Study on Yield Estimation of Wheat Varieties Based on Multi-Source Data
SONG Cheng-yang1, GENG Hong-wei1, FEI Shuai-peng2, LI Lei2, GAN Tian2, ZENG Chao-wu3, XIAO Yong-gui2*, TAO Zhi-qiang2*
1. College of Agronomy, Xinjiang Agricultural University, Urumqi 830052, China
2. Institute of Crop Sciences, Chinese Academy of Agricultural Sciences, Beijing 100081, China
3. Research Institute of Grain Crops, Xinjiang Academy of Agricultural Sciences, Urumqi 830091, China
Abstract:Pre-production estimation of wheat production is related to the formulation of agricultural production plans, food security, national economy and macro-decision-making, and the application of drones can estimate wheat production in a non-destructive, fast, accurate, timely and efficient manner. The machine learning method is used to fully tap the potential of multi-source remote sensing data to estimate the grain yield of multiple wheat varieties and to clarify the effect of multi-source data fusion on improving the yield estimation accuracy of cultivars. It is significant for crop field management and ensuring a high and stable yield in wheat. In this study, field trials of winter wheat were carried out with 140 main wheat varieties in the Huanghuai wheat region as materials. The drone platform equipped with red green blue (RGB) and multispectral sensors were used to collect the canopy information of 140 winter wheat varieties during the grain filling period. Six machine learning algorithms were used, namely Ridge Regression (RR), support vector regression (SVR), Random Forest Regression (RFR), Gaussian Process (GP), k-Nearest Neighbor (k-NN) and Cubist, to build yield estimation models from single sensor data and multi-source data fusion. Coefficient of determination (R2), root mean square error (RMSE) and relative root mean square error (RRMSE) were used to evaluate the estimation model. The results showed that the selected 10 visible vegetation indices and 13 multispectral covered indices were significantly correlated with the measured yield (p<0.05), and the absolute value of the correlation coefficient from high to low was multispectral vegetation index (0.54~0.83), color index (0.45~0.61), texture feature (<0.45), all six machine learning algorithms have the highest yield estimation and prediction accuracy when using multi-source data fusion. Multi-source data fusion yield estimation accuracy (average coefficient of determination R2=0.50~0.71)>multi-spectral sensor yield estimation accuracy (R2=0.53~0.69)>RGB sensor yield estimation accuracy (R2=0.35~0.51). Compared with RGB data, the R2 of multi-source data fusion increases by 0.17~0.23, and the mean root mean square error (RMSE) decreases by 0.06~0.09 t·hm-2; compared with multi-spectral data, the R2 increases by 0.01~0.06, and the RMSE decreases by 0.01~0.03 t·hm-2. Compared with the other five algorithms, the multi-source data fusion model established by the Cubist algorithm has the highest yield estimation accuracy, with an R2 of 0.71 and an RMSE of 0.29 t·hm-2. It shows that compared with the yield estimation model of single sensor data, multi-source data fusion can effectively improve the yield estimation accuracy of winter wheat varieties, and the Cubist algorithm can better process multi-mode data to improve the yield prediction accuracy, providing theoretical guidance for predicting the yield of different wheat varieties.
宋成阳,耿洪伟,费帅鹏,李 雷,甘 甜,曾潮武,肖永贵,陶志强. 基于多源数据的小麦品种产量估测研究[J]. 光谱学与光谱分析, 2023, 43(07): 2210-2219.
SONG Cheng-yang, GENG Hong-wei, FEI Shuai-peng, LI Lei, GAN Tian, ZENG Chao-wu, XIAO Yong-gui, TAO Zhi-qiang. Study on Yield Estimation of Wheat Varieties Based on Multi-Source Data. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2210-2219.
[1] HE Zhong-hu, ZHUANG Qiao-sheng, CHENG Shun-he, et al(何中虎, 庄巧生, 程顺和, 等). Journal of Agriculture(农学学报), 2018, 8(1): 99.
[2] National Bureau of Statistics of China(中华人民共和国国家统计局). CHINA STATISTICAL YEARBOOK (中国统计年鉴),2021-12-06:http://www.gov.cn/xinwen/2021-12/06/content_5656247.htm.
[3] Wu X, Feng H, Wu D, et al. Genome Biology, 2021, 22(1): 185.
[4] Schut A G T, Traore P C S, Blaes X, et al. Field Crops Research, 2018, 221: 98.
[5] Du M, Noguchi N. IFAC-PapersOnLine, 2016, 49(16): 5.
[6] Zhang C, Kovacs J M. Precision Agriculture, 2012, 13(6): 693.
[7] GUO Tao, YAN An, GENG Hong-wei(郭 涛, 颜 安, 耿洪伟). Journal of Triticeae Crops(麦类作物学报), 2020, 40(9): 1129.
[8] WU Bing-fang, ZHANG Miao, ZENG Hong-wei, et al(吴炳方, 张 淼, 曾红伟, 等). Journal of Remote Sensing(遥感学报), 2016, 20(5): 1027.
