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Acta Agronomica Sinica ›› 2022, Vol. 48 ›› Issue (5): 1248-1261.doi: 10.3724/SP.J.1006.2022.02065

• TILLAGE & CULTIVATION·PHYSIOLOGY & BIOCHEMISTRY • Previous Articles     Next Articles

Estimation and evaluation of paddy rice canopy characteristics based on images from UAV and ground camera

WANG Ze1(), ZHOU Qin-Yang1(), LIU Cong1, MU Yue1, GUO Wei2, DING Yan-Feng1,*(), NINOMIYA Seishi1,2,*()   

  1. 1Plant Phenomics Research Center, Academy for Advanced Interdisciplinary Studies, Jiangsu Collaborative Innovation Center for Modern Crop Production, Jiangsu Collaborative Innovation Center for Modern Crop Production co-sponsored by Province and Ministry, Nanjing Agricultural University, Nanjing 210095, Jiangsu, China
    2International Field Phenomics Research Laboratory, Institute for Sustainable Agro-ecosystem Services, University of Tokyo, 1-1-1 Midori-cho, Nishi-Tokyo, Tokyo 188-0002, Japan
  • Received:2020-10-09 Accepted:2021-09-09 Online:2022-05-12 Published:2021-10-19
  • Contact: DING Yan-Feng,NINOMIYA Seishi E-mail:2019101170@njau.edu.cn;2019201022@njau.edu.cn;dingyf@njau.edu.cn;snino@g.ecc.u-tokyo.ac.jp
  • About author:First author contact:**Contributed equally to this work
  • Supported by:
    Plant Phenomics Research Program of Science and Technology Department of Jiangsu Province(BM2018001);Fundamental Research Funds for the Central Universities(KYRC202002)

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Abstract:

Phenotypic monitoring of rice in the field can be used to analyze traits related to rice yield, which is of great significance to guide rice cultivation management and yield prediction. In this study, to explore the applicability of image analysis methods to evaluate rice growth in different fields under multiple cultivars and cultivation environments, we estimated and evaluated the main phenotypic parameters of rice canopy in paddy fields with six different cultivars under three nitrogen treatments. Based on UAV and field fixed camera images, this study used image processing, three-dimensional modeling and machine learning to automatically estimate rice canopy coverage, plant height, and panicle number in the field, and evaluated the accuracy combining with the actual measurement results. The results showed that the rice canopy segmentation based on the decision tree classification model using UAV images were consistent with the manually segmented results (mean value and variance of Qseg was 0.75 and 0.08), and the rice canopy coverage calculated by this method had a relatively high correlation with that calculated by manually segmentation (R2= 0.83, RMSE = 5.36%). The average rice plant height estimated by the canopy height model in each plot had a high correlation with the mean plant height measured in the field (R2= 0.81, RMSE = 9.81 cm), but it was underestimated as a whole. Based on the ground image, the panicle count results obtained by decision tree classification and morphological parameter filter had a relatively high correlation with the measured panicle number (R2= 0.83, RMSE = 10.99). Overall, combined with image analysis algorithm, using low-altitude UAV remote sensing technology to high-throughput and automatically estimate rice canopy coverage and plant height can achieve relatively high accuracy; using image from ground camera to accurately count the rice panicle number is of significant potentiality. The proposed pipeline in this research could be used to analyze nitrogen effect on rice growth status and evaluate nitrogen response of different rice cultivars, and it is of great significance for mining rice field phenotypic information and yield prediction.

Key words: rice phenotype, phenotyping platform, growth parameters, plant height, canopy coverage, panicle counting

Fig. 1

Experimental location and plots layout A: experimental location; B: experimental layout. The map of China comes from the standard map service system (GS(2020)3183) (http://bzdt.ch.mnr.gov.cn/)."

