• Laser & Optoelectronics Progress
  • Vol. 57, Issue 14, 142801 (2020)
Meng Zhu1、2, Zhongfa Zhou1、2、*, Yi Jiang1、2, and Denghong Huang1、2
Author Affiliations
  • 1School of Karst Science/School of Geography and Environment Science, Guizhou Normal University, Guiyang, Guizhou 550001, China
  • 2State Engineering Technology Institute for Karst Desertification Control, Guiyang, Guizhou 550001, China
  • show less
    DOI: 10.3788/LOP57.142801 Cite this Article Set citation alerts
    Meng Zhu, Zhongfa Zhou, Yi Jiang, Denghong Huang. An Accurate Recognition Method of Pitaya Plants Based on Visible Light Band UAV Remote Sensing[J]. Laser & Optoelectronics Progress, 2020, 57(14): 142801 Copy Citation Text show less
    References

    [1] Pimienta-Barrios E, Nobel P S. Pitaya (Stenocereus Spp., Cactaceae): an ancient and modern fruit crop of Mexico[J]. Economic Botany, 48, 76-83(1994).

    [2] He X F, Ding W S, Zhong Y Y et al. Study on in vitro cultural technique of red pitaya[J]. Journal of Yunnan Agricultural University(Natural Science), 34, 656-662(2019).

    [3] Zhu M, Zhou Z F, Zhao X et al. Recognition and extraction method of single dragon fruit plant in plateau-canyon areas based on UAV remote sensing[J]. Tropical Geography, 39, 502-511(2019).

    [4] Wang L J, Shi J Q, Huang J J. Research on weed recognition algorithm based on overall shape characteristics of plants[J]. Agricultural Engineering Technology, 36, 15-16(2016).

    [5] Wu L L, Liu J Y, Wen Y X et al. Weed identification method based on SVM in the corn field[J]. Transactions of the Chinese Society for Agricultural Machinery, 40, 162-166(2009).

    [6] Xavier S, Coffin A, Olson D et al. Remotely estimating beneficial arthropod populations: implications of a low-cost small unmanned aerial system[J]. Remote Sensing, 10, 1485(2018).

    [7] Cao J J, Wang Y M, Mao W H et al. Weed detection method in wheat field based on texture and position features[J]. Transactions of the Chinese Society for Agricultural Machinery, 38, 107-110(2007).

    [8] Wang H Y, Li J L. Identification of field weeds in corn seedling stage based on texture features[J]. Jiangsu Agricultural Sciences, 42, 143-145(2014).

    [9] Wang H Y, Lü J X. Identifying corn weed based on texture features and optimized SVM[J]. Hubei Agricultural Sciences, 53, 3163-3166, 3169(2014).

    [10] Woebbecke D M, Meyer G E, Bargen K V et al. Color indices for weed identification under various soil, residue, and lighting conditions[J]. Transactions of the ASAE, 38, 259-269(1995).

    [11] Li P. Corn and weed seedlings detection based on multi-spectral images[D]. Xianyang: Northwest A & F University(2014).

    [12] Bai J, Xu Y, Wei X H et al. Weed identification from winter rape at seedling stage based on spectrum characteristics analysis[J]. Transactions of the CSAE, 29, 128-134(2013).

    [13] Chen S R, Zou H D, Wu R M et al. Identification for weedy rice at seeding stage based on hyper-spectral imaging technique[J]. Transactions of the Chinese Society for Agricultural Machinery, 44, 253-257, 163(2013).

    [14] Liu B, Fang J Y, Liu X et al. Research on crop-weed discrimination using a field ImagingSpectrometer[J]. Spectroscopy and Spectral Analysis, 30, 1830-1833(2010).

    [15] Zhao P, Wei X Z. Weed recognition in agricultural field using multiple feature fusions[J]. Transactions of the Chinese Society of Agricultural Machinery, 45, 275-281(2014).

    [16] Wang S F, Yang L X. Feature dimension reduction and category identification of weeds in cotton field based on GA-ANN complex algorithm[J]. Journal of Henan Agricultural Sciences, 47, 148-154, 160(2018).

