Abstract
Forest pests are an important aspect of forest pest prevention and control work. However, it is difficult for forest pest researchers to gain a comprehensive understanding of the occurrence and control of pests using traditional statistical methods. It is a considerable challenge to help researchers to find useful information from pest occurrence and control data. Combining features of forest pest occurrence, such as timing, geography, hierarchy, disaster grade and pest species, we propose a multi-view collaborative hybrid visual analysis method to analyze the occurrence and control of forest pests from multiple angles. On this basis, we design and realize a multi-view collaborative hybrid visual analysis system for the occurrence and control of forest pests. Via case studies on the test dataset using the developed system, we complete an omni-directional analysis of the overall situation of forest pests, the overall situation of a certain pest species, the overall situation of pests in a certain region, and the occurrence of a certain pest in a certain region. The experimental results show that the visualization technologies and interactive technologies used in the paper can effectively assist researchers in the analysis of related data, and it is also demonstrated that the system is user-friendly and that the applied visualization methods are effective.
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Chen Q, Xu Z, Zhang L, Lu P, Zhang Y (2018) Geostatistical analysis of the spatial distribution of arhopalus rusticus larvae and adults. Acta Ecol Sin 38(3):975–983. https://doi.org/10.5846/stxb201611182350
Cho I, Wesslen R, Volkova S, Ribarsky W, Dou W (2017) Crystalball: a visual analytic system for future event discovery and analysis from social media data. In: VAST 2017: IEEE conference on visual analytics science and technology
Ferreira N, Lins L, Fink D, Kelling S, Wood C, Freire J, Silva C (2011) Birdvis: visualizing and understanding bird populations. IEEE Trans Vis Comput Graph 17(12):2374–2383. https://doi.org/10.1109/TVCG.2011.176
Han T (2015) The classification and statistics of crop pests and diseases. China Plant Prot 35(12):70–73. https://doi.org/10.3969/j.issn.1672-6820.2015.12.020
He G (2011) The application of microsoft excel in crop pest investigation and mathematical statistics forecasting. Mod Agric Sci Technol 0(09):27+29
He K, Lu B (2018) Investigation and analysis of diseases and insect pests in fine variety base of pinus massoniana lamb in Guizhou. Agric Technol 38(1):91–93. https://doi.org/10.11974/nyyjs.20180132038
Heimerl F, Han Q, Koch S, Ertl T (2016) Citerivers: visual analytics of citation patterns. IEEE Trans Vis Comput Graph 22(1):190–199. https://doi.org/10.1109/TVCG.2015.2467621
Hu R (2015) Research on mixed information visualization—take the case of real-time network attack visual design. Inf Stud Theory Appl 38(11):103–107. https://doi.org/10.16353/j.cnki.1000-7490.2015.11.020
Hu Q, Hu T, Wang Y, Wang S, Cao K (2016) Survey on the occurrence and distribution of apple diseases in China. Plant Prot 42(01):175–179. https://doi.org/10.3969/j.issn.0529-1542.2016.01.032
Jin Y (2012) Discussion on the investigation statistical methods of forest pest. Jilin Agric 0(11):195
Li X (2014) The application of functions in excel software in the statistics of soybean pests and diseases. Sci Technol Econ Mark 0(12):164–165
Lin H (1990) To discuss the statistical problem of the occurrence and control area of forest pests and diseases. For Pest Dis 0(03):48–49
Lin Y, Liang C (2005) Statistics and management of harmful biological data in forestry. For Sci Technol 0(10):27–30. https://doi.org/10.3969/j.issn.1671-4938.2005.10.016
Liu F (2013) Information visualization technology and application research. Ph.D. thesis, Zhejiang University
Liu L, Tan F (2013) Study on the relationship between plant diseases and insect pests of rice and meteorological conditions based on R statistical methods. J Heilongjiang Bayi Agric Univ 25(01):61–65. https://doi.org/10.3969/j.issn.1002-2090.2013.01.014
Liu W, Liu Z, Huang C, Lu M, Liu J, Yang Q (2016a) Statistics and analysis of crop yield losses caused by main diseases and insect pests in recent 10 years. Plant Prot 42(05):1–9. https://doi.org/10.3969/j.issn.0529-1542.2016.05.001
