# Grain Yield Estimation in Rice Breeding Using Phenological Data and Vegetation Indices Derived from UAV Images

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

^{2}of 0.78 and RMSE of 391 kg ha

^{−1}based on the UAV imagery. Ashapure et al. [33] demonstrated that a simple Artificial Neural Network (ANN) model could perform well for yield estimation in a set of tomato varieties by using UAV-RGB imagery.

## 2. Materials and Methods

#### 2.1. Field Trial Design

^{−1}, and 1.76 g kg

^{−1}, respectively.

^{−1}), P

_{2}O

_{5}(112 kg ha

^{−1}), and K

_{2}O (112 kg ha

^{−1}), using urea as the N fertilizer. The N fertilizer was split into three applications, with 40% being basal fertilizer, 40% being tillering fertilizer, and 20% being ear fertilizer. All phosphorus and potassium fertilizer were used as basal fertilizer. Water, insects, weeds, and disease were controlled when needed. Sowing was conducted in early June; then, transplanting was performed in late June or early July, depending on the climate conditions and seedling growth status (Table 1).

#### 2.2. Field Data Collection

#### 2.3. UAV Data Acquisition and Image Processing

#### 2.4. RF Model

#### 2.5. CERES-Rice Model

#### 2.5.1. Input Data

^{−2}d

^{−1}), and precipitation (mm). The soil parameters and characteristics are shown in Table 4. The physical and chemical properties of each horizon of collected soil samples (Table 4) were analyzed by using the Soil Science Society of America (SSSA) and American Society of Agronomy (ASA) methods for soil analysis [53]. Field management information, such as planting date, transplanting date, harvest date, and fertilization amount, were shown in Table 1, Table A1, and Section 2.1. There are eight cultivar parameters in the CERES-Rice model: four phenology-related parameters (P1, P2R, P5, and P2O) and four yield-related parameters (G1, G2, G3, and G4). The cultivar parameters were determined by the generalized likelihood uncertainty estimation (GLUE) method. The specific procedures of parameter estimations were described in the following section.

#### 2.5.2. Cultivar Parameter Estimations

#### 2.6. Statistical Methods

^{2}, Equation (2)), root mean square error (RMSE, Equation (3)), and mean absolute error (MAE, Equation (4)). These statistical indices were calculated as follows:

^{2}, a smaller RMSE and MAE corresponds to more accurate results.

## 3. Results

#### 3.1. Statistical Analysis of Measured Yield

#### 3.2. Analysis of VIs and Phenological Data

#### 3.3. RF Method for Yield Estimation

^{2}, RMSE, and MAE. Figure 5 shows the distributions of the simulated yield by those models in calibration and validation sets. When using VIs only as inputs, the simulated yield based on the RF model was mainly around 9 t ha

^{−1}. The variation of simulated yield was small, especially in the validation dataset (Figure 5). When using phenology data as inputs, the distribution of simulated yield was similar to that of the RF (VIs + phenoloy) model. The range of simulated yield in the RF (VIs + phenoloy) model was closer to the range of the measured yield. However, all the RF models underestimated yield at the high yield level, to a certain extent.

^{2}= 0.06, RMSE = 0.65 t ha

^{−1}, and MAE = 0.51 t ha

^{−1}, respectively. In contrast, the RF model that used only phenological data exhibited good yield estimations with R

^{2}values of 0.62 and 0.46 in the calibration and validation sets, respectively, which indicated that the phenological information across rice cultivars was an important factor in determining yield in rice breeding. When combined RGB–VIs and phenological data as inputs, the RF model (VIs + phenology) achieved the highest performance in rice yield estimation in the calibration and validation sets; R

^{2}, RMSE, and MAE were 0.70 and 0.53, 0.48 and 0.43 t ha

^{−1}, and 0.38 and 0.34 t ha

^{−1}, respectively (Table 6). These results indicated that more variables in the RF model provided more accurate yield estimation. However, the RF (VIs + phenology) model in the all datasets underestimated the yield when the measured yields were greater than 10 t ha

^{−1}(Figure 6). Additionally, the optimal RF model overestimated yield when the measured yields were less than 7 t ha

^{−1}(Figure 6).

