References
- ABDULLAH, A. – ULLAH, M. I. – RAZA, A. B. M. – ARSHAD, M. – AFZAL, M. 2019. Host plant selection affects biological parameters in armyworm, Spodoptera litura (Lepidoptera: Noctuidae). In Pakistan Journal of Zoology, vol. 51, no. 6, pp. 2117–2123. DOI: http://dx.doi.org/10.17582/journal.pjz/2019.51.6.2117.2123
- BRÉVAULT, T. – NDIAYE, A. – BADIANE, D. – BAL, A. B. – SEMBENE, M. – SILVIE, P. – HARAN, J. 2018. First records of the fall armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae), in Senegal. In Entomologia Generalis, vol. 37, no. 2, pp. 129–142. DOI: https://doi.org/10.1127/entomologia/2018/0553
- DAHHAM, G. A. – Al-IRHAYIM, M. N. – Al-MISTAWI, K. E. – KHESSRO, M. K. 2023. Performance evaluation of artificial neural network modelling to a ploughing unit in various soil conditions. In Acta Technologica Agriculturae, vol. 26, no. 4, pp. 94–200. DOI: https://doi.org/10.2478/ata-2023-0026
- DAY, R. – ABRAHAMS, P. – BATEMAN, M. – BEALE, T. – CLOTTEY, V. – COCK, M. – COLMENAREZ, Y. – CORNIANI, N. – EARLY, R. – GODWIN, J. – GOMEZ, J. – MORENO, P. G. – MURPHY, S. T. – OPPONG-MENSAH, B. – PHIRI, N. – PRATT, C. – SILVESTRI, S. – WITT, A. 2017. Fall armyworm: Impacts and implications for Africa. In Outlooks on Pest Management, vol. 28, no. 5, pp. 196–201. DOI: https://doi.org/10.1564/v28_oct_02
- DE GROOTE, H. – KIMENJU, S. C. – MUNYUA, B. – PALMAS, S. – KASSIE, M. – BRUCE, A. 2020. Spread and impact of fall armyworm (Spodoptera frugiperda JE Smith) in maize production areas of Kenya. In Agriculture, Ecosystems & Environment, vol. 292, article no. 106804. DOI: https://doi.org/10.1016/j.agee.2019.106804
- GOERGEN, G. – KUMAR, P. L. – SANKUNG, S. B. – TOGOLA, A. – TAMO, M. 2016. First report of outbreaks of the fall armyworm Spodoptera frugiperda (J E Smith) (Lepidoptera, Noctuidae), a new alien invasive pest in West and Central Africa. In PloS One, vol. 11, no. 10, article no. e0165632. DOI: https://doi.org/10.1371/journal.pone.0165632
- KASINATHAN, T. – UYYALA, S. R. 2023. Detection of fall armyworm (Spodoptera frugiperda) in field crops based on mask R-CNN. In Signal, Image and Video Processing, vol. 17, pp. 2689–2695. DOI: https://doi.org/10.1007/s11760-023-02485-3
- LI, J. – JIANG, Z. – ZHENG, Y. – ZHANG, H. – SHI, J. – HU, D. – LUO, W. – JIANG, Z. – XUE, C. 2022. Weakly supervised histopathological image representation learning based on contrastive dynamic clustering. In Proceedings of SPIE 12039, Medical Imaging 2022: Digital and Computational Pathology, 1203905. San Diego, California, U.S. : SPIE, pp. 14–19. DOI: https://doi.org/10.1117/12.2611418
- LONGKUMER, B. – NEOG, P. – DEVI, H. S. 2023. Effect of sowing dates and cultivars on incidence of the exotic army worm Spodoptera frugiperda (J.E. Smith) of maize (Zea mays L.). In ENTOMON, vol. 48, no. 3, pp. 427–432. DOI: https://doi.org/10.33307/entomon.v48i3.944
- MADRID-GUIJARRO, A. – GARCÍA-PÉREZ-DE-LEMA, D. – VAN AUKEN, H. 2013. An investigation of Spanish SME innovation during different economic conditions. In Journal of Smart Business Management, vol. 51, no. 4, pp. 578–601. DOI: https://doi.org/10.1111/jsbm.12004
- OVERTON, K. – MAINO, J. L. – DAY, R. – UMINA, P. A. – BETT, B. – CARNOVALE, D. – EKESI, S. – MEAGHER, R. – REYNOLDS, O. L. 2021. Global crop impacts, yield losses and action thresholds for fall armyworm (Spodoptera frugiperda): A review. In Crop Protection, vol. 145, article no. 105641. DOI: https://doi.org/10.1016/j.cropro.2021.105641
- POURDARBANI, R. – SABZI, S. – ZOHRABI, R. – GARCÍA MATEOS, G. – FERNANDEZ-BELTRAN, R. – MOLINA-MARTINEZ, J. M. – ROHBAN, M. H. 2023. Comparison of 2D and 3D convolutional neural networks in hyperspectral image analysis of fruits applied to orange bruise detection. In Journal of Food Science, vol. 88, no. 12, pp. 5149–5163. DOI: https://doi.org/10.1111/1750-3841.16801
- PRASANNA, B. M. – BRUCE, A. B. – BAYENE, Y. – MAKUMBI, D. – GOWDA, M. – ASIM, M. – MARTINELLI, S. – HEAD, G. P. – PARIMI, S. 2022. Host plant resistance for fall armyworm management in maize: relevance, status and prospects in Africa and Asia. In Theoretical and Applied Genetics, vol. 135, pp. 3897–3916. DOI: 10.1007/s00122-022-04073-4
- PRASATH, B. – AKILA, M. – MOHAN, M. 2023. A comprehensive survey on IoT-aided pest detection and classification in agriculture using different image processing techniques. In International Journal of Image and Graphics, vol. 18, article no. 2550040. DOI: https://doi.org/10.1142/S0219467825500408
- PRATT, L. A. – BRODY, D. J. – GU, Q. 2017. Antidepressant use among persons aged 12 and over: United States, 2011–2014. In NCHS Data Brief, vol. 283, pp. 1–8. PMID: 29155679.
