可见光波段无人机遥感图像的小麦茎蘖密度定量反演

doi:10.3964/j.issn.1000-0593(2021)12-3828-09

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Abstract Nitrogen topdressing is a vital agrotechnical measure to boost tillering process and improve the population structure of wheat stalks. However, uniform nitrogen topdressing is apt to cause excessive application and low agronomic efficiency of nitrogen fertilizer. However, variable-rate fertilization can solve the contradiction between individual development and formation of population structure of wheat stalks, by decreasing the application of nitrogen fertilizer according to the actual growing status of wheat stalks in the field. The key technology to improve the population structure of wheat stalks using variable-rate nitrogen topdressing is to accurately obtain the information of spatial variations of wheat stalk numbers at field scale. Thus, with the objectives of fertilizer reduction and yield increasing, this research studies wheat growth status in the tillering stage to invert and regulate population densities of wheat stalks at field scale. Firstly, a DJI mini 2 with a CMOS (Complementary Metal Oxide Semiconductors) image sensor is utilized to acquire visible-band imagery of wheat from the experimental field. Secondly, vegetation indices of ratio type such asVDVI (Visible-band Difference Vegetation Index), NGRDI(Normalized Green-Red Difference Index), NGBDI (Normalized Green-Blue Difference Index), and RGRI (Ratio Green-Red Index）were calculated out of the visible-band images, in order to highlight vegetation features and reduce the impact of uneven light intensity on remote sensing images. Furthermore, FVC (Fractional Vegetation Coverage), which indicates the growth vigor of both individuals of wheat tillers and stalk population as a whole, was calculated based on the VDVI map. Subsequently, a BP (Backward Propagation) Neural Network prediction model was built to quantitatively invert wheat stalk density, using FGV, VDVI, NGRDI, NGBDI, and RGRI the input layer, and ground truth samples of wheat stalk densities as output layer. Upon completion of the BP Neural Network training, weight and threshold values of the prediction model were obtained, and a validation experiment was implemented. The result of the validation experiment showed that the RMSE (Root Mean Square Error) and MAPE (Mean Absolute Percentage Error) of the BP Neural Network prediction model is 19 and 3.62%, respectively. Compared with the average value of 635 of the wheat stalk density’s ground truth values, the BP Neural Network model has extraordinary wheat stalk density prediction accuracy. The statistical data of the inversed wheat stalk density at field scale indicated that the area of wheat stalk density below 500 stalks·m^-2, between 501～800 stalks·m^-2, and above 800 stalks·m^-2 accounted for 6.67%, 74.67%, 18.66%, respectively, which provided data support for variable-rate nitrogen topdressing. The implementation of this research under the background of “negative growth of fertilizer usage” proposed by the state is the actual demand for developing resource-saving and environment-friendly Green Agriculture. The research results provide new approaches and technical support for digitalization of wheat plantation, theoretical basis and data support for creating a high and stable yield of wheat in a large area, which is of great scientific significance.

Key words：Drone; Agricultural remote sensing; Wheat vigor; Wheat tiller; Vegetation index; Fractional vegetation coverage

Received: 2021-03-14 Accepted: 2021-06-26

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http://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2021)12-3828-09 OR http://www.gpxygpfx.com/EN/Y2021/V41/I12/3828

[1] HE Zhong-hu, XIA Xian-chun, CHEN Xin-min, et al(何中虎, 夏先春, 陈新民, 等). Acta Agronomica Sinica(作物学报), 2011, 37(2): 202.
[2] LI Wei(李伟). Chinese Journal of Agricultural Resources and Regional Planning(中国农业资源与区划), 2019, 40(10): 49.
[3] YANG Cheng, LI Xiang-dong, ZHANG De-qi, et al(杨程, 李向东, 张德奇, 等). Journal of Henan Agricultural Sciences(河南农业科学), 2016, 45(10): 19.
[4] YANG Zhen-ping, ZHOU Nai-jian, MIAO Guo-yuan(杨珍平, 周乃健, 苗果园). Journal of Shanxi Agricultural University·Natural Science Edition(山西农业大学学报·自然科学版), 2003, 23(3): 92.
[5] YI Yuan, SU Sheng-nan, LI Fu-jian, et al(易媛，苏盛楠，李福建，等). Journal of Triticeae Crops(麦类作物学报), 2018, 38(12)：1465.
[6] Du Mengmeng, Noguchi Noboru. Remote Sensing, 2017, 9(3): 289.
[7] Du Mengmeng, Noguchi Noboru, Itoh Atsushi, et al. International Journal of Agricultural and Biological Engineering, 2017, 10(5): 1.
[8] Jin Xiuliang，Liu Shouyang，Frederic Baret，et al. Remote Sensing of Environment，2017, 198: 105.
[9] Sripada R, Farrer D, Weisz R, et al. Agronomy Journal，2007, 99(6): 1424.
[10] Basso B, Fiorentino C, Cammarano D, et al. Precision Agriculture, 2016, 17(2): 168.
[11] Walsh O, Klatt A, Solie J, et al. Precision Agriculture, 2013, 14: 343.
[12] Rezzouk F, Gracia R, Kefauver S, et al. Plant Science, 2019, 295: 110281.
[13] ZHANG Meng, SUN Hong, LI Min-zan, et al(张猛, 孙红, 李民赞, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2016, 47(9): 341.
[14] WU Huan-li, CUI Ke-wang, ZHANG Xin, et al(吴焕丽, 崔可旺, 张馨, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2019, 50(1): 42.
[15] HUANG Fen, GAO Shuai, YAO Xia, et al(黄芬, 高帅, 姚霞, 等). Journal of Nanjing Agricultural University(南京农业大学学报), 2020, 43(2): 364.
[16] LIU Ming-xing, LI Chang-chun, LI Zhen-hai, et al(刘明星, 李长春, 李振海, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报)，2020, 51(2): 192.
[17] Jiang J, Zheng H, Ji X, et al. Sensors, 2019, 19: 747.
[18] DU Ying, CAI Yi-cheng, TAN Chang-wei, et al(杜颖, 蔡义承, 谭昌伟, 等). Scientia Agricultura Sinica(中国农业科学), 2019, 52(1): 21.
[19] XU Kai-jian, TIAN Qing-jiu, XU Nian-xu, et al(徐凯健，田庆久，徐念旭，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(12): 3794.