Abstract
In recent years, the identification of pests and diseases has become a hot topic. More and more researchers are dedicated to the detection and identification of pests and diseases to achieve precision agriculture. Automatic detection of the number of pests on crops in the area has become an important means to optimize agricultural resources. With the development of modern digital technology, image processing technology has also developed rapidly, opening a new way for the identification of harmful organisms. During the agricultural planting process, timely and accurately analyze crop pests and diseases in order to make quick and accurate responses, spray pesticides accurately on the affected area, ensure the efficient use of pesticides, and achieve high yields. This article will introduce the research progress of pest identification in the second part, including disease and pest identification, pest number and position detection, existing dataset. In the third part, this article will introduce some of the methods used in previous articles. Summarize in the fourth part.
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References
Al Bashish, D., Braik, M., Bani-Ahmad, S.: A framework for detection and classification of plant leaf and stem diseases. In: 2010 International Conference on Signal and Image Processing, pp. 113–118. IEEE (2010)
Rothe, P., Kshirsagar, D.R.: Svm-based classifier system for recognition of cotton leaf diseases. Int. J. Emerg. Technol. Comput. Appl. Sci. 7(4), 427–432 (2014)
Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., Stefanovic, D.: Deep neural networks based recognition of plant diseases by leaf image classification. Comput. Intell. Neurosci. 2016 (2016)
Wang, G., Sun, Y., Wang, J.: Automatic image-based plant disease severity estimation using deep learning. Comput. Intell. Neurosci. 2017 (2017)
Ramcharan, A., Baranowski, K., McCloskey, P., Ahmed, B., Legg, J., Hughes, D.P.: Deep learning for image-based cassava disease detection. Front. Plant Sci. 8, 1852 (2017)
Deng, L., Wang, Y., Han, Z., Yu, R.: Research on insect pest image detection and recognition based on bio-inspired methods. Biosyst. Eng. 169, 139–148 (2018)
Alfarisy, A.A., Chen, Q., Guo, M.: Deep learning based classification for paddy pests & diseases recognition. In: Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence, pp. 21–25 (2018)
Dawei, W., Limiao, D., Jiangong, N., Jiyue, G., Hongfei, Z., Zhongzhi, H.: Recognition pest by image-based transfer learning. J. Sci. Food Agric. 99(10), 4524–4531 (2019)
Liu, L., Wang, R., Xie, C., Yang, P., Wang, F., Sudirman, S., Liu, W.: Pestnet: an end-to-end deep learning approach for large-scale multi-class pest detection and classification. IEEE Access 7, 45301–45312 (2019)
Mishra, M., Singh, P.K., Brahmachari, A., Debnath, N.C., Choudhury, P.: A robust pest identification system using morphological analysis in neural networks. Periodicals Eng. Nat. Sci. 7(1), 483–495 (2019)
Liu, Z., Gao, J., Yang, G., Zhang, H., He, Y.: Localization and classification of paddy field pests using a saliency map and deep convolutional neural network. Sci. Rep. 6, 20410 (2016)
Ding, W., Taylor, G.: Automatic moth detection from trap images for pest management. Comput. Electron. Agric. 123, 17–28 (2016)
Zhong, Y., Gao, J., Lei, Q., Zhou, Y.: A vision-based counting and recognition system for flying insects in intelligent agriculture. Sensors 18(5), 1489 (2018)
Li, W., Chen, P., Wang, B., Xie, C.: Automatic localization and count of agricultural crop pests based on an improved deep learning pipeline. Sci. Rep. 9(1), 1–11 (2019)
Wu, X., Zhan, C., Lai, Y.K., Cheng, M.M., Yang, J.: Ip102: a large-scale bench- mark dataset for insect pest recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 8787–8796 (2019)
Chen, L., Yuan, Y.: An image dataset for field crop disease identification. China Scientific Data (2019)
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint arXiv:1409.1556
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
Szegedy, C., et al.: Going deeper with convolutions. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–9 (2015)
Zhang, X., Zhou, X., Lin, M., Sun, J.: Shufflenet: an extremely efficient convolutional neural network for mobile devices. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6848–6856 (2018)
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., Chen, L.C.: Mobilenetv2: inverted residuals and linear bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4510–4520 (2018)
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Huo, M., Tan, J. (2020). Overview: Research Progress on Pest and Disease Identification. In: Lu, Y., Vincent, N., Yuen, P.C., Zheng, WS., Cheriet, F., Suen, C.Y. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2020. Lecture Notes in Computer Science(), vol 12068. Springer, Cham. https://doi.org/10.1007/978-3-030-59830-3_35
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DOI: https://doi.org/10.1007/978-3-030-59830-3_35
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