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Erschienen in: Cluster Computing 4/2019

16.03.2018

The recognition of rice images by UAV based on capsule network

verfasst von: Yu Li, Meiyu Qian, Pengfeng Liu, Qian Cai, Xiaoying Li, Junwen Guo, Huan Yan, Fengyuan Yu, Kun Yuan, Juan Yu, Luman Qin, Hongxin Liu, Wan Wu, Peiyun Xiao, Ziwei Zhou

Erschienen in: Cluster Computing | Sonderheft 4/2019

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Abstract

It is important to recognize the rice image captured by unmanned aerial vehicle (UAV) for monitoring the growth of rice and preventing the diseases and pests. Aiming at the image recognition, we use rice images captured by UAV as our data source, the structure of capsule network (CapsNet) is built to recognize rice images in this paper. The images are preprocessed through histogram equalization method into grayscale images and through superpixel algorithm into the superpixel segmentation results. The both results are output into the CapsNet. The function of CapsNet is to perform the reverse analysis of rice images. The CapsNet consists of five layers: an input layer, a convolution layer, a primary capsules layer, a digital capsules layer and an output layer. The CapsNet trains classification and predicts the output vector based on routing-by-agreement protocol. Therefore, the features of rice image by UAV can be precisely and efficiently extracted. The method is more convenient than the traditional artificial recognition. It provides the scientific support and reference for decision-making process of precision agriculture.