#### 3.4. CERES-Rice Model for Yield Estimation

^{2}, RMSE, and MAE values of 0.75 and 0.80, 0.37 and 0.48 t ha

^{−1}, and 0.31 and 0.42 t ha

^{−1}in calibration set and validation set, respectively. The accuracy of CERES-Rice model for yield estimation was slightly better to that of deep convolutional neural networks for rice yield estimation with R

^{2}and RMSE values of 0.59 and 0.66 t ha

^{−1}, respectively [27]. These results indicated that the yield from the CERES-Rice model provided relatively accurate estimations.

#### 3.5. Performance Comparison between CERES-Rice Model and the Optimal RF Model

^{2}values of the linear regression between simulated yield, based on the CERES-Rice and measured yield, were closer to 1. However, the values of RMSE and MAE, based on the optimal RF model, were comparable to those based on the CERES-Rice model (Table 7). In addition, the paired-sample t-tests results showed that there were no significant differences between the CERES-Rice model and the optimal RF model for simulated yields in 2019 (p > 0.05, Table 7).

## 4. Discussion

#### 4.1. Response of Phenology to Yield

#### 4.2. Importance of Phenology to RF Model Formulation

#### 4.3. Comparison between RF and CERES-Rice Models

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

## Appendix A

Year | Plots | Cultivars | Initial Heading Date | Full Heading Date | Maturity Date |
---|---|---|---|---|---|

2017 | 366 | 122 | August 25–September 15 | August 31–September 19 | October 8–December 2 |

2018 | 381 | 127 | August 19–September 5 | August 23–September 9 | October 5–October 17 |

2019 | 315 | 105 | August 15–September 3 | August 20–September 8 | October 1–October 28 |

Descriptive Statistics | Duration length (d) | Growth Degree Days (°C) | ||||||||
---|---|---|---|---|---|---|---|---|---|---|

ST | TI | IH | HM | SM | GDDST | GDDTI | GDDIH | GDDHM | GDDSM | |

Min | 19 | 47 | 3 | 34 | 117 | 358 | 990 | 59 | 412 | 2061 |

Max | 32 | 68 | 7 | 56 | 148 | 521 | 1412 | 140 | 720 | 2329 |

Mean | 24.03 | 58.99 | 4.55 | 42.83 | 130.40 | 419.64 | 1211.13 | 97.10 | 560.11 | 2233 |

SD | 5.76 | 3.91 | 0.55 | 3.44 | 7.08 | 72.55 | 77.03 | 17.69 | 62.45 | 35.86 |

CV (%) | 23.96 | 6.63 | 12.16 | 8.02 | 5.43 | 17.29 | 6.36 | 18.22 | 11.15 | 1.61 |

Cultivar Name | P1 | P2R | P5 | P2O | G1 | G2 | G3 | G4 |
---|---|---|---|---|---|---|---|---|

