Authors:
Ananjan Maiti,Dipankar Basu,Indranil Sarkar,Jyoti Sekhar Banerjee,Atri Adhikari, Panagiotis Sarigiannidis,DOI NO:
https://doi.org/10.26782/jmcms.2025.09.00002Keywords:
Invasive Insects,Acoustic features,Classification,Deep Learning,Bioacoustics Pest Management,Mel Frequency Cepstral Coefficients (MFCC),Abstract
This study presents a novel framework for the early identification of invasive insect species using advanced bioacoustic analysis integrated with deep learning algorithms. In this paper, we develop a new method that uses spectral subtraction with wingbeat frequency modulation to identify invasive insects with high acoustic accuracy. We analyze acoustic signatures using a robust pipeline that involves adaptive noise cancellation, spectral subtraction with wingbeat frequency modulation-based features, and a deep learning model. The system shows great potential in classification, with an average 96% to 98% accuracy in a data set of 17 species of insects, six of which are invasive. Significantly, our proposed solution does not disrupt the natural environment by using noninvasive surveillance, providing real-time identification. In addition, the work presents several methodological enhancements, for example, the hybrid noise reduction approach that leads to a signal-to-noise ratio gain of 9.64 dB and the custom deep learning model that was fine-tuned through systematic hyperparameter optimization. These advances greatly surpass current classification methodologies and have broad potential for applications in agriculture, defense, ecological studies, and invasive species control. Our results provide a solid basis for using acoustic ecology with machine learning for entomological studies and pest control.Refference:
I. Barbedo, J.G.A.: Detecting and classifying pests in crops using proximal images and machine learning: A review. Biosystems Engineering, 193, 1-16 (2020).
II. Basak, S., Ghosh, A., Saha, D., Dutta, C., Maiti, A.: Insects Sound Classification with Acoustic Features and k-Nearest Algorithm. In 12th Inter-University Engineering, Science & Technology Academic Meet – (2022). 10.36375/prepare_u.foset.a291
III. Bhuiyan, T.H.: Insect classification and explainability from image data via deep learning techniques. PhD thesis, University of South Florida (2023). https://digitalcommons.usf.edu/etd/9957/
IV. Boulila, W., Alzahem, A., Koubaa, A., Benjdira, B., Ammara, A.: Early detection of red palm weevil infestations using deep learning classification of acoustic signals. Computers and Electronics in Agriculture 194, 106782 (2023).
V. Chen, Y., Lin, Z., Zhao, X., Wang, G., Gu, Y.: Deep learning-based classification of hyperspectral data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 7(6), 2094-2107 (2014).
VI. Cooperband, M.F., Wickham, J.D., Warden, M.L.: Factors guiding the orientation of nymphal spotted lanternfly, Lycorma delicatula. Insects 14(3), 279-279 (2023).
VII. Dasgupta, S., Das, T., Saha, D., Dutta, C., Maiti, A.: Classification of Indian Herbal Leaf with Random Forest Classifier. FOSET – 12th Inter-University Engineering, Science & Technology Academic Meet, Guru Nanak Institute of Technology, Kolkata, India. 10.36375/prepare_u.foset.a302
VIII. Debrah, S.K., Anankware, P.J., Asomah, S., Ofori, D.O.: Challenges associated with Rhynchophorus phoenicis Fabricius (Coleoptera: Curculionidae) farming. Journal of Insects as Food and Feed 9(1), 15-24 (2023).
IX. Demertzis, K., Iliadis, L.S., Anezakis, V.D.: Extreme deep learning in biosecurity: the case of machine hearing for marine species identification. Journal of Information and Telecommunication 2(4), 492-510 (2018).
X. Dong, Q., Sun, L., Han, T., Cai, M., Gao, C.: Pestlite: A novel YOLO-based deep learning technique for crop pest detection. Agriculture 14(2), 228 (2024).
XI. Dutta, A., Mitra, C., Ghosh, A., Sarkar, I., Maiti, A.: Advances in Audio Noise Cancellation Through Deep Learning, Hybrid Approaches, and Real-Time Applications. National Conference on Research Advancements and Innovations in Computing, Communications, and Information Technologies (RAICCIT-2025), ISBN: 978-81-973699-3-3 (2025).
XII. Ganchev, T., Potamitis, I.: Automatic acoustic identification of singing insects. Bioacoustics 16(3), 281-328 (2007).
XIII. Hagenbucher, S., Eisenring, M., Meissle, M., Rathore, K.S., Romeis, J.: Constitutive and induced insect resistance in RNAi-mediated ultra-low gossypol cottonseed cotton. BMC Plant Biology 19(1) (2019).