- SABZI, S. – POURDARBANI, R. – KALANTARI, D. – PANAGOPOULOS, T. 2020. Designing a fruit identification algorithm in orchard conditions to develop robots using video processing and majority voting based on hybrid artificial neural network. In Applied Sciences, vol.10, no. 1, article no. 383. DOI: https://doi.org/10.3390/app10010383
- SENA Jr., D. G. – PINTO, F. A. C. – QUEIROZ, D. M. – VIANA, P. A. 2003. Fall armyworm damaged maize plant identification using digital images. In Biosystems Engineering, vol. 85, no. 4, pp. 449–454. DOI. https://doi.org/10.1016/S1537-5110(03)00098-9
- SIGNORETTI, A. G. C. – PEÑAFLOR, M. F. G. V. – BENTO, J. M. S. 2012. Fall armyworm, Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae), female moths respond to herbivore-induced corn volatiles. In Neotropical Entomology, vol. 41, pp. 22–26. DOI: https://doi.org/10.1007/s13744-011-0003-y
- STUHL, C. J. – MEAGHER, R. L. – NAGOSHI, R. N. 2008. Genetic variation in neonate behavior of fall armyworm (Lepidoptera: Noctuidae). In Florida Entomologist, vol. 91, no. 2, pp. 151–158. DOI: https://doi.org/10.1653/0015-4040(2008)91[151:GVINBO]2.0.CO;2
- SUN, X.-X. – HU, C.-X. – JIA, H.-R. – WU, Q.-L. – SHEN, X.-J. – ZHAO, S.-Y. – JIANG, Y.-Y. – WU, K.-M. 2021. Case study on the first immigration of fall armyworm, Spodoptera frugiperda invading into China. In Journal of Integrative Agriculture, vol. 20, no. 3, pp. 664–672. DOI: https://doi.org/10.1016/S2095-3119(19)62839-X
- TAN, M. – QUOC, V. L. 2019. EfficientNet: Rethinking model scaling for convolutional neural networks. In Proceedings of the 36th International Conference on Machine Learning. Long Beach, California, US, vol. 97, pp. 6105–6114.
- TANG, P. – WANG, H. – KWONG, S. 2017. G-MS2F: GoogLeNet based multi-stage feature fusion of deep CNN for scene recognition. In Neurocomputing, vol. 225, pp. 188–197. DOI: https://doi.org/10.1016/j.neucom.2016.11.023
- VAKILIAN, K. A. – MASSAH, J. 2013. Performance evaluation of a machine vision system for insect pests identification of field crops using artificial neural networks. In Archives of Phytopathology and Plant Protection, vol. 46, no. 11, pp. 1262–1269. DOI: https://doi.org/10.1080/03235408.2013.763620
- WANG, K. – CHEN, K. – DU, H. – LIU, S. – XU, J. – ZHAO, J. – CHEN, H. – LIU, Y. – LIU, Y. 2022. New image dataset and new negative sample judgment method for crop pest recognition based on deep learning models. In Ecological Informatics, vol. 69, article no. 101620. DOI: https://doi.org/10.1016/j.ecoinf.2022.101620
- WU, P. – WU, F. – FAN, J. – ZHANG, R. 2021. Potential economic impact of invasive fall armyworm on mainly affected crops in China. In Journal of Pest Science, vol. 94, pp. 1065–1073. DOI: https://doi.org/10.1007/s10340-021-01336-9
- ZHAO, N. – ZHOU, L. – HUANG, T. – TAHA, M. F. – HE, Y. – QIU, Z. 2022. Development of an automatic pest monitoring system using a deep learning model of DPeNet. In Measurement, vol. 203, article no. 111970. DOI: https://doi.org/10.1016/j.measurement.2022.111970