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


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)


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"

Rice variety
Nitrogen application (kg hm-2)
Estimated canopy coverage (%)
Calibrated height (cm)
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 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
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
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
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
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."

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