Autonomous Pest Detection System using IoT Sensors and Predictive Analysis using Deep Learning and GenAI Techniques
Keywords:
Smart Agriculture, Pest Detection, IoT Sensors, Deep Learning Techniques, Generative AI ChatbotAbstract
To develop an autonomous smart pest detection system, the utilization of Smart IoT devices and adoption of deep learning techniques along with generative artificial intelligence techniques is necessary. Specifically designed for farming applications, the proposed system inbuilds an array of sensors comprising of an Infrared Sensor for nocturnal insect movement observation, an acoustic sensor for capturing sounds generated by the pests and environmental sensors like temperature, humidity, and light sensors, image sensors as well. The temperature sensor signifies its role in identifying optimal breeding conditions for pests prevalent in agricultural settings. The humidity sensor measures the moisture levels as per the pest activity and breeding. The light sensor monitors quantifies the pest behavior during different times of the day. These sensors are fabricated managing the pest ecosystem in farming. By using the widely adaptive deep learning techniques, such as Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM), the collected data is trained on the smart system for precise pest identification. The use of GenAI technique, further enhances the system by introducing a Chabot capable of interpreting observed data and supporting the potential pest-related trends according to the dynamic environmental conditions. Several test cases are performed on the proposed detection system, providing the fabricated smart system as an efficient one. The results and performance of the proposed system is well-suited for deployment in agricultural settings, with the potential that improves both the quality and quantity of crops in farming practices
Downloads
Metrics
References
Adavanne Sand T. Virtanen. (2017). Sound event detection using weakly labeled dataset with stacked convolutional and recurrent neural network. arXiv preprint arXiv:1710.02998.
Alexandridis A. K. and A. D. Zapranis. (2013). Wavelet neural networks: a practical guide. Neural Networks, 42:1–27.
Alphey L., M. Benedict, R. Bellini, G. G. Clark, D. A. Dame, M. W. Service, and S. L. Dobson. (2010). Sterile-insect methods for control of mosquito-borne diseases: an analysis. Vector-Borne and Zoonotic Diseases, 10(3):295–311.
Amores J.. (2013). Multiple instance classification: review, taxonomy and comparative study. Artificial Intelligence, 201:81–105.
Ana Sanz-Aguilar, I Cortés, I Gascón, O Martínez, S Ginard, G Tavecchia Modelling pest dynamics under uncertainty in pest detection: The case of the red palm weevil. Biological Invasions 22, 1635-1645.
Automated plant pest and disease detection using deep learning techniques: A review. Neural Computing and Applications, 32(3), 765-783. [7] Jayarathna, T., & Jayasena, H. (2020). Real-time pest and disease detection and prevention system using deep learning techniques. International Journal of Intelligent Engineering and Systems, 13(1), 72-82.
Azfar, S.; Nadeem, A.; Alkhodre, A.B.; Ahsan, K.; Mehmood, N.; Alghmdi, T.; Alsaawy, Y. Monitoring, detection and control techniques of agricultural pests and diseases using WSN: A Review. Int. J. Adv. Comput. Sci. Appl. 2018, 9, 424–433.
Bansal A. and A. Kumar. (2015). Heisenberg uncertainty inequality for Gabor transform. arXiv preprint arXiv:1507.00446.
Berger J. O.. (1985). Statistical Decision Theory and Bayesian Analysis. Springer Science & Business Media.
Bhatt S., D. J. Weiss, E. Cameron, D. Bisanzio, B. Mappin, U. Dalrymple, K. E. Battle, C. L. Moyes, A. Henry, P. A. Eckhoff, E. A. Wenger, O. Briet, M. A. Penny, T. A. Smith, A. Bennett, J. Yukich, T. P. Eisele, J. T. Griffin, C. A. Fergus, M. Lynch, F. Lindgren, J. M. Cohen, C. L. J. Murray, D. L. Smith, S. I. Hay, R. E. Cibulskis, and P. W. Gething. (2015). The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature, 526(7572): 207–211.