Liu X, Yang Z, Wang G, Li G (2016b) Study on the main disease distribution of Alfalfa in Heilongjiang province. Chin J Grassl 38(05):115–120. https://doi.org/10.16742/j.zgcdxb.2016-05-19
Luo L, Wei X (2017) The application of WPS table in the statistics of cotton pests and diseases. Xinjiang Agric Sci Technol 0(06):20–21. https://doi.org/10.3969/j.issn.1007-3574.2017.06.009
Meng X (1991) To discuss the statistical criteria of the occurrence and control area of forest pests and diseases. For Xinjiang 0(06):26–27
Niederer C, Stitz H, Hourieh R, Grassinger F, Aigner W, Streit M (2018) TACO: visualizing changes in tables over time. IEEE Trans Vis Comput Graph 24(1):677–686. https://doi.org/10.1109/TVCG.2017.2745298
Pan B, Liu M (2007) Discussion on statistical method of forest pest in Hunan province. Hunan For Sci Technol 34(05):67–68. https://doi.org/10.3969/j.issn.1003-5710.2007.05.025
Peng Q, Dong Z, Geng Y, Ma J (2003) Drawing forest pest distribution maps with computer. For Pest Dis 22(02):30–31. https://doi.org/10.3969/j.issn.1671-0886.2003.02.013
Pezzotti N, Hllt T, Gemert JV, Lelieveldt BPF, Eisemann E, Vilanova A (2018) Deepeyes: progressive visual analytics for designing deep neural networks. IEEE Trans Vis Comput Graph 24(1):98–108. https://doi.org/10.1109/TVCG.2017.2744358
Strobelt H, Gehrmann S, Pfister H, Rush AM (2018) Lstmvis: a tool for visual analysis of hidden state dynamics in recurrent neural networks. IEEE Trans Vis Comput Graph 24(1):667–676. https://doi.org/10.1109/TVCG.2017.2744158
Tang H, Zhou Z, Bao X, Luo L (2006) 3D medical image hybrid visualization system based on graphics processing unit. J Data Acquis Process 21(4):428–433. https://doi.org/10.3969/j.issn.1004-9037.2006.04.011
Tian X, Li P, Zhang K (2018) A brief analysis on the statistics and management of harmful biological data in forestry. Money China (Acad Edn) 04(0):136. https://doi.org/10.16266/j.cnki.cn11-4098/f.2018.04.101
Wang R (2012) Statistical characteristics and predicting analysis of the main forest pests and diseases in Fuding of Fujian province. J Fujian Coll For 32(03):268–273. https://doi.org/10.3969/j.issn.1001-389X.2012.03.014
Wu Y, Lan J, Shu X, Ji C, Zhao K, Wang J, Zhang H (2018) ITTVIS: interactive visualization of table tennis data. IEEE Trans Vis Comput Graph 24(1):709–718. https://doi.org/10.1109/TVCG.2017.2744218
Xia J, Ye F, Chen W, Wang Y, Chen W, Ma Y, Tung AKH (2018) LDSScanner: exploratory analysis of low-dimensional structures in high-dimensional datasets. IEEE Trans Vis Comput Graph 24(1):236–245. https://doi.org/10.1109/TVCG.2017.2744098
Xu X, Sun H, Li M (2018) Study on the distribution and prevention of forest pest in Hongshan district of Chifeng city based on GIS. Inn Mong For Investig Des 41(01):57–58+60. https://doi.org/10.13387/j.cnki.nmld.2018.01.021
Yang J, Wang Y, Wang X, Zhao H, Cao K (2013) Statistical analysis of apple pests occurrence and pesticide application from 2011 to 2012 in China. North Hortic 0(12):124–127
Zhang H, Guo C (2012) Research on application trend and classification of data visualization technology. Softw Guide 11(5):169–172
Zhang G, Yi H, Sun X (2009) Application of statistical analysis on plant disease research. China Plant Prot 29(02):11–15. https://doi.org/10.3969/j.issn.1672-6820.2009.02.003
Zhang PX, Zeng JP, Zeng C, Peng LH (2014a) Status quo, losses and trend of forest diseases from 2000 to 2010 in China. Biol Disaster Sci 0(02):101–108. https://doi.org/10.3969/j.issn.2095-3704.2014.02.001
Zhang W, Dai X, Xu J, Wang F (2014b) Temporal pattern analysis of citrus diseases and pests in Jiangxi province based on circular statistics. J Gannan Norm Univ 0(06):52–55
Acknowledgements
The authors would like to thank the forest protection experts from Shanxi Provincial Bureau of Forestry Pest Control and Quarantine for providing valuable feedback and suggestions for this project. This work is supported by the Fundamental Research Funds for the Central Universities (No. 2015ZCQ-XX) and the National Key Research and Development Program (2017YFD0600105).
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Yang, B., Cao, W. & Tian, C. Visual analysis of occurrence and control of forest pests with multi-view collaboration. J Vis 22, 177–195 (2019). https://doi.org/10.1007/s12650-018-0515-1
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DOI: https://doi.org/10.1007/s12650-018-0515-1