|
|
|
|
|
|
| Estimation of Potato Above Ground Biomass Based on Hyperspectral Images of UAV |
| LIU Yang1, 4, 5, ZHANG Han2, FENG Hai-kuan1, 3, 5*, SUN Qian1, 5, HUANG Jue4, WANG Jiao-jiao1, 5, YANG Gui-jun1, 5 |
1. Key Laboratory of Quantitative Remote Sensing in Agriculture, Ministry of Agriculture, Beijing Research Center for Information Technology in Agriculture, Beijing 100097, China
2. University of New South Wales, Sydney NSW 2052, Australia
3. National Information Agriculture Engineering Technology Center of Nanjing Agricultural University, Nanjing 210095, China
4. College of Surveying Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
5. National Engineering Research Center for Information Technology in Agriculture, Beijing 100097, China |
|
|
|
|
Abstract Accurate monitoring of above-ground biomass (AGB) is an important part of farm production management, so rapid and accurate estimation of AGB is important for the development of precision agriculture. Traditionally, AGB has been obtained using destructive sampling methods, which makes large-area, long-term measurements difficult. However, with the advancement of science and technology, UAV hyperspectral remote sensing has become the most effective technical means to estimate AGB of large crops because of its advantages of high mobility, high spectral resolution and map integration. In this study, the canopy hyperspectral images and actual AGB data of potato tuber formation, tuber growth and starch accumulation stages were obtained by carrying imaging spectrometer sensors on the UAV platform and drying and weighing method, respectively. Correlation analysis method (CAM), random frog method (RFM) and Gaussian process regression bands analysis tool (GPR-BAT) were used to screen canopy original spectra (COS) and first-order derivative spectra (FDS) for sensitive wavelengths, respectively, combined with partial least squares regression (PLSR) and Gaussian process regression (GPR) techniques to establish AGB estimation models for each fertility period and the estimation effects of different models were compared. The results showed that (1) the effect of combining the two regression techniques based on the characteristic wavelengths screened by the same method for COS and FDS to estimate AGB all changed from good to bad from the tuber formation stage to the starch accumulation stage. (2) Based on the characteristic wavelengths screened by the three methods of FDS respectively, the models constructed by homogeneous regression techniques are more effective than those based on COS accordingly. (3) The number of characteristic wavelengths screened based on COS and FDS using CAM, RFM and GPR-BAT methods were 28, 12, 6 and 12, 23, 10 at the tuber formation stage, 32, 8, 2 and 18, 28, 4 at the tuber growth stage, and 30, 15, 3 and 21, 33, 5 at the starch accumulation stage, respectively. (4) The effect of sensitive wavelengths for AGB estimation based on COS and FDS screened by three methods at each reproductive stage were GPR-BAT, RFM and CAM in descending order. (5) The models based on sensitive wavelengths screened by FDS through the GPR-BAT method at each fertility stage combined with PLSR were more accurate and stable with R2 of 0. 67, 0. 73 and 0. 65, NRMSE of 16.63%, 15.84% and 20.81%, respectively. This study shows that AGB can be accurately estimated using UAV hyperspectral imaging technology, which provides scientific guidance and reference for achieving dynamic monitoring of potato crop growth.
|
|
Received: 2021-04-08
Accepted: 2021-06-28
|
|
|
|
Corresponding Authors:
FENG Hai-kuan
E-mail: fenghaikuan123@163.com
|
|
[1] LIU Yang, FENG Hai-kuan, HUANG Jue, et al（刘 杨，冯海宽，黄 珏，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报）, 2020, 36(23): 181.
[2] LIU Yang, FENG Hai-kuan, HUANG Jue, et al（刘 杨，冯海宽，黄 珏，等）. Transactions of the Chinese Society for Agricultural Machinery（农业机械学报）, 2021, 52(2): 188.
[3] Yue J B, Feng H K, Yang G J, et al. Remote Sensing, 2018, 10(2): 66.
[4] Tao H L, Feng H K, Xu L J, et al. Sensors, 2020, 20(5): 1296.
[5] Kanke Y, Tubana B, Dalen M, et al. Precision Agriculture, 2016, 17(5): 507.
[6] LIU Bin, REN Jian-qiang, CHEN Zhong-xin, et al（刘 斌，任建强，陈仲新，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报）, 2016, 32(16): 125.
[7] TAO Hui-lin, FENG Hai-kuan, XU Liang-ji, et al（陶惠林，冯海宽，徐良骥，等）. Jiangsu Journal of Agricultural Sciences（江苏农业学报）, 2020, 36(5): 1154.
[8] SHI Ya-jiao, CHEN Peng-fei（石雅娇，陈鹏飞）. Chinese Agricultural Science Bulletin（中国农学通报）, 2019, 35(17): 117.
[9] Liu N F, Townsend P A, Naber M R, et al. Remote Sensing of Environment, 2021, 255(3): 112303.
[10] Luo S J, He Y B, Li Q, et al. Journal of Applied Remote Sensing, 2020, 14(1): 1.
[11] SUN Hong, ZHENG Tao, LIU Ning, et al（孙 红，郑 涛，刘 宁，等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报）, 2018, 34(1): 149.