Fig. 2

Cultivars and nitrogen treatments in experimental plots of 2019 A1: Zhongzheyou 8; A2: Y Liangyou 900; A3: Zhaoyou 5431; B1: Nanjing 9108; B2: Ningjing 8; B3: Wuyunjing 23. Plot in gray, light green, and dark green is nitrogen level of N0, N150, and N300, respectively."

Fig. S1

Cultivars and nitrogen treatments in experimental plots of 2020 a1: Huanghuazhan; a2: Yliangyou900; a3: Zhongzheyou1; a4: Yongyou1540; b1: Ningjing8; b2: Nanjing46; b3: 9You418; b4: Yongyou538. Plot in green, purple, brown, red and light green is nitrogen level of normal 300 kg hm-2 (N1), 10%reduction 270 kg hm-2 (N2), 20%reduction 240 kg hm-2(N3), 20%reduction by using slow-mixed fertilizer 240 kg hm-2 (N4) and 30%reduction kg hm-2 (N5), respectively."

Fig. 3

An automatic image acquisition and transmission device for field crops using solar energy"

Fig. 4

Workflow for rice canopy phenotypic parameters estimation based on ground fixed camera platform and low altitude UAV platform"

Fig. 5

RGB orthomosaic (A) and canopy surface height (B) in 2019"

Fig. 6

Rice ear segmentation and denoising A: decision-tree-based segmentation output image; B: morphological parameter filtering output image."

Fig. 7

Classification results of 2019 UAV images based on decision tree model A: orthophoto; B: binary image of model classification."

Fig. 8

Correlation analysis and linear regression between calculated and measured rice canopy height"

Fig. S2

Classification results of UAV images based on decision tree model (A: orthophoto; B: binary image of classification)"

Fig. 9

Correlation analysis and linear regression between calculated and measured rice plant height"

Table 1

Statistics of rice canopy phenotypic parameters in 2019"

品种
Rice variety		 栽培品种
Cultivar		 施氮量
Nitrogen application (kg hm-2)		 估算冠层覆盖度
Estimated canopy coverage (%)		 校正后株高
Calibrated height (cm)
籼稻
Japonica		 中浙优8
Zhongzheyou 8		 0 61.76 ± 4.72 b 67.91 ± 3.44 c
150 87.30 ± 3.22 a 91.34 ± 5.37 b
300 84.86 ± 3.58 a 101.73 ± 0.45 a
Y两优900
Y Liangyou 900		 0 61.49 ± 1.29 b 58.47 ± 7.81 b
150 90.96 ± 3.61 a 78.54 ± 3.48 a
300 84.69 ± 5.47 a 87.82 ± 3.83 a
兆优5431
Zhaoyou 5431		 0 84.72 ± 5.99 b 67.41 ± 5.29 b
150 97.65 ± 0.77 a 93.08 ± 1.94 a
300 90.45 ± 2.62 ab 100.56 ± 2.95 a
粳稻
Indica		 南粳9108
Nanjing 9108		 0 54.05 ± 4.40 b 48.67 ± 2.99 b
150 90.95 ± 3.43 a 66.56 ± 2.13 a
300 90.16 ± 3.21 a 71.10 ± 1.53 a
宁粳8号
Ningjing 8		 0 39.53 ± 2.12 b 45.29 ± 8.51 b
150 70.79 ± 2.46 a 60.42 ± 1.19 a
300 68.82 ± 5.66 a 66.79 ± 1.45 a
武运粳23
Wuyunjing 23		 0 60.65 ± 7.28 b 46.79 ± 4.58 b
150 90.35 ± 3.70 a 64.13 ± 0.42 a
300 89.66 ± 3.02 a 67.89 ± 1.91 a

Fig. 10

Comparison between manually and automatically identifying rice panicle number"

Fig. 11

Automatic identification of rice panicle A: original figure; B: manual labeling; C: automatic identification; D: difference of manual labeling and automatic identification. Pink represents the tassels identified by the algorithm, and blue represents the tassels artificially marked."