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Literatur
1.
Zurück zum Zitat Pádua, L., Adão, T., Hruška, J., et al.: Very high resolution aerial data to support multi-temporal precision agriculture information management. Proc. Comput. Sci. 121, 407–414 (2017)CrossRef Pádua, L., Adão, T., Hruška, J., et al.: Very high resolution aerial data to support multi-temporal precision agriculture information management. Proc. Comput. Sci. 121, 407–414 (2017)CrossRef
2.
Zurück zum Zitat Schmidt, D.F., Botwinick, J.: UAV-based imaging for multi-temporal, very high resolution crop surface models to monitor crop growth variability. Photogrammetrie-Fernerkundung-Geoinformation 6(6), 551–562 (2013) Schmidt, D.F., Botwinick, J.: UAV-based imaging for multi-temporal, very high resolution crop surface models to monitor crop growth variability. Photogrammetrie-Fernerkundung-Geoinformation 6(6), 551–562 (2013)
3.
Zurück zum Zitat Bendig, J., Yu, K., Aasen, H., et al.: Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. Int. J. Appl. Earth Obs. Geoinf. 39, 79–87 (2015)CrossRef Bendig, J., Yu, K., Aasen, H., et al.: Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. Int. J. Appl. Earth Obs. Geoinf. 39, 79–87 (2015)CrossRef
4.
Zurück zum Zitat Shen, K., Li, W., Pei, Z., et al.: Crop area estimation from UAV transect and MSR image data using spatial sampling method. Proc. Environ. Sci. 26, 95–100 (2015)CrossRef Shen, K., Li, W., Pei, Z., et al.: Crop area estimation from UAV transect and MSR image data using spatial sampling method. Proc. Environ. Sci. 26, 95–100 (2015)CrossRef
5.
Zurück zum Zitat Chang, A., Jung, J., Maeda, M.M., et al.: Crop height monitoring with digital imagery from unmanned aerial system (UAS). Comput. Electron. Agric. 141, 232–237 (2017)CrossRef Chang, A., Jung, J., Maeda, M.M., et al.: Crop height monitoring with digital imagery from unmanned aerial system (UAS). Comput. Electron. Agric. 141, 232–237 (2017)CrossRef
6.
Zurück zum Zitat Bendig, J., Yu, K., Aasen, H., et al.: Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. Int. J. Appl. Earth Obs. Geoinf. 39, 79–87 (2015)CrossRef Bendig, J., Yu, K., Aasen, H., et al.: Combining UAV-based plant height from crop surface models, visible, and near infrared vegetation indices for biomass monitoring in barley. Int. J. Appl. Earth Obs. Geoinf. 39, 79–87 (2015)CrossRef
7.
Zurück zum Zitat Xiang, H., Tian, L.: Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV). Biosys. Eng. 108(2), 174–190 (2011)MathSciNetCrossRef Xiang, H., Tian, L.: Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV). Biosys. Eng. 108(2), 174–190 (2011)MathSciNetCrossRef
8.
Zurück zum Zitat Gibson-Poole, S., Humphris, S., Toth, I.: Identification of the onset of disease within a potato crop using a UAV equipped with unmodified and modified commercial off-the-shelf digital cameras. Adv. Anim. Biosci. 8, 2812–2816 (2017)CrossRef Gibson-Poole, S., Humphris, S., Toth, I.: Identification of the onset of disease within a potato crop using a UAV equipped with unmodified and modified commercial off-the-shelf digital cameras. Adv. Anim. Biosci. 8, 2812–2816 (2017)CrossRef
9.
Zurück zum Zitat Barbedo, J.G.A.: A review on the main challenges in automatic plant disease identification based on visible range images. Biosys. Eng. 144, 52–60 (2016)CrossRef Barbedo, J.G.A.: A review on the main challenges in automatic plant disease identification based on visible range images. Biosys. Eng. 144, 52–60 (2016)CrossRef
10.
Zurück zum Zitat Latte, M.V., Shidnal, S., Anami, B.S., et al.: A combined HSV and GLCM approach for paddy variety identification from crop images. Int. J. Signal Process. Image Process. Pattern Recognit. 8 (2015) Latte, M.V., Shidnal, S., Anami, B.S., et al.: A combined HSV and GLCM approach for paddy variety identification from crop images. Int. J. Signal Process. Image Process. Pattern Recognit. 8 (2015)
11.
Zurück zum Zitat Dorj, U.O., Lee, M., Yun, S.S.: An yield estimation in citrus orchards via fruit detection and counting using image processing. Comput. Electron. Agric. 140, 103–112 (2017)CrossRef Dorj, U.O., Lee, M., Yun, S.S.: An yield estimation in citrus orchards via fruit detection and counting using image processing. Comput. Electron. Agric. 140, 103–112 (2017)CrossRef
12.
Zurück zum Zitat Grinblat, G.L., Uzal, L.C., Larese, M.G., et al.: Deep learning for plant identification using vein morphological patterns. Comput. Electron. Agric. 127, 418–424 (2016)CrossRef Grinblat, G.L., Uzal, L.C., Larese, M.G., et al.: Deep learning for plant identification using vein morphological patterns. Comput. Electron. Agric. 127, 418–424 (2016)CrossRef
13.
Zurück zum Zitat dos Santos Ferreira, A., Freitas, D.M., da Silva, G.G.: Weed detection in soybean crops using ConvNets. Comput. Electron. Agric. 143, 314–324 (2017)CrossRef dos Santos Ferreira, A., Freitas, D.M., da Silva, G.G.: Weed detection in soybean crops using ConvNets. Comput. Electron. Agric. 143, 314–324 (2017)CrossRef
15.
Zurück zum Zitat Shin, M., Kim, M., Kwon, D.S.: Baseline CNN structure analysis for facial expression recognition. In: Robot and Human Interactive Communication (RO-MAN), 2016 25th IEEE International Symposium on, pp. 724–729 (2016) Shin, M., Kim, M., Kwon, D.S.: Baseline CNN structure analysis for facial expression recognition. In: Robot and Human Interactive Communication (RO-MAN), 2016 25th IEEE International Symposium on, pp. 724–729 (2016)
16.
Zurück zum Zitat García-Santillán, I.D., Pajares, G.: On-line crop/weed discrimination through the Mahalanobis distance from images in maize fields. Biosyst. Eng. 166, 28–43 (2018)CrossRef García-Santillán, I.D., Pajares, G.: On-line crop/weed discrimination through the Mahalanobis distance from images in maize fields. Biosyst. Eng. 166, 28–43 (2018)CrossRef
17.
Zurück zum Zitat Achanta, R., Shaji, A., Smith, K., et al.: SLIC superpixels. Epfl (2010) Achanta, R., Shaji, A., Smith, K., et al.: SLIC superpixels. Epfl (2010)
18.
Zurück zum Zitat Achanta, R., Shaji, A., Smith, K., et al.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)CrossRef Achanta, R., Shaji, A., Smith, K., et al.: SLIC superpixels compared to state-of-the-art superpixel methods. IEEE Trans. Pattern Anal. Mach. Intell. 34(11), 2274–2282 (2012)CrossRef
19.
Zurück zum Zitat Hsu, C.Y., Ding, J.J.: Efficient image segmentation algorithm using SLIC superpixels and boundary-focused region merging, pp. 1–5. In: Communications and Signal Processing. IEEE (2013) Hsu, C.Y., Ding, J.J.: Efficient image segmentation algorithm using SLIC superpixels and boundary-focused region merging, pp. 1–5. In: Communications and Signal Processing. IEEE (2013)
20.
Zurück zum Zitat Dubey, S.R., Jalal, A.S.: Detection and classification of apple fruit diseases using complete local binary patterns. In: Third International Conference on Computer and Communication Technology, pp. 346–351. IEEE Computer Society (2012) Dubey, S.R., Jalal, A.S.: Detection and classification of apple fruit diseases using complete local binary patterns. In: Third International Conference on Computer and Communication Technology, pp. 346–351. IEEE Computer Society (2012)
21.
Zurück zum Zitat Omrani, E., Khoshnevisan, B., Shamshirband, S., et al.: Potential of radial basis function-based support vector regression for apple disease detection. Measurement 55(9), 512–519 (2014)CrossRef Omrani, E., Khoshnevisan, B., Shamshirband, S., et al.: Potential of radial basis function-based support vector regression for apple disease detection. Measurement 55(9), 512–519 (2014)CrossRef
22.
Zurück zum Zitat Wen, C., Wu, D., Hu, H., et al.: Pose estimation-dependent identification method for field moth images using deep learning architecture. Biosys. Eng. 136, 117–128 (2015)CrossRef Wen, C., Wu, D., Hu, H., et al.: Pose estimation-dependent identification method for field moth images using deep learning architecture. Biosys. Eng. 136, 117–128 (2015)CrossRef
23.
Zurück zum Zitat Grinblat, G.L., Uzal, L.C., Larese, M.G., et al.: Deep learning for plant identification using vein morphological patterns. Comput. Electron. Agric. 127, 418–424 (2016)CrossRef Grinblat, G.L., Uzal, L.C., Larese, M.G., et al.: Deep learning for plant identification using vein morphological patterns. Comput. Electron. Agric. 127, 418–424 (2016)CrossRef
Metadaten
Titel
The recognition of rice images by UAV based on capsule network
verfasst von
Yu Li
Meiyu Qian
Pengfeng Liu
Qian Cai
Xiaoying Li
Junwen Guo
Huan Yan
Fengyuan Yu
Kun Yuan
Juan Yu
Luman Qin
Hongxin Liu
Wan Wu
Peiyun Xiao
Ziwei Zhou
Publikationsdatum
16.03.2018
Verlag
Springer US
Erschienen in
Cluster Computing / Ausgabe Sonderheft 4/2019
Print ISSN: 1386-7857
Elektronische ISSN: 1573-7543
DOI
https://doi.org/10.1007/s10586-018-2482-7

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