IoT-Enabled Pest Identification and Classification with New Meta-Heuristic-Based Deep Learning Framework

References

• Ai, Y., C. Sun, J. Tie, and X. Cai. 2020. Research on recognition model of crop diseases and insect pests based on deep learning in harsh environments. IEEE Access 8:171686–93. doi:10.1109/ACCESS.2020.3025325.
   Web of Science ®Google Scholar
• Aly, G. H., M. Marey, S. A. El-Sayed, and M. F. Tolba. 2021. YOLO based breast masses detection and classification in full-field digital mammograms. Computer Methods and Programs in Biomedicine 200:105823. doi:10.1016/j.cmpb.2020.105823.
   PubMed Web of Science ®Google Scholar
• Ambati, L. S., K. Narukonda, G. R. Bojja, and D. Bishop. 2020. Factors influencing the adoption of artificial intelligence in organizations – from an employee’s perspective, MWAIS 2020 Proceedings, 20.
   Google Scholar
• Ayan, E., H. Erbay, and F. Varçın. 2020. Crop pest classification with a genetic algorithm-based weighted ensemble of deep convolutional neural networks. Computers and Electronics in Agriculture 179:105809. doi:10.1016/j.compag.2020.105809.
   Web of Science ®Google Scholar
• Ayaz, M., M. Ammad-Uddin, Z. Sharif, A. Mansour, and E.-H. M. Aggoune. 2019. Internet-of-things (IoT)-based smart agriculture: Toward making the fields talk. IEEE Access 7:129551–83. doi:10.1109/ACCESS.2019.2932609.
   Web of Science ®Google Scholar
• Bhoi, S. K., K. K. Jena, S. K. Panda, H. V. Long, R. Kumar, P. Subbulakshmi, and H. B. Jebreen. 2021. An internet of things assisted unmanned aerial vehicle based artificial intelligence model for rice pest detection. Microprocessors and Microsystems 80:103607. doi:10.1016/j.micpro.2020.103607.
   Web of Science ®Google Scholar
• Bojja, G. R., M. Ofori, J. Liu, and L. S. Ambati. 2020. Early public outlook on the coronavirus disease (COVID-19): A social media study. AMCIS 2020 Proceedings, 3:1–10.
   Google Scholar
• Chaudhary, A., R. Thakur, S. Kolhe, and R. Kama. 2020. A particle swarm optimization based ensemble for vegetable crop disease recognition. Computers and Electronics in Agriculture 178:105747. doi:10.1016/j.compag.2020.105747.
   Web of Science ®Google Scholar
• Chen, C. J., Y. Y. Huang, Y. S. Li, C.-Y. Chang, and Y.-M. Huang. 2020. An AIoT based smart agricultural system for pests detection. IEEE Access 8:180750–61. doi:10.1109/ACCESS.2020.3024891.
   Web of Science ®Google Scholar
• Chen, C.-J., Y.-Y. Huang, Y.-S. Li, Y.-C. Chen, C.-Y. Chang, and Y.-M. Huang. 2021a. Identification of fruit tree pests with deep learning on embedded drone to achieve accurate pesticide spraying. IEEE Access 9:21986–97. doi:10.1109/ACCESS.2021.3056082.
   Web of Science ®Google Scholar
• Chen, J., W. Chen, A. Zeb, D. Zhang, and Y. A. Nanehkaran. 2021b. Crop pest recognition using attention-embedded lightweight network under field conditions. Applied Entomology and Zoology 56 (4):427–42. doi:10.1007/s13355-021-00732-y.
   Web of Science ®Google Scholar
• Cheng, X., Y. Zhang, Y. Chen, Y. Wu, and Y. Yue. 2017. Pest identification via deep residual learning in complex background. Computers and Electronics in Agriculture 141:351–6. doi:10.1016/j.compag.2017.08.005.
   Web of Science ®Google Scholar
• Divya, D., and T. R. Ganeshbabu. 2020. Fitness adaptive deer hunting-based region growing and recurrent neural network for melanoma skin cancer detection. International Journal of Imaging Systems and Technology 30 (3):731–52.