Bishop C. M. (1995) Neural Networks for Pattern Recognition. Oxford University Press, 1995.
Bishop C. M.. (2006). Pattern Recognition and Machine Learning. Springer, 2006.
Bottou L. Large-scale machine learning with stochastic gradient descent. In Proceedings of COMPSTAT’2010, pages 177–186. Springer, 2010.
Boureau Y.-L., J. Ponce, and Y. LeCun. A theoretical analysis of feature pooling in visual recognition. In Proceedings of the 27th International Conference on Machine Learning (ICML-10), pages 111–118, 2010.
Box G. E.. Science and statistics. Journal of the American Statistical Association, 71(356):791–799, 1976.
Cakır E., G. Parascandolo, T. Heittola, H. Huttunen, and T. Virtanen. Convolutional recurrent neural networks for polyphonic sound event detection. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 25(6):1291–1303, 2017.
Cakir E., T. Heittola, H. Huttunen, and T. Virtanen. Polyphonic sound event detection using multi label deep neural networks. In 2015 International Joint Conference on Neural Networks (IJCNN). IEEE, 2015.
Cao R., A. Cuevas, and W. G. Manteiga. A comparative study of several smoothing methods in density estimation. Computational Statistics & Data Analysis, 17(2): 153–176, 1994.
Cartwright M., G. Dove, A. E. Méndez Méndez, J. P. Bello, and O. Nov. Crowdsourcing multi-label audio annotation tasks with citizen scientists. In Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, page 292. ACM, 2019.
Caruana R., A. Munson, and A. Niculescu-Mizil. Getting the most out of ensemble selection. In Sixth International Conference on Data Mining (ICDM’06), pages 828–833. IEEE, 2006.
Chen P, Xiao Q, Zhang J, Xie C, Wang B (2020) Occurrence prediction of cotton pests and diseases by bidirectional long short-term memory networks with climate and atmosphere circulation. Comput Electron Agric 176:105612.
Chen Y., A. Why, G. Batista, A. Mafra-Neto, and E. Keogh. Flying insect classification with inexpensive sensors. Journal of Insect Behavior, 27(5):657–677, 2014.
Chen Y., B. Yang, and J. Dong. Time-series prediction using a local linear wavelet neural network. Neurocomputing, 69(4-6):449–465, 2006.
Chesmore E. and E. Ohya. Automated identification of field-recorded songs of four British grasshoppers using bioacoustic signal recognition. Bulletin of Entomological Research, 94(04):319–330, 2004.
Chew H.-G., R. E. Bogner, and C.-C. Lim. Dual ν-support vector machine with error rate and training size biasing. In Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 1269–1272, Salt Lake City, USA, May 2001.
Chollet F., Keras, 2015. URL https://keras.io. Accessed: 2018-06-07. (Cited on page 62.) C. Christopoulos, A. Skodras, and T. Ebrahimi. The JPEG2000 still image coding system: an overview. IEEE Transactions on Consumer Electronics, 46(4):1103– 1127, 2000.
Claesen M., F. De Smet, J. A. Suykens, and B. De Moor. Fast prediction with SVM models containing RBF kernels. arXiv preprint arXiv:1403.0736, 2014.
Clements A. N.. The Biology of Mosquitoes, Volume 2: Sensory Reception and Behaviour. CABI Publishing, 1999.
Clemins P. J., M. T. Johnson, K. M. Leong, and A. Savage. Automatic classification and speaker identification of African elephant (Loxodonta africana) vocalizations. The Journal of the Acoustical Society of America, 117(2):956–963, 2005.
Cobb A. D., A. G. Baydin, I. Kiskin, A. Markham, and S. J. Roberts. Semi-separable Hamiltonian Monte Carlo for inference in Bayesian neural networks. In Advances in Neural Information Processing Systems Workshop on Bayesian Deep Learning, 2019.
Cobb A. D., S. J. Roberts, and Y. Gal. Loss-calibrated approximate inference in Bayesian neural networks. arXiv preprint arXiv:1805.03901, 2018.