5960You058 | 665 | 285.7 | 518.1 | 12.72 | 51.81 | 0.026 | 1.187 | 0.954 |

CLY343 | 706.2 | 190.6 | 445.2 | 11.96 | 51.32 | 0.022 | 1.231 | 0.991 |

R534 | 591.1 | 169 | 491.2 | 11.11 | 51.73 | 0.022 | 1.079 | 1.001 |

HYY605 | 612.9 | 221.6 | 491.7 | 12.78 | 67.41 | 0.025 | 1.134 | 0.943 |

HLY2035 | 275 | 278.2 | 422.7 | 11.27 | 55.6 | 0.021 | 1.265 | 0.8 |

HLY3748 | 288.6 | 227.1 | 483.2 | 11.44 | 66.58 | 0.02 | 1.245 | 0.878 |

HLY5035 | 565.7 | 249.5 | 432.4 | 12.09 | 58.46 | 0.022 | 1.239 | 1.019 |

HLY7155 | 487.7 | 243.4 | 531.7 | 13.11 | 66.84 | 0.025 | 0.793 | 0.988 |

HLY8210 | 418.9 | 259.5 | 483.3 | 13.57 | 73.89 | 0.025 | 0.729 | 1.059 |

JLY1678 | 395.4 | 95.5 | 689.1 | 9.62 | 71.17 | 0.025 | 0.866 | 1.032 |

JLY2626 | 395.4 | 75.1 | 454 | 7.544 | 66.38 | 0.024 | 1.257 | 1.043 |

JLY9936 | 395.4 | 182.4 | 643.1 | 11.91 | 59.47 | 0.03 | 0.851 | 0.974 |

LFY905 | 601.3 | 98.76 | 465.1 | 10.33 | 60.05 | 0.022 | 1.106 | 1.057 |

LJY8246 | 555.8 | 130 | 409.9 | 10.05 | 50.04 | 0.024 | 0.85 | 1.006 |

LLY3748 | 395.4 | 283.7 | 461.8 | 12.35 | 61.62 | 0.024 | 0.841 | 1.039 |

LLY5809 | 395.4 | 76.56 | 450 | 7.133 | 59.89 | 0.025 | 1.263 | 1.077 |

RLY1019 | 417.5 | 142.8 | 520 | 10.5 | 55.55 | 0.03 | 1.097 | 0.88 |

WLY6018 | 395.4 | 60 | 472.7 | 3 | 71.52 | 0.025 | 1.196 | 0.957 |

XLY7629 | 395.4 | 86.07 | 506.5 | 9.049 | 52.11 | 0.03 | 0.955 | 1.045 |

XLY8736 | 395.4 | 330 | 550 | 12.94 | 61.46 | 0.03 | 1.244 | 0.907 |

YLY526 | 601.3 | 193.2 | 450 | 11.52 | 55.44 | 0.024 | 0.966 | 0.898 |

**Table A4.**The mean daily temperature (${\mathrm{T}}_{\mathrm{avg}},\mathbb{C}$), minimum temperature (${\mathrm{T}}_{\mathrm{min}},\mathbb{C}$) and maximum temperature (${\mathrm{T}}_{\mathrm{max}},\mathbb{C}$) during different growth stages from 2017 to 2019.

Year | ST | TI | IH | HM | SM | ||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

${\mathbf{T}}_{\mathbf{m}\mathbf{i}\mathbf{n}}$ | ${\mathbf{T}}_{\mathbf{m}\mathbf{a}\mathbf{x}}$ | ${\mathbf{T}}_{\mathbf{a}\mathbf{v}\mathbf{g}}$ | ${\mathbf{T}}_{\mathbf{m}\mathbf{i}\mathbf{n}}$ | ${\mathbf{T}}_{\mathbf{m}\mathbf{a}\mathbf{x}}$ | ${\mathbf{T}}_{\mathbf{a}\mathbf{v}\mathbf{g}}$ | ${\mathbf{T}}_{\mathbf{m}\mathbf{i}\mathbf{n}}$ | ${\mathbf{T}}_{\mathbf{m}\mathbf{a}\mathbf{x}}$ | ${\mathbf{T}}_{\mathbf{a}\mathbf{v}\mathbf{g}}$ | ${\mathbf{T}}_{\mathbf{m}\mathbf{i}\mathbf{n}}$ | ${\mathbf{T}}_{\mathbf{m}\mathbf{a}\mathbf{x}}$ | ${\mathbf{T}}_{\mathbf{a}\mathbf{v}\mathbf{g}}$ | ${\mathbf{T}}_{\mathbf{m}\mathbf{i}\mathbf{n}}$ | ${\mathbf{T}}_{\mathbf{m}\mathbf{a}\mathbf{x}}$ | ${\mathbf{T}}_{\mathbf{a}\mathbf{v}\mathbf{g}}$ | |

2017 | 23.4 | 29.2 | 26.3 | 26.6 | 33.8 | 30.2 | 22.1 | 26.8 | 24.5 | 19.3 | 25.7 | 22.5 | 23.4 | 30.0 | 26.7 |

2018 | 24.8 | 32.7 | 28.8 | 26.7 | 34.5 | 30.6 | 25.4 | 30.7 | 28.0 | 20.6 | 28.0 | 24.3 | 24.3 | 32 | 28.1 |

2019 | 23.0 | 30.1 | 26.5 | 26.4 | 34.4 | 30.4 | 25.2 | 30.8 | 28.0 | 20.4 | 27 | 23.7 | 23.7 | 31.1 | 27.4 |

**Figure A1.**Vegetation indices vs. measured yield; (

**a**) ExG and yield; (

**b**) NRI and yield. These two vegetation indices were Figure 4a.

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**Figure 2.**The relationship between measured yield and GDL for all cultivars in 2017 (366 plots and 122 cultivars), 2018 (381 plots and 127 cultivars), and 2019 (315 plots and 105 cultivars).