   Web of Science ®Google Scholar
• Esgario, J. G. M., P. Tassis, and R. A. Krohling. 2021. An app to assist farmers in the identification of diseases and pests of coffee leaves using deep learning. Information Processing in Agriculture 9 (1):38–47.
   Google Scholar
• Gutierrez, A., A. Ansuategi, L. Susperregi, C. Tubío, I. Rankić, and L. Lenža. 2019. A benchmarking of learning strategies for pest detection and identification on tomato plants for autonomous scouting robots using internal databases. Journal of Sensors 2019:1–15. doi:10.1155/2019/5219471.
   Web of Science ®Google Scholar
• Hashim, F. A., E. H. Houssein, K. Hussain, M. S. Mabrouk, and W. Al-Atabany. 2022. Honey badger algorithm: New metaheuristic algorithm for solving optimization problems. Mathematical and Computers Simulation 192:84–110. doi:10.1016/j.matcom.2021.08.013.
   Web of Science ®Google Scholar
• Karar, M. S., F. Alsunaydi, S. Albusaymi, and S. Alotaibi. 2021. A new mobile application of agricultural pests recognition using deep learning in cloud computing system. Alexandria Engineering Journal 60 (5):4423–32. doi:10.1016/j.aej.2021.03.009.
   Web of Science ®Google Scholar
• Kasinathan, T., and S. R. Uyyala. 2021. Machine learning ensemble with image processing for pest identification and classification in field crops. Neural Computing and Applications 33 (13):7491–504. doi:10.1007/s00521-020-05497-z.
   Web of Science ®Google Scholar
• Khanramaki, M., E. A. Asli-Ardeh, and E. Kozegar. 2021. Citrus pests classification using an ensemble of deep learning models. Computers and Electronics in Agriculture 186:106192. doi:10.1016/j.compag.2021.106192.
   Web of Science ®Google Scholar
• Kong, J., H. Wang, C. Yang, X. Jin, M. Zuo, and X. Zhang. 2022. A spatial feature-enhanced attention neural network with high-order pooling representation for application in pest and disease recognition. Agriculture 12 (4):500. doi:10.3390/agriculture12040500.
   Web of Science ®Google Scholar
• Li, W., D. Wang, M. Li, Y. Gao, J. Wu, and X. Yang. 2021. Field detection of tiny pests from sticky trap images using deep learning in agricultural greenhouse. Computers and Electronics in Agriculture 183:106048. doi:10.1016/j.compag.2021.106048.
   Web of Science ®Google Scholar
• Liu, L., R. Wang, C. Xie, P. Yang, F. Wang, S. Sudirman, and W. Liu. 2019. PestNet: An end-to-end deep learning approach for large-scale multi-class pest detection and classification. IEEE Access 7:45301–12. doi:10.1109/ACCESS.2019.2909522.
   Web of Science ®Google Scholar
• Quy, V. K., N. V. Hau, D. V. Anh, N. M. Quy, N. T. Ban, S. Lanza, G. Randazzo, and A. Muzirafuti. 2022. IoT-enabled smart agriculture: architecture, applications, and challenges. Applied Sciences 12 (7):3396. doi:10.3390/app12073396.
   Google Scholar
• Rahman, C. R., P. S. Arko, M. E. Ali, M. A. Iqbal Khan, S. H. Apon, F. Nowrin, and A. Wasif. 2020. Identification and recognition of rice diseases and pests using convolutional neural networks. Biosystems Engineering. 194:112–20. doi:10.1016/j.biosystemseng.2020.03.020.
   Web of Science ®Google Scholar
• Rustia, D. J. A., J.-J. Chao, J.-Y. Chung, and T.-T. Lin. 2019. An online unsupervised deep learning approach for an automated pest insect monitoring system. In ASABE Annu. Int. Meet.
   Google Scholar
• Rustia, D. J. A., C.-Y. Lu, J.-J. Chao, Y.-F. Wu, J.-Y. Chung, J.-C. Hsu, and T.-T. Lin. 2021. Online semi-supervised learning applied to an automated insect pest monitoring system. Biosystems Engineering 208:28–44. doi:10.1016/j.biosystemseng.2021.05.006.