Cobb A. D.. The Practicalities of Scaling Bayesian Neural Networks to Real-World Applications. PhD thesis, University of Oxford, 2020.
Cody M. A.. The fast wavelet transform: beyond Fourier transforms. Dr. Dobb’s Journal, 17(4):16–28, 1992.
Cortes C. and V. Vapnik. Support-vector networks. Machine Learning, 20(3): 273–297, Sept. 1995.
Daubechies I., J. Lu, and H.-T. Wu. Synchrosqueezed wavelet transforms: an empirical mode decomposition-like tool. Applied and Computational Harmonic Analysis, 30(2):243–261, 2011.
Daubechies I.. The wavelet transform, time-frequency localization and signal analysis. IEEE Transactions on Information Theory, 36(5):961–1005, 1990.
Du P., W. A. Kibbe, and S. M. Lin. Improved peak detection in mass spectrum by incorporating continuous wavelet transform-based pattern matching. Bioinformatics, 22(17):2059–2065, 2006. doi: 10.1093/bioinformatics/btl355.
Farooq, M.S.; Riaz, S.; Abid, A.; Umer, T.; Zikria, Y.B. Role of IoT technology in agriculture: A systematic literature review. Electronics 2020, 9, 319.
Gaikwad, S.V. An innovative IoT based system for precision farming. Comput. Electron. Agric. 2021, 187, 106–116.
Gao, D.; Sun, Q.; Hu, B.; Zhang, S. A framework for agricultural pest and disease monitoring based on internet-of-things and unmanned aerial vehicles. Sensors 2020, 20, 1487.
Gogoi, D., & Bora, D. (2019). Pest Detection and Classification Using Deep Learning Techniques: A Review. International Journal of Recent Technology and Engineering, 8(2), 666-674.
Humphrey J, Bello J. P., and LeCun Y. (2013). Feature learning and deep architectures: new directions for music informatics. Journal of Intelligent Information Systems, 41(3):461–481.
Li, W.; Zheng, T.; Yang, Z.; Li, M.; Sun, C.; Yang, X. Classification and detection of insects from field images using deep learning for smart pest management: A systematic review. Ecol. Inform. 2021, 66, 101460.
Magarey, R.D. The NCSU/APHIS Plant Pest Forecasting System (NAPPFAST). In Pest Risk Modelling and Mapping for Invasive Alien Species; CABI: Wallingford, UK, 2015; pp. 82–96.
Mankin, R. Recent developments in the use of acoustic sensors and signal processing tools to target early infestations of red palm weevil in agricultural environments. Fla. Entomol. 2011, 94, 761.
Murali, S., & Sabarimalai Manikandan, S. (2021). Automated Pesticide Spray System Using Internet of Things and Image Processing Techniques. International Journal of Engineering and Advanced Technology, 10(4), 3703-3708.
Prasad, M. A., & Ramakrishna, A. V. (2020). A Review on Automated Pest Detection in Agricultural Crops Using Image Processing Techniques. International Journal of Emerging Technologies in Engineering Research, 8(7), 40-45.
Razzak, M. I., Naz, S., & Zaheer, A. (2018). Deep learning for plant disease detection: A comprehensive review. Computers and Electronics in Agriculture, 145, 228-237. Raza, S., Shaukat, A., & Al-Jumaily, A. (2020).
Rustia, D.J.A.; Lin, T.T. An IoT-based wireless imaging and sensor node system for remote greenhouse pest monitoring. Chem. Eng. Trans. 2017, 58, 601–606.
Warren, G. Gibson, and I. J. Russell. (2009). Sex recognition through midflight mating duets in culex mosquitoes is mediated by acoustic distortion. Current Biology, 19 (6):485–491
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to:
- Share — copy and redistribute the material in any medium or format
- Adapt — remix, transform, and build upon the material for any purpose, even commercially.
Terms:
- Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
- No additional restrictions — You may not apply legal terms or technological measures that legally restrict others from doing anything the license permits.
