics, 13, 822666.
Gulzar, Y. (2023). Fruit image classification model based on MobileNetV2 with deep transfer learning technique. Sustainability, 15(3), 1906.
Gunning, D., & Aha, D. (2019). DARPA’s explainable artificial intelligence (XAI) program. AI magazine, 40(2), 44-58.
Hossain, M. E., Shahrukh, S., & Hossain, S. A. (2022). Chemical fertilizers and pesticides: impacts on soil degradation, groundwater, and human health in Bangladesh. In Environmental Degradation: Challenges and Strategies for Mitigation (pp. 63-92). Cham: Springer International Publishing.
Ikromovich, H. O., & Mamatkulovich, B. B. (2023). Facial recognition using transfer learning in the deep CNN. Open Access Repository, 4(3), 502-507.
Jain, T. R. (2023). Dangerous farm insects dataset. https://www.kaggle.com/datasets/tarundalal/dangerous-insects-dataset.
Kathamuthu, N. D., Subramaniam, S., Le, Q. H., Muthusamy, S., Panchal, H., Sundararajan, S. C. M., ... & Zahra, M. M. A. (2023). A deep transfer learning-based convolution neural network model for COVID-19 detection using computed tomography scan images for medical applications. Advances in Engineering Software, 175, 103317.
Kingma, D. P. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980.
Mahmud, T., Barua, K., Barua, A., Das, S., Basnin, N., Hossain, M. S., ... & Sharmen, N. (2023, August). Exploring deep transfer learning ensemble for improved diagnosis and classification of Alzheimer’s disease. In International Conference on Brain Informatics (pp. 109-120). Cham: Springer Nature Switzerland.
Loshchilov, I., & Hutter, F. (2017). Fixing weight decay regularization in adam. arXiv preprint arXiv:1711.05101, 5.
Parthiban, S., Moorthy, S., Sabanayagam, S., Shanmugasundaram, S., Naganathan, A., Annamalai, M., & Balasubramanian, S. (2023). Deep learning based recognition of plant diseases. In Computer Vision and Machine Intelligence Paradigms for SDGs: Select Proceedings of ICRTAC-CVMIP 2021 (pp. 83-93). Singapore: Springer Nature Singapore.
Park, Y. H., Choi, S. H., Kwon, Y. J., Kwon, S. W., Kang, Y. J., & Jun, T. H. (2023). Detection of soybean insect pest and a forecasting platform using deep learning with unmanned ground vehicles. Agronomy, 13(2), 477.
Quach, L. D., Nguyen, K. Q., Nguyen, A. Q., Thai-Nghe, N., & Nguyen, T. G. (2023). Explainable deep learning models with gradient-weighted class activation mapping for smart agriculture. IEEE Access, 11(August), 83752-83762.
Saeed, A., Abdel-Aziz, A. A., Mossad, A., Abdelhamid, M. A., Alkhaled, A. Y., & Mayhoub, M. (2023). Smart detection of tomato leaf diseases using transfer learning-based convolutional neural networks. Agriculture, 13(1), 139.
Samek, W. (2017). Explainable artificial intelligence: Understanding, visualizing and interpreting deep learning models. arXiv preprint arXiv:1708.08296.
Savary, S., Ficke, A., Aubertot, J. N., & Hollier, C. (2012). Crop losses due to diseases and their implications for global food production losses and food security. Food Security, 4(4), 519-537.
Schutte, K., Moindrot, O., Hérent, P., Schiratti, J. B., & Jégou, S. (2021). Using stylegan for visual interpretability of deep learning models on medical images. arXiv preprint arXiv:2101.07563.
Sekharamantry, P. K., Melgani, F., & Malacarne, J. (2023). Deep learning-based apple detection with attention module and improved loss function in YOLO. Remote Sensing, 15(6), 1516.
Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-cam: Visual explanations from deep networks via gradient-based localization. In Proceedings of the IEEE international conference on computer vision (pp. 618-626).
Singh, A., Sengupta, S., & Lakshminarayanan, V. (2020). Explainable deep learning models in medical image analysis. Journal of Imaging, 6(6), 52.
Shukla, S., Upadhyay, D., Mishra, A., Jindal, T., & Shukla, K. (2022). Challenges faced by farmers in crops production due to fungal pathogens and their effect on Indian economy. In Fungal diversity, ecology and control management (pp. 495-505). Singapore: Springer Nature Singapore.
Srivastav, A. L. (2020). Chemical fertilizers and pesticides: role in groundwater contamination. In Agrochemicals detection, treatment and remediation (pp. 143-159). Butterworth-Heinemann.
The United Nations. (2023). Population. https://www.un.org/en/global-issues/population.
Torrey, L., & Shavlik, J. (2010). Transfer learning. In Handbook of research on machine learning applications and trends: algorithms, methods, and techniques (pp. 242-264). IGI Global.
Vo, H. T., Quach, L. D., & Hoang, T. N. (2023a). Ensemble of deep learning models for multi-plant disease classification in smart farming. International Journal of Advanced Computer Science and Applications, 14(5).
Vo, H. T., Thien, N. N., & Mui, K. C. (2023b). Tomato disease recognition: Advancing accuracy through xception and bilinear pooling fusion. International Journal of Advanced Computer Science and Applications, 14(8).
Wu, J. (2017). Introduction to convolutional neural networks. National Key Lab for Novel Software Technology. Nanjing University. China, 5(23), 495.
Yonbawi, S., Alahmari, S., Daniel, R., Lydia, E. L., Ishak, M. K., Alkahtani, H. K., ... & Mostafa, S. M. (2023). Modified Metaheuristics with Transfer Learning Based Insect Pest Classification for Agricultural Crops. Computer Systems Science & Engineering, 46(3).
Zaller, J. G., & Zaller, J. G. (2020). Pesticide impacts on the environment and humans. Daily Poison: Pesticides-An Underestimated Danger, 127-221.
Zou, F., Shen, L., Jie, Z., Zhang, W., & Liu, W. (2019). A sufficient condition for convergences of Adam and RMSProp. In Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition (pp. 11127-11135).