[9] LIU Jian-gang, ZHAO Chun-jiang, YANG Gui-jun, et al(刘建刚, 赵春江, 杨贵军, 等). Transactions of the Chinese Society of Agricultural Engineering (农业工程学报), 2016, 32(24): 98.
[10] Hassan M A, Yang M, Rasheed A, et al. Plant Science, 2019, 282: 95.
[11] Hassan M, Yang M J, Rasheed A, et al. Remote Sensing, 2018, 10(6): 809.
[12] TAO Hui-lin, FENG Hai-kuan, YANG Gui-jun, et al (陶惠林, 冯海宽, 杨贵军, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2019, 35(23): 111.
[13] Zhu W X, Li S, Zhang X, et al. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34: 78.
[14] Hall D L,Llinas J. Proceedings of the IEEE, 1997, 85: 6.
[15] WANG Lai-gang, ZHENG Guo-qing, GUO Yan, et al(王来刚, 郑国清, 郭 燕, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2022, 53(1): 198.
[16] Wei Q, Bioucas Dias J, Dobigeon N, et al. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(7): 3658.
[17] Bilgin G, Ustuner M. Journal of Applied Remote Sensing, 2015, 9(1): 096054.
[18] Maimaitijiang M, Sagan V, Sidike P, et al. Remote Sensing of Environment, 2020, 237: 111599.
[19] Jhan J P, Rau J Y, Haala N. ISPRS Journal of Photogrammetry and Remote Sensing, 2018, 137: 47.
[20] Bendig J, Yu K, Aasen H, et al. International Journal of Applied Earth Observation and Geoinformation, 2015, 39: 79.
[21] Metternicht G. International Journal of Remote Sensing, 2003, 24(14): 2855.
[22] Louhaichi M, Borman M M, Johnson D E. Geocarto International, 2001, 16(1): 65.
[23] Woebbecke D M, Meyer G E, Von Bargen K, et al. Transactions of the American Society of Agricultural Engineers, 1995, 38: 259.
[24] Kataoka T, Kaneko T, Okamoto H, et al. Crop Growth Estimation System Using Machine Vision,2003, 2: 1079.
[25] Gitelson A A, Kaufman Y J, Stark R, et al. Remote Sensing of Environment, 2002, 80(1): 76.
[26] Kawashima S, Nakatani M. Annals of Botany, 1998, 81(1): 49.
[27] Woebbecke D M, Meyer G E, Von Bargen K, et al. Transactions of the ASAE, 1995, 38(1): 259.
[28] ZHANG Pei-song, SUN Yi-ming, GUO Peng-tao, et al(张培松, 孙毅明, 郭澎涛, 等). Journal of Tropical Crops(热带作物学报), 2015, 36(12): 2120.
[29] Gitelson A A, Kaufman Y J, Merzlyak M N. Remote Sensing of Environment, 1996, 58(3): 289.
[30] Dash J, Curran P J. International Journal of Remote Sensing, 2004, 25(23): 5403.
[31] Liu X, Wei Y, Jiao Q, et al. Remote Sensing Technology and Application, 2019, 34(4): 756.
[32] CHEN La, HUANG Jing-feng, WANG Xiu-zhen(陈 拉, 黄敬峰, 王秀珍). Journal of Remote Sensing(遥感学报), 2008, 1: 143.
[33] Xue L, Cao W, Luo W, et al. Agronomy Journal, 2004, 96(1): 135.
[34] Chen J M. Canadian Journal of Remote Sensing, 1996, 22(3): 229.
[35] Wang K, Shen Z Q, Wang R C. Journal of Zhejiang University (Agriculture and Life Sciences), 1998, 1: 95.
[36] NIU Qing-lin, FENG Hai-kuan, ZHOU Xin-guo, et al(牛庆林, 冯海宽, 周新国, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2021, 52(8): 183.
[37] Gitelson A A, Viña A, Arkebauer T J, et al. Geophysical Research Letters, 2003, 30(5): 1248.
[38] LIU Chang, YANG Gui-jun, LI Zhen-hai, et al(刘 畅, 杨贵军, 李振海, 等). Scientia Agricultura Sinica(中国农业科学), 2018, 51(16): 3060.
[39] Yue J B, YangG J, Li C, et al. Remote Sensing, 2017, 9: 708.
[40] Yang K, Gong Y, Fang S, et al. Remote Sensing, 2021, 13(15): 3001.
[41] LIU Xin-yi, ZHONG Xiao-chun, CHEN Chen, et al(刘欣谊, 仲晓春, 陈 晨, 等). Journal of Triticeae Crops(麦类作物学报), 2020, 40(8): 1002.
[42] YAO Rui, LIU Jin-rong, LIU Pei-jiang, et al(姚 睿, 刘金容, 刘培江, 等). Mathematical Theory and Applications(数学理论与应用), 2019, 39(1): 111.
[43] OU Qiang-xin, LI Hai-kui, LEI Xiang-dong, et al(欧强新, 李海奎, 雷相东, 等). Chinese Journal of Applied Ecology(应用生态学报), 2018, 29(6): 2007.