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Research Article

Transfer Learning-Based Framework for Classification of Pest in Tomato Plants

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References

  • An assignment on Pests of tomato. 2012. Published on Mar 11, 2012, by Dinesh Dalvaniya. https://www.slideshare.net/DineshDalvaniya/pests-of-tomato1.
  • Atherton, J., and J. Rudich, Eds.. 2012. The tomato crop: A scientific basis for improvement. Springer Science & Business Media, New York, USA.
  • Boissard, P., V. Martin, and S. Moisan. 2008. A cognitive vision approach to early pest detection in greenhouse crops. Computers and Electronics in Agriculture 62 (2):81–93. doi:10.1016/j.compag.2007.11.009.
  • Brahimi, M., K. Boukhalfa, and A. Moussaoui. 2017. Deep learning for tomato diseases: Classification and symptoms visualization. Applied Artificial Intelligence 31 (4):299–315. doi:10.1080/08839514.2017.1315516.
  • Cho, J., J. Choi, M. Qiao, C. W. Ji, H. Y. Kim, K. B. Uhm, and T. S. Chon. 2007. Automatic identification of whiteflies, aphids and thrips in greenhouse based on image analysis. J. Math. Comput. Simul. 1:46–53.
  • Chollet, F. 2017. Xception: Deep learning with depthwise separable convolutions. In Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, Hawaii, (pp. 1251–58).
  • Chrysodeixis Chalcites. (golden twin-spot moths). https://www.cabi.org/isc/datasheet/13243.
  • Desneux, N., E. Wajnberg, K. A. Wyckhuys, G. Burgio, S. Arpaia, C. A. Narváez-Vasquez, and J. Pizzol. 2010. Biological invasion of European tomato crops by Tuta absoluta: Ecology, geographic expansion, and prospects for biological control. Journal of Pest Science 83 (3):197–215. doi:10.1007/s10340-010-0321-6.
  • Ehler, L. E. 2006. Integrated pest management (IPM): Definition, historical development and implementation, and the other IPM. Pest Management Science 62 (9):787–89. doi:10.1002/ps.1247.
  • Faithpraise, F., P. Birch, R. Young, J. Obu, B. Faithpraise, and C. Chatwin. 2013. Automatic plant pest detection and recognition using k-means clustering algorithm and correspondence filters. International Journal of Advanced Biotechnology and Research 4 (2):189–99.
  • Flickr. 2018. The online photo management and sharing application in the world. Photos of pests available online. Accessed December 10, 2018. https://www.flickr.com/search/?text=helicoverpa%20armigera.
  • Food and Agriculture Organization of the United Nations. 2017. Plant pests and diseases. http://www.fao.org/emergencies/emergency-types/plant-pests-and-diseases/en/.
  • Fuentes, A., S. Yoon, S. Kim, and D. Park. 2017. A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition. Sensors 17 (9):2022. doi:10.3390/s17092022.
  • Gautam, A., and V. Singh. 2020. CNN-VSR: A deep learning architecture with validation-based stopping rule for time series classification. Applied Artificial Intelligence 34 (2):101–24. doi:10.1080/08839514.2020.1713454.
  • 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.
  • He, K., X. Zhang, S. Ren, and J. Sun. 2016, October. Identity mappings in deep residual networks. In European conference on computer vision (pp. 630–45). Springer, Cham.
  • He, Q., B. Ma, D. Qu, Q. Zhang, X. Hou, and J. Zhao. 2013. Cotton pests and diseases detection based on image processing. TELKOMNIKA Indonesian Journal of Electrical Engineering 11 (6):3445–50. doi:10.11591/telkomnika.v11i6.2721.
  • Howard, A. G., M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, and H. Adam. 2017. Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv Preprint arXiv:1704.04861.
  • Huang, G., Z. Liu, L. Van Der Maaten, and K. Q. Weinberger. 2017. Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition, Honolulu, Hawaii, (pp. 4700–08).
  • Insect Images. 2018. The entomology society of America and USDA Identification Technology Program, Last updated in 2018. Photos of pests available online. Accessed December 22 2018. https://www.insectimages.org/search/action.cfm?q=spodoptera+litura.
  • IPM Images. 2018. The center for invasive species and ecosystem health, last updated in 2018. Photos of pests available online. Accessed December 18, 2018. https://www.ipmimages.org/browse/Areathumb.cfm?area=63.
  • Krizhevsky, A., I. Sutskever, and G. E. Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in Neural Information Processing Systems, 25 (pp. 1097–1105).
  • Manoja, M., and J. Rajalakshmi. 2014. Early detection of pest on leaves using support vector machine. International Journal of Electrical and Electronics Research 2 (4):187–94.
  • Meena, S. C., K. K. Sharma, A. Mohanasundaram, and M. Md. 2012. Icerya aegyptiaca Douglas: A new pest of Flemingia semialata and as an alternate host of Aprostocetus purpureus (Cameron) in the lac ecosystem. Indian Journal of Entomology 74 (4):404–05.
  • Mutwiwa, U. N., and H. J. Tantau. 2005. Suitability of a UV lamp for trapping the greenhouse whitefly Trialeurodes vaporariorum Westwood (Hom: Aleyrodidae). Agricultural Engineering International: CIGR Journal, VII: 1-11.