    [17] He D J, Qiao Y L, Li P et al. Weed recognition based on SVM-DS multi-feature fusion[J]. Transactions of the Chinese Society for Agricultural Machinery, 44, 182-187(2013).

    [18] Deng X W, Qi L, Ma X et al. Recognition of weeds at seedling stage in paddy fields using multi-feature fusion and deep belief networks[J]. Transactions of the CSAE, 34, 165-172(2018).

    [19] Yan B Z. Identification of weeds in maize seedling stage by machine vision technology[J]. Journal of Agricultural Mechanization Research, 40, 212-216(2018).

    [20] Zhou Y, Fang J D, Zhao Y D. Weed identification method of weeding robot based on PCA-NBC classification model[J]. Machine Tool & Hydraulics, 46, 104-110, 126(2018).

    [21] Zhao N[J]. Study on identification method of weeds based on machine vision Wireless Internet Technology, 2016, 141-142.

    [22] Kim D W, Yun H E, Jeong S J et al. Modeling and testing of growth status for Chinese cabbage and white radish with UAV-based RGB imagery[J]. Remote Sensing, 10, 563(2018).

    [23] Yuan L, Yuan J S, Zhang D Z. Remote sensing image classification based on DeepLab-v3+[J]. Laser & Optoelectronics Progress, 56, 152801(2019).

    [24] Zou H D, Chen S R, Chen G et al. Identification of weedy rice using vis/NIR spectroscopy and artificial neural network[J]. Journal of Agricultural Mechanization Research, 35, 156-158, 163(2013).

    [25] Böhler J, Schaepman M, Kneubühler M. Crop classification in a heterogeneous arable landscape using uncalibrated UAV data[J]. Remote Sensing, 10, 1282(2018).

    [26] Yang J J. Study on identification of the field weed based on BP neural networks[D]. Changchun: Jilin Agricultural University(2017).

    [27] Dong L, Lei L Y, Li X Y et al[J]. Weed identification technology of greenhouse vegetable crops in greenhouse based on improved artificial neural network Northern Horticulture, 2017, 79-82.

    [28] Wang C, Wu X H, Li Z W. Recognition of maize and weed based on multi-scale hierarchical features extracted by convolutional neural network[J]. Transactions of the CSAE, 34, 144-151(2018).

    [29] Jiang H H, Wang P F, Zhang Z et al. Fast identification of field weeds based on deep convolutional network and binary hash code[J]. Transactions of the Chinese Society for Agricultural Machinery, 49, 30-38(2018).

    [30] Louhaichi M, Borman M M, Johnson D E. Spatially located platform and aerial photography for documentation of grazing impacts on wheat[J]. Geocarto International, 16, 65-70(2001).

    [31] Wang S S, Wang S, Zhang H et al. Soybean field weed recognition based on light sum-product networks and UAV remote sensing images[J]. Transactions of the CSAE, 35, 81-89(2019).

    [32] Wang X Q, Wang M M, Wang S Q et al. 31(5): 152-157, 159, 158(2015).

    [33] Chi D X, Zhang W, Wang Y. Segmentation of rice seedling image based on EXG factor[J]. Journal of Anhui Agricultural Sciences, 40, 17902-17903(2012).

    [34] Meyer G E, Neto J C. Verification of color vegetation indices for automated crop imaging applications[J]. Computers and Electronics in Agriculture, 63, 282-293(2008).

    [35] Gonzalez R C, Woods R E[M]. Digital image processing, 479-483(2010).

    [36] Engineering Science. 03): 42-45+50[J]. Zhang Y. License plate binarization algorithm based on analysis of the spatial distribution, maximum variance between clusters. Journal of Zhejiang University(2001).

    Meng Zhu, Zhongfa Zhou, Yi Jiang, Denghong Huang. An Accurate Recognition Method of Pitaya Plants Based on Visible Light Band UAV Remote Sensing[J]. Laser & Optoelectronics Progress, 2020, 57(14): 142801
    Download Citation