# Prediction of Rice Yield in East China Based on Climate and Agronomic Traits Data Using Artificial Neural Networks and Partial Least Squares Regression

^{1}

^{2}

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## Abstract

**:**

^{2}) and root mean square error (RMSE) were 0.611 vs. 0.374 and 0.578 vs. 0.865 ton/ha using climate data, respectively; and 0.742 vs. 0.689 and 0.556 vs. 0.608 using agronomic trait data, respectively. When using fused data the R

^{2}and RMSE improved to 0.843 vs. 0.746 and 0.440 vs. 0.549, respectively. The optimum architecture of the FFBN consisted of one hidden layer with 29 neurons. Therefore, the FFBN algorithm is an effective option for the prediction of rice yield in complex systems of rice production.

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Data Collection

#### 2.2. Feed-Forward Backpropagation Neural Network (FFBN)

_{max}is the maximum value of X, X

_{min}is the minimum value of X.

#### 2.3. Partial Least Squares Regression (PLSR)

#### 2.4. Model Evaluations

^{2}) and the root mean square error (RMSE) were used to evaluate the model performances.

## 3. Results and Discussion

#### 3.1. Climate Data Based Modeling

^{2}was 0.374 for the training dataset and 0.368 for testing dataset, whereas the R

^{2}of the FFBN model was 0.611 and 0.578 for training and testing datasets, respectively. The RMSE of FFBN was therefore smaller than that of the PLSR model. Both models showed a significant relationship between measured and predicted values, indicating that the climate parameters contributed to the rice yield, and better prediction performance of FFBN was observed.

^{2}values were obtained in these predictions, total prediction accuracy was improved in this study.

#### 3.2. Agronomic Trait-Based Modeling

^{2}was 0.707 for the training dataset and 0.689 for the testing dataset; the R

^{2}of the FFBN model was 0.750 and 0.742 for training and testing datasets, respectively; and the RMSE of PLSR and FFBN in the testing set was 0.608 and 0.556 ton/ha, respectively. Both models showed significant relationships between measured and predicted values. In addition, both models, particularly the PLSR model, were significantly improved using the agronomic trait data compared to using the climate data. This indicates a greater contribution was made by agronomic traits to the prediction, and better prediction performance of FFBN was also observed.

#### 3.3. Climate and Agronomic Traits Data Fused Modelling

^{2}was 0.753 for the training dataset and 0.746 for the testing dataset, and the R

^{2}of the FFBN model was 0.867 and 0.843 for the training and testing datasets, respectively; the RMSE of PLSR and FFBN in the testing set was 0.440 and 0.549 ton/ha, respectively. Both models, particularly the FFBN model, were significantly improved using the fused data. In addition, the FFBN model showed better prediction performance, which demonstrated that the nonlinear model performed better than the linear model in a complicated system.

^{2}and RMSE compared to those of other nonlinear ML models.

## 4. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

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**Figure 3.**(

**a**) Performance of partial least squares regression (PLSR) model with optimized PLS factor of 2 for training and testing sets using climate data; (

**b**) performance of feed-forward backpropagation neural network (FFBN) model with optimized number of neurons of 13 in the hidden layer for training and testing sets using climate data.

**Figure 4.**Weight ratio of climate parameters from the PLSR model for training and testing sets using climate data. GST, ground surface temperature (°C); PRS, pressure of the station (hPa); RHU, relative humidity (%); TEM, temperature (°C); WIN, wind speed (m/s); EVP, evaporation (mm); PRE, precipitation (mm); SR, solar radiation (MJ/m

^{2}); SSD, sunshine duration (hour); YIELD, rice yield (ton/ha).

**Figure 5.**(

**a**) Performance of PLSR model with optimized PLS factor of 4 for training and testing sets using agronomic traits data; (

**b**) Performance of FFBN model with optimized neurons of 10 in hidden layer for train and test set using agronomic traits data.