   Web of Science ®Google Scholar
• Tabjula, J., S. Kalyani, P. Rajagopal, and B. Srinivasan. 2021a. Statistics-based baseline-free approach for rapid inspection of delamination in composite structures using ultrasonic guided waves. Structural Health Monitoring. doi:10.1177/14759217211073335
   PubMed Web of Science ®Google Scholar
• Tabjula, J. L., S. Kanakambaran, S. Kalyani, P. Rajagopal, and B. Srinivasan. 2021b. Outlier analysis for defect detection using sparse sampling in guided wave structural health monitoring. Structural Control and Health Monitering 28 (3).
   PubMed Web of Science ®Google Scholar
• Tetila, E. C., B. B. Machado, G. Astolfi, N. A. S. Belete, W. P. Amorim, A. R. Roel, and H. Pistori. 2020. Detection and classification of soybean pests using deep learning with UAV images. Computers and Electronics in Agriculture 179:105836. doi:10.1016/j.compag.2020.105836.
   Web of Science ®Google Scholar
• Thenmozhi, K., and U. S. Reddy. 2019. Crop pest classification based on deep convolutional neural network and transfer learning. Computers and Electronics in Agriculture 164 (104906):104906. doi:10.1016/j.compag.2019.104906.
   Web of Science ®Google Scholar
• Turkoglu, M., D. Hanbay, and A. Sengur. 2022a. Multi-model LSTM-based convolutional neural networks for detection of apple diseases and pests. Journal of Ambient Intelligence and Humanized Computing 13 (7):3335–45. doi:10.1007/s12652-019-01591-w.
   Web of Science ®Google Scholar
• Turkoglu, M., B. Yanikoğlu, and D. Hanbay. 2022b. PlantDiseaseNet: Convolutional neural network ensemble for plant disease and pest detection. Signal, Image and Video Processing 16 (2):301–9. doi:10.1007/s11760-021-01909-2.
   Web of Science ®Google Scholar
• Wang, F., R. Wang, C. Xie, J. Zhang, R. Li, and L. Liu. 2021. Convolutional neural network based automatic pest monitoring system using hand-held mobile image analysis towards non-site-specific wild environment. Computers and Electronics in Agriculture 187:106268. doi:10.1016/j.compag.2021.106268.
   Web of Science ®Google Scholar
• Wang, F., R. Wang, C. Xie, P. Yang, and L. Liu. 2020a. Fusing multi-scale context-aware information representation for automatic in-field pest detection and recognition. Computers and Electronics in Agriculture 169:105222. doi:10.1016/j.compag.2020.105222.
   Web of Science ®Google Scholar
• Wang, J., Y. Li, H. Feng, L. Ren, X. Du, and J. Wu. 2020b. Common pests image recognition based on deep convolutional neural network. Computers and Electronics in Agriculture 179:105834. doi:10.1016/j.compag.2020.105834.
   Web of Science ®Google Scholar
• Wankhede, J., P. Sambandam, and M. Kumar. 2021. Effective prediction of heart disease using hybrid ensemble deep learning and tunicate swarm algorithm. Journal of Biomolecular Structure and Dynamics. doi:10.1080/07391102.2021.1987328.
   PubMed Web of Science ®Google Scholar
• Xie, C., R. Wang, J. Zhang, P. Chen, W. Dong, R. Li, T. Chen, and H. Chen. 2018. Multi-level learning features for automatic classification of field crop pests. Computers and Electronics in Agriculture 152:233–41. doi:10.1016/j.compag.2018.07.014.
   Web of Science ®Google Scholar
• Yue, Y., X. Cheng, D. Zhang, Y. Wu, Y. Zhao, Y. Chen, G. Fan, and Y. Zhang. 2018. Deep recursive super resolution network with Laplacian Pyramid for better agricultural pest surveillance and detection. Computers and Electronics in Agriculture 150:26–32. doi:10.1016/j.compag.2018.04.004.
   Web of Science ®Google Scholar