Citation: Fei Tang, Dabin Zhang, Xuehua Zhao. Efficiently deep learning for monitoring Ipomoea cairica (L.) sweets in the wild[J]. Mathematical Biosciences and Engineering, 2021, 18(2): 1121-1135. doi: 10.3934/mbe.2021060
[1] | Z. Y. Sun, T. J. Zhang, J. Q. Su, W. S. Chow, J. Q. Liu, L. L. Chen, et al., A novel role of ethephon in controlling the noxious weed Ipomoea cairica (Linn.) Sweets, Sci. Rep., 5 (2015), 11372. |
[2] | G. Liu, Y. Gao, F. Huang, M. Yuan, S. Peng, The Invasion of coastal areas in south China by Ipomoea cairica may be accelerated by the ecotype being more locally adapted to salt stress, Plos One, 11 (2016), e0149262. |
[3] | S. Shen, Z. Shen, M. Zhao, Big Data Monitoring System Design and Implementation of Invasive Alien Plants Based on WSNs and WebGIS, Wireless Pers. Commun., 97 (2017), 4251–4263. doi: 10.1007/s11277-017-4723-0 |
[4] | M. Mafanya, P. Tsele, J. O. Botai, P. Manyama, G. J. Chirima, T. Monate, Radiometric calibration framework for ultra-high-resolution UAV-derived orthomosaics for large-scale mapping of invasive alien plants in semi-arid woodlands: Harrisia pomanensis as a case study, Int. J. Remote Sens., 39 (2018), 5119–5140. doi: 10.1080/01431161.2018.1490503 |
[5] | M. Liu, H. Li, L. Li, W. Man, M. Jia, Z. Wang, et al., Monitoring the Invasion of Spartina alterniflora Using Multi-source High-resolution Imagery in the Zhangjiang Estuary, China, Remote Sens., 9 (2017), 539. |
[6] | A. M. West, P. H. Evangelista, C. S. Jarnevich, N. E. Young, T. J. Stohlgren, C. Talbert, et al., Integrating remote sensing with species distribution models; mapping Tamarisk invasions using the software for assisted habitat modeling (SAHM), J. Visualized Exp., 116 (2016), e54578. |
[7] | J. Bustamante, D. Aragonés, I. Afán, C. Luque, A. Pérez-Vázquez, E. Castellanos, et al., Hyperspectral sensors as a management tool to prevent the invasion of the exotic cordgrass spartina densiflora in the doñana wetlands, Remote Sens., 8 (2016), 1001. |
[8] | J. M. Barbosa, G. P. Asner, R. E. Martin, C. A. Baldeck, F. Hughes, T. Johnson, Determining subcanopy psidium cattleianum invasion in hawaiian forests using imaging spectroscopy, Remote Sens., 8 (2016), 33. |
[9] | S. Khare, H. Latifi, S. K. Ghosh, Multi-scale assessment of invasive plant species diversity using Pléiades 1A, RapidEye and Landsat-8 data, Geocarto Int., 33 (2018), 681–698. doi: 10.1080/10106049.2017.1289562 |
[10] | L. Tao, C. Cheng, Plant Identification Based on Image Set Analysis, In: Artificial Intelligence and Mobile Services–AIMS 2018, Lecture Notes in Computer Science, 2018. |
[11] | S. Ge, R. Carruthers, P. Gong, A. Herrera, Texture analysis for mapping Tamarix parviflora using aerial photographs along the Cache Creek, California, Environ. Monit. Assess., 114 (2006), 65–83. doi: 10.1007/s10661-006-1071-z |
[12] | D. Jones, S. Pike, M. Thomas, D. Murphy, Object-based image analysis for detection of Japanese knotweed sl taxa (Polygonaceae) in Wales (UK), Remote Sens., 3 (2011), 319–342. doi: 10.3390/rs3020319 |
[13] | W. Dorigo, A. Lucieer, T. Podobnikar, A. Čarni, Mapping invasive Fallopia japonica by combined spectral, spatial, and temporal analysis of digital orthophotos, Int. J. Appl. Earth Obs. Geoinf., 19 (2012), 185–195. |
[14] | J. Sandino, F. Gonzalez, K. Mengersen, K. J. Gaston, UAVs and machine learning revolutionising invasive grass and vegetation surveys in remote arid lands, Sensors, 18 (2018), 605. |
[15] | C. Hung, Z. Xu, S. Sukkarieh, Feature learning based approach for weed classification using high resolution aerial images from a digital camera mounted on a UAV, Remote Sens., 6 (2014), 12037–12054. doi: 10.3390/rs61212037 |
[16] | M. P. Pound, J. A. Atkinson, A. J. Townsend, M. H. Wilson, M. Griffiths, A. S. Jackson, et al., Deep machine learning provides state-of-the-art performance in image-based plant phenotyping, GigaScience, 6 (2017), gix083. |
[17] | A. Fuentes, S. Yoon, S. C. Kim, D. S. Park, A robust deep-learning-based detector for real-time tomato plant diseases and pests recognition, Sensors, 17 (2017), 2022. doi: 10.3390/s17092022 |
[18] | A. Ramcharan, K. Baranowski, P. McCloskey, B. Ahmed, J. Legg, D. P. Hughes, Deep learning for image-based cassava disease detection, Front. Plant Sci., 8 (2017), 1852. doi: 10.3389/fpls.2017.01852 |
[19] | H. Lu, Z. Cao, Y. Xiao, B. Zhuang, C. Shen, TasselNet: counting maize tassels in the wild via local counts regression network, Plant Methods, 13 (2017), 79. doi: 10.1186/s13007-017-0224-0 |
[20] | M. Z. Alom, T. M. Taha, C. Yakopcic, S. Westberg, P. Sidike, M. S. Nasrin, et al., The history began from alexnet: A comprehensive survey on deep learning approaches, arXiv preprint arXiv: 1803.01164, 2018. |
[21] | Y. Lecun, L. Bottou, Y. Bengio, P. Haffner, Gradient-based learning applied to document recognition, Proc. IEEE, 86 (1998), 2278–2324. doi: 10.1109/5.726791 |
[22] | A. Krizhevsky, I. Sutskever, G. Hinton, Imagenet classification with deep convolutional neural networks, Commun. ACM, 60 (2017), 84–90. doi: 10.1145/3065386 |
[23] | C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, et al., Going Deeper with Convolutions, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, 2015. |
[24] | K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition, Comput. Sci., 2014 (2014), 21–30. |
[25] | K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, 2016. |