**Figure 3.**The relationship between measured yield and GDL for 21 replicated cultivars with three replicates across the years.

**Figure 4.**Pearson correlation coefficients between (

**a**) VIs and yield, (

**b**) phenology and yield, and (

**c**) VIs and GDL for all cultivars across the years. The numbers in panels (

**a**–

**c**) represent the Pearson correlation coefficients. Note: * F-test statistical significance at the 0.05 probability level. ** F-test statistical significance at the 0.01 probability level. n.s. refers to no statistical significance.

**Figure 5.**The distribution of measured yield and simulated yield of three RF models in calibration (747 plots in 2017 and 2018) and validation (315 plots in 2019) sets.

**Figure 6.**Measured vs. simulated yield from the RF (VIs + phenology) model in calibration set (

**a**, 747 plots in 2017 and 2018) and validation set (

**b**, 315 plots in 2019).

**Figure 7.**Measured vs. simulated yield from CERES-Rice model for 21 replicated cultivars in the calibration set (

**a**, in 2017 and 2018) and validation set (

**b**, in 2019). The horizontal error bar refers to ± one standard deviation associated with each mean in the measured yield distributions. The error bar in the CERES-Rice model was not presented here. Due to the same input data and cultivar parameters in the same cultivar with three replicates, the estimated yields obtained from the CERES-Rice model for three replicates were consistent.

**Figure 8.**Measured vs. simulated yield from the CERES-Rice model and the optimal RF model for 21 cultivars in 2019. The dots represent the mean values of 21 cultivars with three replicates. The horizontal and vertical error bars refer to ± one standard deviation associated with each mean, respectively, in the measured yield distributions and prediction distributions of the optimal RF model for each cultivar. The error bar in the CERES-Rice model was not presented here.

**Figure 10.**Yield maps in rice breeding from 2017 to 2019: (

**a**–

**c**) measured yield and (

**d**–

**f**) simulated yield from the optimal RF model.

Year | Plots | Cultivars | Sowing Date | Transplanting Date |
---|---|---|---|---|

2017 | 366 | 122 | June 7–10 | July 9 |

2018 | 381 | 127 | June 10 | June 29 |

2019 | 315 | 105 | June 6 | June 27 |

Vegetation Index or Band | Formula | Reference |
---|---|---|

R band of UAV image (R) | DN values of R band | -- |

G band of UAV image (G) | DN values of G band | -- |

B band of UAV image (B) | DN values of B band | -- |

Normalized red index (NRI) | R/(R + G + B) | [41] |

Normalized green index (NGI) | G/(R + G + B) | [41] |

Normalized blue index (NBI) | B/(R + G + B) | [41] |

Normalized excess green index (E × G) | (2G − R − B)/(G + R + B) | [42] |

Normalized excess red index (E × R) | (1.4R − G)/(G + R + B) | [43] |

Green–red ratio index (G/R) | G/R | [44] |

Green–blue ratio index (G/B) | G/B | [44] |

Red–blue ratio index (R/B) | R/B | [44] |

Green minus red index (GMR) | G − R | [45] |

Color intensity index (INT) | (R + G + B)/3 | [46] |

Green and red index (VIgreen) | (G − R)/(G + R) | [47] |

Parameter | Range | Interval | Model | ||
---|---|---|---|---|---|

RF (VIs) | RF (Phenology) | RF (Vis + Phenology) | |||

max_depth | 2–8 | 1 | 2 | 3 | 4 |

min_samples_split | 2–14 | 2 | 12 | 8 | 12 |

min_samples_leaf | 2–16 | 2 | 8 | 10 | 4 |

Layer (cm) | Clay (%) | Silt (%) | Organic Carbon (%) | Cation Exchange Capacity (cmol kg ^{−1}) | Total Nitrogen (%) |
---|---|---|---|---|---|

0–20 | 26.0 | 28.6 | 2.1 | 14.5 | 0.18 |

20–40 | 25.1 | 27.1 | 2.1 | 16.9 | 0.20 |

40–60 | 23.0 | 25.7 | 1.7 | 17.1 | 0.17 |

60–80 | 23.0 | 27.9 | 1.5 | 17.2 | 0.19 |

80–100 | 24.5 | 28.0 | 1.6 | 14.6 | 0.16 |

**Table 5.**Descriptive statistics of measured yield (t ha

^{−1}) and GDL for all cultivars and 21 replicated cultivars across the years.