[12] SUN Hong, LIU Ning, WU Li, et al（孙 红，刘 宁，吴 莉，等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析）, 2019, 39(3): 910.
[13] Fu Y Y, Yang G J, Li Z H, et al. Remote Sensing, 2020, 12(22): 3778. |
| [1] |
LI Xin-ting, ZHANG Feng, FENG Jie*. Convolutional Neural Network Combined With Improved Spectral
Processing Method for Potato Disease Detection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 215-224. |
| [2] |
ZHU Wen-jing1, 2，FENG Zhan-kang1, 2，DAI Shi-yuan1, 2，ZHANG Ping-ping3，JI Wen4，WANG Ai-chen1, 2，WEI Xin-hua1, 2*. Multi-Feature Fusion Detection of Wheat Lodging Information Based on UAV Multispectral Images[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2024, 44(01): 197-206. |
| [3] |
LI Wei1, TAN Feng2*, ZHANG Wei1, GAO Lu-si3, LI Jin-shan4. Application of Improved Random Frog Algorithm in Fast Identification of Soybean Varieties[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3763-3769. |
| [4] |
SHEN Si-cong, ZHANG Jing-xue, CHEN Ming-hui, LI Zhi-wei, SUN Sheng-nan, YAN Xue-bing*. Estimation of Above-Ground Biomass and Chlorophyll Content of
Different Alfalfa Varieties Based on UAV Multi-Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3847-3852. |
| [5] |
HUANG You-ju1, TIAN Yi-chao2, 3*, ZHANG Qiang2, TAO Jin2, ZHANG Ya-li2, YANG Yong-wei2, LIN Jun-liang2. Estimation of Aboveground Biomass of Mangroves in Maowei Sea of Beibu Gulf Based on ZY-1-02D Satellite Hyperspectral Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(12): 3906-3915. |
| [6] |
ZHU Zhi-cheng1, WU Yong-feng2*, MA Jun-cheng2, JI Lin2, LIU Bin-hui3*, JIN Hai-liang1*. Response of Winter Wheat Canopy Spectra to Chlorophyll Changes Under Water Stress Based on Unmanned Aerial Vehicle Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3524-3534. |
| [7] |
SUN Lin1, BI Wei-hong1, LIU Tong1, WU Jia-qing1, ZHANG Bao-jun1, FU Guang-wei1, JIN Wa1, WANG Bing2, FU Xing-hu1*. Identification Algorithm of Green Algae Using Airborne Hyperspectral and Machine Learning Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(11): 3637-3643. |
| [8] |
CHEN Jia-wei1, 2, ZHOU De-qiang1, 2*, CUI Chen-hao3, REN Zhi-jun1, ZUO Wen-juan1. Prediction Model of Farinograph Characteristics of Wheat Flour Based on Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3089-3097. |
| [9] |
FENG Hai-kuan1, 2, FAN Yi-guang1, TAO Hui-lin1, YANG Fu-qin3, YANG Gui-jun1, ZHAO Chun-jiang1, 2*. Monitoring of Nitrogen Content in Winter Wheat Based on UAV
Hyperspectral Imagery[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3239-3246. |
| [10] |
AN Bai-song1, 2, WANG Xue-mei1, 2*, HUANG Xiao-yu1, 2, KAWUQIATI Bai-shan1, 2. Hyperspectral Estimation of Soil Lead Content Based on Random Frog Band Selection Algorithm[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3302-3309. |
| [11] |
WEI Zi-kai, WANG Jie, ZHANG Ruo-yu, ZHANG Meng-yun*. Classification of Foreign Matter in Cotton Using Line Scan Hyperspectral Transmittance Imaging[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3230-3238. |
| [12] |
FENG Hai-kuan1, 2, YUE Ji-bo3, FAN Yi-guang2, YANG Gui-jun2, ZHAO Chun-jiang1, 2*. Estimation of Potato Above-Ground Biomass Based on VGC-AGB Model and Hyperspectral Remote Sensing[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(09): 2876-2884. |
| [13] |
XIA Chen-zhen1, 2, 3, JIANG Yan-yan4, ZHANG Xing-yu1, 2, 3, SHA Ye5, CUI Shuai1, 2, 3, MI Guo-hua5, GAO Qiang1, 2, 3, ZHANG Yue1, 2, 3*. Estimation of Soil Organic Matter in Maize Field of Black Soil Area Based on UAV Hyperspectral Image[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(08): 2617-2626. |
| [14] |
SONG Cheng-yang1, GENG Hong-wei1, FEI Shuai-peng2, LI Lei2, GAN Tian2, ZENG Chao-wu3, XIAO Yong-gui2*, TAO Zhi-qiang2*. Study on Yield Estimation of Wheat Varieties Based on Multi-Source Data[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(07): 2210-2219. |
| [15] |
ZHOU Qi1, 2, WANG Jian-jun1, 2*, HUO Zhong-yang1, 2*, LIU Chang1, 2, WANG Wei-ling1, 2, DING Lin3. UAV Multi-Spectral Remote Sensing Estimation of Wheat Canopy SPAD Value in Different Growth Periods[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1912-1920. |
|
|
|
|
