UAV LOW-ALTITUDE AGRICULTURAL INFORMATION REMOTE SENSING MONITORING
Keywords:
UAV, Low-altitude remote sensing, Agricultural conditions monitoring, Information precisionAbstract
Timely and accurate acquisition of field crop growth status and environmental information is the premise and foundation for precise crop management. UAV low-altitude remote sensing technology has incomparable advantages in obtaining crop images of different scales, and has become an important means and method for agricultural information monitoring. This article mainly focuses on the composition of UAV low-altitude remote sensing systems, crop growth monitoring, and yield prediction. The research and application of nutrition diagnosis, pest and disease monitoring, crop lodging, growth stress diagnosis, etc. were summarized and analyzed. The problems of UAV low-altitude remote sensing in agricultural information monitoring were analyzed. Finally, the application prospects and development were proposed. Trend. Future research should focus on the continuous expansion of the breadth and depth of remote sensing monitoring of agricultural conditions, the continuous development of intelligence, and the exploration of low-cost, micro-miniature sensing equipment, and use the integration and complementarity of multi-source data to form a highly versatile system, easy-to-operate solution, further expanding the application scope of UAV low-altitude remote sensing in the acquisition and analysis of crop phenotypic information in precision agriculture.References
[1] He Yong, Cen Haiyan, He Liwen. Agricultural UAV technology and its applications. Beijing: Science Press, 2018: 232-233.
[2] Huang Yiqi, Liu Qi, Zhao Jianye. Research on mangrove drone monitoring based on convolutional neural network. Chinese Journal of Agricultural Mechanization, 2020, 41(2): 141-146+189.
[3] SIEBERTH T, WACKROW R, CHANDLER J H. Automatic detection of blurred images in UAV image sets. ISPRS Journal of Photogrammetry and Remote Sensing, 2016, 122: 1-16.
[4] Wang Pei, Luo Xiwen, Zhou Zhiyan. Review of key technologies for remote sensing information acquisition based on micro-UAVs. Chinese Journal of Agricultural Engineering, 2014, 30(18): 1-12.
[5] Li Xiaopeng, Hu Pengcheng, Xu Zhaoli. Method and application of rapidly acquiring field plant images based on quad-rotor drones. Journal of China Agricultural University, 2017, 22(12): 131-137.
[6] Liu Jiangang, Zhao Chunjiang, Yang Guijun. Research progress in analyzing field crop phenotypic information using UAV remote sensing. Chinese Journal of Agricultural Engineering, 2016, 32(24): 98-106.
[7] Zhao Peng, Shen Tingzhi, Shan Baotang. Design of micro-UAV remote sensing system based on CMOS image sensor. Acta Photonics, 2008(8): 1657-1661.
[8] Liu Zhong, Wan Wei, Huang Jinyu. Research progress on inversion of key parameters of crop growth based on UAV remote sensing. Chinese Journal of Agricultural Engineering, 2018, 34(24): 60-71.
[9] AASEN H, BURKART A, BOLTEN A. Generating 3D hyperspectral information with lightweight UAV snapshot cameras for vegetation monitoring: From camera calibration to quality assurance. ISPRS Journal of Photogrammetry and Remote Sensing, 2015, 108: 245-259.
[10] SANTESTEBAN LG, DI SF, HERRERO A. High-resolution UAV-based thermal imaging to estimate the instantaneous and seasonal variability of plant water status within a vineyard. Agricultural Water Management, 2017, 183: 49-59.
[11] Han Wenxuan, Arifu Kurban, Huang Zitong. Rapid detection of small targets in remote sensing images based on improved SSD algorithm. Journal of Xinjiang University (Natural Science Edition) (Chinese and English), 2020, 37(2): 163-169.
[12] Shao Guomin. Research on field corn crop coefficient estimation method based on UAV multispectral remote sensing. Yangling: Northwest A&F University, 2018.
[13] Gao Lin, Li Changchun, Wang Baoshan. Comparison of soybean leaf area index estimation accuracy based on multi-source remote sensing data. Journal of Applied Ecology, 2016, 27(1): 191-200.
[14] Chen Peng, Feng Haikuan, Li Changchun. Estimating chlorophyll content of potato leaves using spectral and texture fusion information from drone images. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(11): 63-74.
[15] BIAN J, ZHANG Z, CHEN J. Simple evaluation of cotton water stress using high resolution unmanned aerial vehicle thermal imagery. Remote Sensing, 2019, 11(3): 267.
[16] HASSAN MA, YANG MJ, RASHEED AY. A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform. Plant science: an International Journal of Experimental Plant Biology, 2019, 282: 95-103.
[17] Pei Haojie, Feng Haikuan, Li Changchun. UAV remote sensing monitoring of winter wheat growth based on comprehensive indicators. Chinese Journal of Agricultural Engineering, 2017, 33(20): 74-82.
[18] Gao Lin, Yang Guijun, Yu Haiyang. Winter wheat leaf area index inversion based on UAV hyperspectral remote sensing. Chinese Journal of Agricultural Engineering, 2016, 32(22): 113-120.
[19] Tao Huilin, Xu Liangji, Feng Haikuan. Winter wheat growth monitoring based on UAV hyperspectral growth indicators. Journal of Agricultural Machinery, 2020, 51(2): 180-191.
[20] NEUMANN K, VERBURG PH, STEHFEST E. The yield gap of global grain production: A spatial analysis. Agricultural Systems, 2010, 103(5): 316-326.
[21] TEWES A, SCHELLBERG J. Towards remote estimation of radiation use e?ciency in maize using UAV-based low-cost camera imagery. Agronomy, 2018, 8(2): 16.
[22] Zhao Xiaoqing, Yang Guijun, Liu Jiangang. Soybean breeding yield estimation based on UAV-borne hyperspectral spatial scale optimization. Chinese Journal of Agricultural Engineering, 2017, 33(1) 110-116.
[23] Zhu Wanxue, Li Shiji, Zhang Xubo. Field-scale winter wheat yield estimation based on UAV remote sensing vegetation index optimization. Chinese Journal of Agricultural Engineering, 2018, 34(11): 78-86.
[24] MD NR, SEOP N, SUN W B. Rice yield estimation based on K-means clustering with graph-cut segmentation using low-altitude UAV images. Biosystems Engineering, 2019, 177: 109-121.
[25] ZHANG MN, FENG AJ, ZHOU JF. Cotton yield prediction using remote visual and spectral images captured by UAV system. Transactions of the Chinese Society of Agricultural Engineering, 2019, 35(5): 91-98.
[26] Qin Zhanfei, Chang Qingrui, Xie Baoni. Estimation of total nitrogen content of rice leaves in the Yellow River irrigation area based on UAV hyperspectral images. Transactions of the Chinese Society of Agricultural Engineering, 2016, 32(23): 77-85.
[27] Wei Pengfei, Xu Xingang, Li Zhongyuan. Remote sensing estimation of summer corn leaf nitrogen content based on UAV multispectral images. Chinese Journal of Agricultural Engineering, 2019, 35(8): 126-133.
[28] LIU HY, ZHU HC, WANG P. Quantitative modeling for leaf nitrogen content of winter wheat using UAV-based hyper-spectral data. International Journal of Remote Sensing, 2017, 38(8/9/10): 2117-2134.
[29] JAY S, MAUPAS F, BENDOULA R. Retrieving LAI, chlorophyll and nitrogen contents in sugar beet crops from multi-angul