  • The National Bureau of Agricultural Insect Resources (NBAIR). 2013. Insects in Indian agro ecosystem. Photos of pests available online. Accessed December 25 2018. http://www.nbair.res.in/insectpests/Bactrocera-latifrons.php.
  • Nieuwenhuizen, A. T., J. Hemming, and H. Suh. 2018. Detection and classification of insects on stick-traps in a tomato crop using Faster R-CNN. Proceedings of the Netherlands Conference on Computer Vision NCCV18, Netherlands (pp. 1-4).
  • Oerke, E. C. 2006. Crop losses to pests. The Journal of Agricultural Science 144 (1):31–43. doi:10.1017/S0021859605005708.
  • Pérez-Hedo, M., Á. M. Arias-Sanguine, and A. Urbaneja. 2018. Induced tomato plant resistance against Tetranychus urticae triggered by the phytophagy of Nesidiocoris tenuis. Frontiers in Plant Science 9: doi: 10.3389/fpls.2018.01419.
  • Pinto-Zevallos, D. M., and I. Vänninen. 2013. Yellow sticky traps for decision-making in whitefly management: What has been achieved? Crop Protection 47:74–84. doi:10.1016/j.cropro.2013.01.009.
  • Prathibha, G. P., T. G. Goutham, M. V. Tejaswini, P. R. Rajas, and K. Balasubramani. 2014. Early pest detection in tomato plantation using image processing. International Journal of Computer Applications 96 (12): 22.
  • Qing, Y., D. X. Xian, Q. J. Liu, B. J. Yang, G. Q. Diao, and T. A. N. G. Jian. 2014. Automated counting of rice planthoppers in paddy fields based on image processing. Journal of Integrative Agriculture 13 (8):1736–45. doi:10.1016/S2095-3119(14)60799-1.
  • Rajagopal, D., and T. P. Trivedi. 1989. Status, bio ecology, and management of Epilachna beetle, Epilachna vigintioctopunctata (Fab.)(Coleoptera: Coccinellidae) on potato in India: A review. International Journal of Pest Management 35 (4):410–13.
  • Rupanagudi, S. R., B. S. Ranjani, P. Nagaraj, V. G. Bhat, and G. Thippeswamy. 2015, January. A novel cloud computing-based smart farming system for early detection of borer insects in tomatoes. In 2015 International Conference on Communication, Information & Computing Technology (ICCICT), Nagpur, India, (pp. 1–6). IEEE.
  • Samanta, R. K., and I. Ghosh. 2012. Tea insect pests classification based on artificial neural networks. International Journal of Computer Engineering Science (IJCES) 2 (6):1–13.
  • Sandler, M., A. Howard, M. Zhu, A. Zhmoginov, and L. C. Chen. 2018. Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, (pp. 4510–20).
  • Sena Jr, D. G., Jr, F. A. C. Pinto, D. M. Queiroz, and P. A. Viana. 2003. Fall armyworm damaged maize plant identification using digital images. Biosystems Engineering 85 (4):449–54. doi:10.1016/S1537-5110(03)00098-9.
  • Shijie, J., J. Peiyi, and H. Siping. 2017 October. Automatic detection of tomato diseases and pests based on leaf images. In 2017 Chinese automation congress (CAC), Jinan, China, 2537–2510. IEEE.
  • Shimizu, Y., T. Kohama, T. Uesato, T. Matsuyama, and M. Yamagishi. 2007. Invasion of solanum fruit fly Bactrocera latifrons (Diptera: Tephritidae) to Yonaguni Island, Okinawa Prefecture, Japan. Applied Entomology and Zoology 42 (2):269–75. doi:10.1303/aez.2007.269.
  • Simonyan, K., and A. Zisserman. 2014. Very deep convolutional networks for large-scale image recognition. arXiv Preprint arXiv:1409.1556.
  • Skalski, P. 2018. Preventing deep neural network from overfitting. Mysteries of neural networks part II. Towards Data Science, Sep. 7.
  • Souza, T. L., E. S. Mapa, K. Dos Santos, and D. Menotti. 2011, September. Application of complex networks for automatic classification of damaging agents in soybean leaflets. In 2011 18th IEEE International Conference on Image Processing, Brussels, Belgium, (pp. 1065–68). IEEE.
  • Szegedy, C., S. Ioffe, V. Vanhoucke, and A. A. Alemi. 2017, February. Inception-v4, inception-resnet and the impact of residual connections on learning. In The Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, California, USA.
  • Szegedy, C., V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna. 2016. Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, Las Vegas, NV, USA, (pp. 2818–26).
  • Talo, M., O. Yildirim, U. B. Baloglu, G. Aydin, and U. R. Acharya. 2019. Convolutional neural networks for multi-class brain disease detection using MRI images. Computerized Medical Imaging and Graphics 78:101673. doi:10.1016/j.compmedimag.2019.101673.
  • The Tamil Nadu Agricultural University (TNAU). 2019. Established in 1971.Pests of Tomato. Accessed January 5, 2019. http://agritech.tnau.ac.in/crop_protection/crop_prot_crop_insect-veg_tomato.html
  • Watson, A. T., M. A. O’Neill, and I. J. Kitching. 2004. Automated identification of live moths (Macrolepidoptera) using a digital automated identification System (DAISY). Systematics and Biodiversity 1 (3):287–300. doi:10.1017/S1477200003001208.
  • Zoph, B., V. Vasudevan, J. Shlens, and Q. V. Le. 2018. Learning transferable architectures for scalable image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition, Salt Lake City, Utah, (pp. 8697–710).

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