[1] 段凌凤, 杨万能. 水稻表型组学研究概况和展望. 生命科学, 2016, 10:1129-1137.
Duan L F, Yang W N. Research advances and future scenarios of rice phenomics. Chin Bull Life Sci, 2016, 10:1129-1137 (in Chinese with English abstract).
[2] 朱德峰, 程式华, 张玉屏, 林贤青, 陈惠哲. 全球水稻生产现状与制约因素分析. 中国农业科学, 2010, 43:474-479.
Zhu D F, Cheng S H, Zhang Y P, Lin X Q, Chen H Z. Analysis of status and constraints of rice production in the world. Sci Agric Sin, 2010, 43:474-479 (in Chinese with English abstract).
[3] 彭永彬, 谢先芝. 表型组学在水稻研究中的应用. 中国水稻科学, 2020, 34:300-306.
Peng Y B, Xie X Z. Application of phenomics in rice research. Chin J Rice Sci, 2020, 34:300-304 (in Chinese with English abstract).
[4] 章曼. 基于高光谱遥感的水稻生长监测研究. 西北农林科技大学硕士学位论文, 陕西杨凌, 2015.
Zhang M. Based on Hyperspectral Remote Sensing of the Rice Growth Monitoring Research. MS Thesis of Northwest Agriculture and Forest University, Yangling, Shaanxi, China, 2015 (in Chinese with English abstract).
[5] Han L, Yang G J, Dai H Y, Xu B, Yang H, Feng H K, Li Z H, Yang X D. Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data. BioMed Central, 2019, 15:10.
[6] Busemeyer L, Mentrup D, Möller K, Wunder E, Alheit K, Hahn V, Maurer H, Reif J, Würschum T, Müller J, Rahe F, Ruckelshausen A. BreedVision—a multi-sensor platform for non-destructive field-based phenotyping in plant breeding. Sensors, 2013, 13:2830-2847.
doi: 10.3390/s130302830 pmid: 23447014
[7] Walter A, Liebisch F, Hund A. Plant phenotyping: from bean weighing to image analysis. Plant Methods, 2015, 11:14.
doi: 10.1186/s13007-015-0056-8 pmid: 25767559
[8] Jin X L, Liu S Y, Baret F, Hemerlé M, Comar A. Estimates of plant density of wheat crops at emergence from very low altitude UAV imagery. Remote Sens Environ, 2017, 198:105-114.
doi: 10.1016/j.rse.2017.06.007
[9] 张玉盛, 肖欢, 吴勇俊, 杨小粉, 汪泽钱, 伍湘, 向焱赟, 张小毅, 敖和军. 粒肥施用时期对水稻镉积累的影响初探. 华北农学报, 2020, 35(2):144-151.
Zhang Y S, Xiao H, Wu Y J, Yang X F, Wang Z Q, Wu X, Xiang Y B, Zhang X Y, Ao H J. Effect of application period of granular fertilizer on cadmium accumulation in rice. Acta Agric Boreali-Sin, 2020, 35(2):144-151 (in Chinese with English abstract).
[10] 韩焕豪, 崔远来, 时元智, 余双, 陈劲丰. SunScan冠层分析仪在水稻叶面积指数测量中的应用. 灌溉排水学报, 2015, 34(8):44-48.
Han H H, Cui Y L, Shi Y Z, Yu S, Chen J F. Application of SunScan canopy analysis system to measure leaf area index of rice. J Irrig Drain, 2015, 34(8):44-48 (in Chinese with English abstract).
[11] Toshihiro S, Cao V P, Aikihiko K, Khang D N, Masayuki Y. Analysis of rapid expansion of inland aquaculture and triple rice-cropping areas in a coastal area of the Vietnamese Mekong Delta using MODIS time-series imagery. Landscape Urban Plan, 2009, 92:34-46.
doi: 10.1016/j.landurbplan.2009.02.002
[12] Xiao X M, Boles S, Frolking S, Li C S, Jagadeesh Y.B, Salas W, Moore B. Mapping paddy rice agriculture in South and Southeast Asia using multi-temporal MODIS images. Remote Sens Environ, 2005, 100:95-113.