**Figure 6.**Weight ratio from PLSR model for training and testing sets using agronomic trait data. PH, plant height (cm); EPN, effective panicle number (ten thousands/hm

^{2}); FGPP, filled grains per panicle (grains); SSR, seed set rate (%); GP, growth period (day).

**Figure 7.**(

**a**) Performance of the PLSR model with optimized PLS factor of 4 for training and testing sets using fused data; (

**b**) performance of the FFBN model with optimized number of neurons of 29 in the hidden layer for training and testing sets using fused data.

**Figure 8.**Weight ratio from the PLSR model for training and testing sets using fused climate data and agronomic trait data. GST, ground surface temperature (°C); PRS, pressure of the station (hPa); RHU, relative humidity (%); TEM, temperature (°C); WIN, wind speed (m/s); EVP, evaporation (mm); PRE, precipitation (mm); SR, solar radiation (MJ/m

^{2}); SSD, sunshine duration (hour); YIELD, rice yield (ton/ha); PH, plant height (cm); EPN, effective panicle number (ten thousands/hm

^{2}); FGPP, filled grains per panicle (grains); SSR, seed set rate (%); GP, growth period (day).

Variables | Synonym | Min | Max | Mean | Std | |
---|---|---|---|---|---|---|

Agronomic Traits | Plant height (cm) | PH | 75.10 | 143.5 | 106.9 | 14.43 |

Effective panicle number (ten thousands/hm^{2}) | EPN | 160.5 | 441.0 | 276.2 | 49.67 | |

Filled grains per panicle (grains) | FGPP | 73.90 | 273.70 | 143.60 | 43.21 | |

Seed set rate (%) | SSR | 0.66 | 0.95 | 0.84 | 0.05 | |

Growth period (day) | GP | 105.1 | 179.5 | 133.1 | 15.22 | |

ClimateData | Ground surface temperature (°C) | GST | 17.25 | 23.53 | 20.18 | 1.54 |

Pressure of the station (hPa) | PRS | 983.3 | 1015.5 | 999.4 | 9.31 | |

Relative humidity (%) | RHU | 69.32 | 81.35 | 75.56 | 2.87 | |

Temperature (°C) | TEM | 15.24 | 20.40 | 17.71 | 1.40 | |

Wind speed (m/s) | WIN | 1.37 | 3.30 | 2.15 | 0.45 | |

Evaporation (mm) | EVP | 1.63 | 3.60 | 2.53 | 0.38 | |

Precipitation (mm) | PRE | 2.47 | 6.86 | 4.45 | 1.06 | |

Solar radiation (MJ/m^{2}) | SR | 9.83 | 22.03 | 13.71 | 2.26 | |

Sunshine duration (hour) | SSD | 4.04 | 6.20 | 4.84 | 0.49 | |

Rice Yield | Rice yield (ton/ha) | YIELD | 5.07 | 11.30 | 8.48 | 1.11 |

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**MDPI and ACS Style**

Guo, Y.; Xiang, H.; Li, Z.; Ma, F.; Du, C.
Prediction of Rice Yield in East China Based on Climate and Agronomic Traits Data Using Artificial Neural Networks and Partial Least Squares Regression. *Agronomy* **2021**, *11*, 282.
https://doi.org/10.3390/agronomy11020282

**AMA Style**

Guo Y, Xiang H, Li Z, Ma F, Du C.
Prediction of Rice Yield in East China Based on Climate and Agronomic Traits Data Using Artificial Neural Networks and Partial Least Squares Regression. *Agronomy*. 2021; 11(2):282.
https://doi.org/10.3390/agronomy11020282

**Chicago/Turabian Style**

Guo, Yuming, Haitao Xiang, Zhenwang Li, Fei Ma, and Changwen Du.
2021. "Prediction of Rice Yield in East China Based on Climate and Agronomic Traits Data Using Artificial Neural Networks and Partial Least Squares Regression" *Agronomy* 11, no. 2: 282.
https://doi.org/10.3390/agronomy11020282