XIV. Holmes, C.J.: Dehydration alters transcript levels in the mosquito midgut, likely facilitating rapid rehydration following a bloodmeal. Insects 14(3), 274-274 (2023).
XV. Huang, J.: Phytochrome b mediates dim-light-reduced insect resistance by promoting the ethylene pathway in rice. Plant Physiology 191(2), 1272-1287 (2022).
XVI. Karar, M.E., Alsunaydi, F., Albusaymi, S., Alotaibi, S.: A new mobile application of agricultural pests’ recognition using deep learning in cloud computing system. Alexandria Engineering Journal 60(5), 4423-4432 (2021).
XVII. Kawabata, A., Myers, R., Miyahira, M., Yamauchi, N., Nakamoto, S.T.: Field efficacy of spine-toram for the management of Hypothenemus hampei. Insects 14(3), 287-287 (2023).
XVIII. Khalighifar, A.: Application of deep learning to automated species identification systems. Biodiversity Information Science and Standards 4, 59007 (2020).
XIX. Kim, J.H., Park, S.J., Lee, K.B.: Deep learning-based insect sound classification system for early pest detection. Sensors 21(4), 1480 (2021).
XX. Le-Qing, Z., Zhen, Z.: An investigation in acoustic insect recognition. Oriental Insects 44(1), 415-428 (2010).
XXI. Li, L.: Succession patterns of sarcosaprophagous insects on pig carcasses in different months in Yangtze River Delta, China. Forensic Science International 342, 111518 (2023).
XXII. Li, Y., Wang, H., Dang, L.M., Sadeghi-Niaraki, A., Moon, H.: Crop pest recognition in natural scenes using convolutional neural networks. Computers and Electronics in Agriculture 169, 105174 (2020).
XXIII. Lima, M.C.F., Leandro, M.E.D.A., Valero, C., Pereira, L.C., Bazzo, C.O.G.: Automatic detection and monitoring of insect pests – a review. Sensors 20(21), 6265 (2020).
XXIV. Liu, T., Chen, W., Wu, W., Sun, C., Guo, W., Zhu, X.: Detection of invasive insect species using deep learning and bioacoustic analysis. Ecological Informatics 58, 101117 (2020).
XXV. Maiti, A., Dutta, C., Banerjee, J.S., Sarigiannidis, P.: AI for Infant Well-being: Advanced Techniques in Cry Interpretation and Monitoring. Journal of Mechanics of Continua and Mathematical Sciences 19 (2024).
XXVI. Nguyen, T., Nguyen, H., Ung, H., Ung, H., Nguyen, B.: Deep-wide learning assistance for insect pest classification. arXiv preprint arXiv:2409.10445 (2024).
XXVII. Noda, J.J., Travieso-Gonzalez, C.M., Sanchez-Rodriguez, D., Alonso-Hernandez, J.B.: Acoustic classification of singing insects based on MFCC/LFCC fusion. Applied Sciences 9(19), 4097 (2019).
XXVIII. Phung, Q.V., Ahmad, I., Habibi, D., Hinckley, S.: Automated insect detection using acoustic features based on sound generated from insect activities. Acoustics Australia 45(2), 445-451 (2017).
XXIX. Priyadarsini, J., Karthick, B. N., Karthick, K., Karthikeyan, B., & Mohan, S. (2019, March). Detection of PH value and Pest control for eco-friendly agriculture. In 2019 5th International Conference on Advanced Computing & Communication Systems (ICACCS) (pp. 801-804). IEEE.
XXX. Qing, Y., Lv, J., Liu, Q., Diao, G., Yang, B., Chen, H., Tang, J.: An insect imaging system to automate rice light-trap pest identification. Journal of Integrative Agriculture 11(6), 978–985 (2012).
XXXI. Shan, S.: A female-biased odorant receptor tuned to the lepidopteran sex pheromone in parasitoid Microplitis mediator guiding habitat of host insects. Journal of Advanced Research 43, 1-12 (2023).
XXXII. Thomas, J., Gorb, S.N., Buscher, T.H.: Influence of surface free energy of the substrate and flooded water on the attachment performance of stick insects. Journal of Experimental Biology 226(3) (2023).
XXXIII. Ullah, N., Khan, J.A., Alharbi, L.A., Raza, A., Khan, W., Ahmad, I.: An efficient approach for crops pests recognition and classification based on novel DeepPestNet deep learning model. IEEE Access 10, 73019–73032 (2022).
XXXIV. Zhang, W., Zhao, X., Li, Y.: A comprehensive study of deep learning methods for insect pest detection. Computers and Electronics in Agriculture 182, 106055 (2021).