Statistical Indicator | Data Set | Year | Cultivars | Descriptive Statistics | ||||
---|---|---|---|---|---|---|---|---|

Minimum | Maximum | Mean | SD | CV (%) | ||||

Yield | All cultivars | 2017 | 122 | 5.67 | 9.62 | 8.01 | 0.84 | 10.51 |

2018 | 127 | 7.61 | 11.01 | 8.99 | 0.60 | 6.7 | ||

2019 | 105 | 6.79 | 10.67 | 8.72 | 0.64 | 7.3 | ||

Replicated cultivars | 2017 | 21 | 7.08 | 9.51 | 8.84 | 0.64 | 7.22 | |

2018 | 21 | 7.82 | 9.93 | 8.93 | 0.58 | 6.46 | ||

2019 | 21 | 7.70 | 9.74 | 8.82 | 0.55 | 6.28 | ||

GDL | All cultivars | 2017 | 122 | 123 | 148 | 137 | 5.02 | 3.68 |

2018 | 127 | 117 | 130 | 124 | 2.1 | 1.71 | ||

2019 | 105 | 117 | 144 | 132 | 4.6 | 3.47 | ||

Replicated cultivars | 2017 | 21 | 125 | 136 | 129 | 2.59 | 2.01 | |

2018 | 21 | 120 | 130 | 125 | 2.72 | 2.17 | ||

2019 | 21 | 119 | 134 | 127 | 3.65 | 2.86 |

**Table 6.**Statistics of RF models for yield estimation in calibration (747 plots in 2017 and 2018) and validation (315 plots in 2019) sets.

Model | Calibration Set | Validation Set | ||||
---|---|---|---|---|---|---|

R^{2} | RMSE (t ha^{−1}) | MAE (t ha^{−1}) | R^{2} | RMSE (t ha^{−1}) | MAE (t ha^{−1}) | |

RF (VIs) | 0.52 | 0.61 | 0.49 | 0.06 | 0.65 | 0.51 |

RF (phenology) | 0.62 | 0.54 | 0.43 | 0.46 | 0.51 | 0.39 |

RF (VIs + phenology) | 0.70 | 0.48 | 0.38 | 0.53 | 0.43 | 0.34 |

**Table 7.**Paired-samples t-tests for difference comparisons between the simulated values of the CERES-Rice model and the optimal RF model for 21 cultivars in 2019.

Model | Cultivars | Statistical Indicators | Mean Paired Differences (t ha ^{−1}) | Significance (p Value) | ||
---|---|---|---|---|---|---|

R^{2} | RMSE (t ha^{−1}) | MAE (t ha^{−1}) | ||||

RF (VIs + phenology) | 21 | 0.51 | 0.44 | 0.38 | −0.07 | 0.69 |

CERES-Rice | 21 | 0.80 | 0.48 | 0.42 |

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## Share and Cite

**MDPI and ACS Style**

Ge, H.; Ma, F.; Li, Z.; Du, C.
Grain Yield Estimation in Rice Breeding Using Phenological Data and Vegetation Indices Derived from UAV Images. *Agronomy* **2021**, *11*, 2439.
https://doi.org/10.3390/agronomy11122439

**AMA Style**

Ge H, Ma F, Li Z, Du C.
Grain Yield Estimation in Rice Breeding Using Phenological Data and Vegetation Indices Derived from UAV Images. *Agronomy*. 2021; 11(12):2439.
https://doi.org/10.3390/agronomy11122439

**Chicago/Turabian Style**

Ge, Haixiao, Fei Ma, Zhenwang Li, and Changwen Du.
2021. "Grain Yield Estimation in Rice Breeding Using Phenological Data and Vegetation Indices Derived from UAV Images" *Agronomy* 11, no. 12: 2439.
https://doi.org/10.3390/agronomy11122439