doi: 10.1016/j.rse.2005.10.004
[13] Yang G J, Liu J G, Zhao C J, Li Z H, Huang Y B, Yu H Y, Xu B, Yang X D, Zhu D M, Zhang X Y, Zhang R Y, Feng H K, Zhao X Q, Li Z H, Li H L, Yang H. Unmanned aerial vehicle remote sensing for field-based crop phenotyping: current status and perspectives. Front Plant Sci, 2017, 8:1111.
doi: 10.3389/fpls.2017.01111
[14] 陈仲新, 郝鹏宇, 刘佳, 安萌, 韩波. 农业遥感卫星发展现状及我国监测需求分析. 智慧农业, 2019, 1(1):32-42.
Chen Z X, Hao P Y, Liu J, An M, Han B. Technical demands for agricultural remote sensing satellites in China. Smart Agric, 2019, 1(1):32-42 (in Chinese with English abstract).
[15] 汪沛, 罗锡文, 周志艳, 臧英, 胡炼. 罗基于微小型无人机的遥感信息获取关键技术综述. 农业工程学报, 2014, 30(18):1-12.
Wang P, Luo X W, Zhou Z Y, Zang Y, Hu L. Key technology for remote sensing information acquisition based on micro UAV. Trans CSAE, 2014, 30(18):1-12 (in Chinese with English abstract).
[16] Desai S V, Balasubramanian V N, Fukatsu T, Ninomiya S, Guo W. Automatic estimation of heading date of paddy rice using deep learning. Plant Methods, 2019, 15:1-11.
doi: 10.1186/s13007-018-0385-5
[17] 丁国辉, 许昊, 温明星, 陈佳玮, 王秀娥. 基于经济型低空无人机对小麦重要产量表型性状的多生育时期获取和自动化分析. 农业大数据学报, 2019, 1(2):19-31.
Ding G H, Xu H, Wen M X, Chen J W, Wang X E. Developing cost-effective and low-altitude UAV aerial phenotyping and automated phenotypic analysis to measure key yield-related traits for bread wheat. J Agric Big Data, 2019, 1(2):19-31 (in Chinese with English abstract).
[18] Guo W, Rage U K, Ninomiya S. Illumination invariant segmentation of vegetation for time series wheat images based on decision tree model. Comput Electron Agric, 2013, 96:58-66.
doi: 10.1016/j.compag.2013.04.010
[19] Duan T, Zheng B Y, Guo W, Ninomiya S, Guo Y, Chapman S C. Comparison of ground cover estimates from experiment plots in cotton, sorghum and sugarcane based on images and ortho-mosaics captured by UAV. Funct Plant Biol, 2017, 44:169.
doi: 10.1071/FP16123
[20] 赵锋, 王克俭, 苑迎春. 基于颜色特征的AdaBoost算法的麦穗识别的研究. 作物杂志, 2014, (1):141-144.
Zhao F, Wang K J, Yuan Y C. Study on wheat spike identification based on color features and AdaBoost Algorithm. Crops, 2014, (1):141-144 (in Chinese with English abstract).
[21] Cointault F, Guerin D, Guillemin J-P, Chopinet B. In-field Triticum aestivum ear counting using colour-texture image analysis. New Zeal J Crop Hortic, 2008, 36:117-130.
[22] Zhou C Q, Liang D, Yang X D, Yang H, Yue J B, Yang G J. Wheat ears counting in field conditions based on multi-feature optimization and TWSVM. Front Plant Sci, 2018, 9:1024.
doi: 10.3389/fpls.2018.01024
[23] Fernandez-Gallego J A, Kefauver S C, Gutiérrez N A, Nieto- Taladriz M T, Araus J L. Wheat ear counting in-field conditions: high throughput and low-cost approach using RGB images. Plant Methods, 2018, 14:22.
doi: 10.1186/s13007-018-0289-4 pmid: 29568319
[24] Xiong X, Duan L F, Liu L B, Tu H F, Yang P, Wu D, Chen G X, Xiong L Z, Yang W N, Liu Q. Panicle-SEG: a robust image segmentation method for rice panicles in the field based on deep learning and superpixel optimization. Plant Methods, 2017, 13:104.
doi: 10.1186/s13007-017-0254-7 pmid: 29209408
[25] Olsen P A, Ramamurthy K N, Ribera J, Chen Y H, Thompson A M, Luss R, Tuinstra M, Abe N. Detecting and counting panicles in sorghum images. The 5th IEEE International Conference on Data Science and Advanced Analytics, Turin, Italy, 2018.
[26] 段凌凤, 熊雄, 刘谦, 杨万能, 黄成龙. 基于深度全卷积神经网络的大田稻穗分割. 农业工程学报, 2018, 34(12):202-209.
Duan L F, Xiong X, Liu Q, Yang W N, Huang C L. Field rice panicles segmentation based on deep full convolutional neural network. Trans CSAE, 2018, 34(12):202-209 (in Chinese with English abstract)
[27] 黄国祥. RGB颜色空间及其应用研究. 中南大学博士学位论文, 湖南长沙, 2002.
Huang G X. RGB Color Space and It’s Application. PhD Dissertation of Central South University, Changsha, Hunan, China, 2002 (in Chinese with English abstract).
[28] 秦绪佳, 王慧玲, 杜轶诚, 郑红波, 梁震华. Hsv色彩空间的retinex结构光图像增强算法. 计算机辅助设计与图形学学报, 2013, 25:488-493.
Qin X J, Wang H L, Du Y C, Zheng H B, Liang Z H. Structured light image enhancement algorithm based on Retinex in HSV color space. J Comp-Aied Desig Comp Grap, 2013, 25:488-493.
[29] 张宏建. Lab色彩模式在图像处理中的应用. 福建电脑, 2011, 27(1):146-147.
Zhang H J. Application of Lab color mode in image processing. J Fujian Comput, 2011, 27(1):146-147 (in Chinese with English abstract).
[30] Guo W, Zheng B Y, Duan T, Fukatsu T, Chapman S C, Ninomiya S. EasyPCC: benchmark datasets and tools for high-throughput measurement of the plant canopy coverage ratio under field conditions. Sensors, 2017, 17:798.
doi: 10.3390/s17040798
[31] Guo W, Zheng B Y, Potgieter A B, Diot J, Watanabe K, Noshita K, Jordan D R, Wang X M, Watson J, Ninomiya S, Chapman S C. Aerial imagery analysis-quantifying appearance and number of sorghum heads for applications in breeding and agronomy. Front Plant Sci, 2018, 9:1544.
doi: 10.3389/fpls.2018.01544
[32] 段凌凤. 水稻植株穗部性状在体测量研究. 华中科技大学博士学位论文, 湖北武汉, 2013.
Duan L F. Panicle Traits Measurement of Rice Plant in vivo. PhD Dissertation of Huazhong University of Science and Technology, Wuhan, Hubei, China, 2013 (in Chinese with English abstract).
[33] 王秀娟, 康孟珍, 华净, de Reffye P. 从群体到个体尺度—基于数据的DSSAT和GreenLab作物模型连接探索. 智慧农业, 2021, 3(2):77-87.
Wang X J, Kang M Z, Hua J, de Reffye P. From stand to organ level: a trial of connecting DSSAT and GreenLab crop model through data. Smart Agric, 2021, 3(2):77-87 (in Chinese with English abstract)
[34] Bouman B, Keulen H V, Laar H, Rabbinge R. The ‘School of de Wit’ crop growth simulation models: a pedigree and historical overview. Agric Syst, 1996, 52:171-198.
doi: 10.1016/0308-521X(96)00011-X
[35] Woebbecke D M, Meyer G E, Bargen K V, Mortensen D A. Color indices for weed identification under various soil, residue, and lighting conditions. Trans ASAE, 1995, 38:259-269.
doi: 10.13031/2013.27838
[36] Meyer G E, Neto J C. Verification of color vegetation indices for automated crop imaging applications. Comput Electron Agric, 2008, 63:282-293.
doi: 10.1016/j.compag.2008.03.009
[37] Burgos-Artizzu X P, Ribeiro A, Guijarro M, Pajares G. Real-time image processing for crop/weed discrimination in maize fields. Comput Electron Agric, 2011, 75:337-346.
doi: 10.1016/j.compag.2010.12.011
[38] Pérez A J, López F, Benlloch J V, Christensen S. Colour and shape analysis techniques for weed detection in cereal fields. Comput Elect Agric, 2000, 25:197-212.
doi: 10.1016/S0168-1699(99)00068-X
[39] 李存军. 基于数字照片特征的小麦覆盖度自动提取研究. 浙江大学学报(农业与生命科学版), 2004, 30(6):64-70.
Li C J. Study on automatic extraction of wheat coverage based on digital photo features. J Zhejiang Univ(Agric Life Sci Edn), 2004, 30(6):64-70 (in Chinese with English abstract).
[40] Lukina E V, Stone M L, Raun W R. Estimating vegetation coverage in wheat using digital images. J Plant Nutr, 1999, 22:341-350.
doi: 10.1080/01904169909365631
[41] 计野. 无人机遥感图像内部畸变校正算法及应用研究. 电子科技大学博士学位论文, 四川成都, 2010.
Ji Y. Research on Internal Distortion Correction Algorithm and Application of UAV Remote Sensing Image. PhD Dissertation of University of Electronic Science and Technology of China, Chengdu, Sichuan, China, 2010 (in Chinese with English abstract).
[42] Weng J, Cohen P. Camera calibration with distortion models and accuracy evaluation. Patt Anal Mach Intell IEEE Trans, 1992, 14:965-980.
doi: 10.1109/34.159901
[43] 牛庆林, 冯海宽, 杨贵军, 李长春, 杨浩, 徐波. 基于无人机数码影像的玉米育种材料株高和LAI监测. 农业工程学报, 2018, 34(5):73-82.
Niu Q L, Feng H K, Yang G J, Li C C, Yang H, Xu B. Monitoring plant height and leaf area index of maize breeding material based on UAV digital images. Trans CSAE, 2018, 34(5):73-82 (in Chinese with English abstract).
[44] 胡鹏程. 基于无人机近感的高通量田间作物几何表型研究. 中国农业大学博士学位论文, 北京, 2018.
Hu P C. High-throughput Field Morphological Phenotyping using UAV-based Proximal Sensing. PhD Dissertation of China Agricultural University, Beijing, China, 2018 (in Chinese with English abstract).
[45] 姜海燕, 徐灿, 陈尧, 成永康. 基于田间图像的局部遮挡小尺寸稻穗检测和计数方法. 农业机械学报, 2020, 51(9):152-162.
Jiang H Y, Xu C, Chen Y, Cheng Y K. A detecting and counting method for small-sized and occluded rice panicles based on in-field images. Trans CSAM, 2020, 51(9):152-162 (in Chinese with English abstract).
[46] Madec S, Jin X, Lu H, Solan B D, Liu S, Duyme F. Ear density estimation from high resolution RGB imagery using deep learning technique. Agric For Meteorol, 2019, 264:225-234.
doi: 10.1016/j.agrformet.2018.10.013
[47] Xiong H, Cao Z, Lu H, Madec S, Shen C. TasselNetv2: in-field counting of wheat spikes with context-augmented local regression networks. Plant Methods, 2019, 15:150.
doi: 10.1186/s13007-019-0537-2
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