RIME2-VMD-LSTM： 基于改进VMD-LSTM的作物冠层温度动态预测模型

doi:10.12133/j.smartag.SA202502015

摘要/Abstract

摘要：

［目的/意义］ 准确预测作物冠层温度，有助于综合衡量作物生长状况、指导农业生产。本研究以猕猴桃和葡萄为研究对象，解决作物冠层温度预测的准确性问题。 ［方法］ 构建一种基于长短期记忆（Long Short-Term Memory， LSTM）、变分模态分解（Variational Mode Decomposition，VMD）和雾凇优化算法（Rime Ice Morphology-based Optimization Algorithm，RIME）的作物冠层温度动态预测模型RIME-VMD-RIME-LSTM（即RIME²-VMD-LSTM）。首先，通过悬挂于滑索上的园区巡检机器人采集作物冠层温度数据。其次，通过多组预测试验的性能表现，选定VMD-LSTM作为基模型，同时为减小VMD不同频率分量之间交叉干扰，运用K-means聚类算法对各分量样本熵进行聚类，重构为新分量。最后，利用RIME优化算法对VMD和LSTM的参数进行优化，提升模型的预测精度。 ［结果和讨论］ 本模型在模拟不同噪声环境下的均方根误差（Root Mean Square Error， RMSE）和平均绝对误差（Mean Absolute Error， MAE）均小于对比模型，分别为0.360 1和0.254 3℃，且R²高达0.994 7。 ［结论］ 本研究模型为动态预测作物冠层温度提供了可行的方法，并为园区作物生长状况提供数据支持。

关键词: 冠层温度, 温度预测, LSTM, RIME, VMD

Abstract:

[Objective] Accurate prediction of crop canopy temperature is essential for comprehensively assessing crop growth status and guiding agricultural production. This study focuses on kiwifruit and grapes to address the challenges in accurately predicting crop canopy temperature. [Methods] A dynamic prediction model for crop canopy temperature was developed, based on Long Short-Term Memory (LSTM), Variational Mode Decomposition (VMD), and the Rime Ice Morphology-based Optimization Algorithm (RIME) optimization algorithm, named RIME-VMD-RIME-LSTM (RIME²-VMD-LSTM). Firstly, crop canopy temperature data were collected by an inspection robot suspended on a cableway. Secondly, through the performance of multiple pre-test experiments, VMD-LSTM was selected as the base model. To reduce cross-interference between different frequency components of VMD, the K-means clustering algorithm was applied to cluster the sample entropy of each component, reconstructing them into new components. Finally, the RIME optimization algorithm was utilized to optimize the parameters of VMD and LSTM, enhancing the model's prediction accuracy. [Results and Discussions] The experimental results demonstrated that the proposed model achieved lower Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) (0.360 1 and 0.254 3°C, respectively) in modeling different noise environments than the comparator model. Furthermore, the R² value reached a maximum of 0.994 7. [Conclusions] Therefore, this model provides a feasible method for dynamically predicting crop canopy temperature and offers data support for assessing crop growth status in agricultural parks.

Key words: canopy temperature, temperature prediction, LSTM, RIME, VMD

中图分类号:

TP181

王毓玺, 黄铝文, 段小琳. RIME²-VMD-LSTM：基于改进VMD-LSTM的作物冠层温度动态预测模型[J]. 智慧农业(中英文), doi: 10.12133/j.smartag.SA202502015.

WANG Yuxi, HUANG Lyuwen, DUAN Xiaolin. RIME²-VMD-LSTM: A Dynamic Prediction Model of Crop Canopy Temperature Based on VMD-LSTM[J]. Smart Agriculture, doi: 10.12133/j.smartag.SA202502015.

图/表 24

Inspection parameters	$T$ Inspection results	$P$	1% Threshold	5% Threshold	10% Threshold
Kiwi canopy temperature	-14.401	0.000 0	-3.960	-3.410	-3.120
Grape canopy temperature	-11.700	0.000 0	-3.430	-2.860	-2.570

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Parameter	Specification
Measurement range	-20~80 ℃
Measurement accuracy	±0.5 ℃
Response time	≤1 s
Operating environment	-40~85 ℃， Humidity ≤95%RH
Data storage frequency	Every 10 minutes

Number of neurons/cell	LSTM		EMD-LSTM		CEEMDAN-LSTM
Number of neurons/cell	MSE	MAE	MSE	MAE	MSE	MAE
50	0.479 0	0.535 0	0.215 0	0.303 5	0.216 5	0.280 5
100	0.456 5	0.473 0	0.148 0	0.278 0	0.329 0	0.348 0
150	0.909 5	0.786 5	0.188 5	0.304 0	0.169 5	0.272 0
200	0.327 0	0.389 0	0.249 5	0.310 5	0.215 5	0.302 5
250	0.405 5	0.447 5	0.233 0	0.326 5	0.289 5	0.337 0
Number of neurons/cell	VMD-LSTM		SGMD-LSTM		TVF-EMD-LSTM
Number of neurons/cell	MSE	MAE	MSE	MAE	MSE	MAE
50	0.101 5	0.192 5	0.185 0	0.262 5	0.178 5	0.242 5
100	0.095 0	0.195 0	0.464 0	0.380 0	0.186 5	0.261 5
150	0.100 5	0.203 0	0.675 5	0.432 5	0.175 0	0.252 5
200	0.127 0	0.213 0	0.409 5	0.398 0	0.205 0	0.299 0
250	0.114 5	0.225 0	0.261 5	0.335 0	0.209 5	0.299 5

Number of neurons/cell	LSTM		EMD-LSTM		CEEMDAN-LSTM
Number of neurons/cell	MSE	MAE	MSE	MAE	MSE	MAE
50	0.563 0	0.548 5	0.336 5	0.402 5	0.318 0	0.325 0
100	0.414 0	0.427 5	0.270 0	0.368 5	0.487 0	0.481 0
150	0.568 0	0.529 0	0.481 0	0.495 5	0.367 0	0.427 5
200	0.323 5	0.405 5	0.380 5	0.477 0	0.421 0	0.425 0
250	0.502 0	0.477 5	0.601 5	0.510 5	0.380 0	0.413 0
Number of neurons/cell	VMD-LSTM		SGMD-LSTM		TVF-EMD-LSTM
Number of neurons/cell	MSE	MAE	MSE	MAE	MSE	MAE
50	0.273 5	0.325 5	0.282 5	0.369 5	0.416 5	0.415 5
100	0.282 0	0.390 5	0.437 0	0.475 5	0.348 0	0.440 5
150	0.257 0	0.320 0	0.930 5	0.604 0	0.273 5	0.370 0
200	0.250 0	0.326 5	0.713 5	0.543 5	0.267 0	0.357 0
250	0.278 0	0.325 0	0.662 5	0.524 5	0.249 0	0.315 5

Temp	comparison models	Next 1 hour		Next 2 hours		Next 3 hours		Average
Temp	comparison models	RMSE	MAE	RMSE	MAE	RMSE	MAE	RMSE	MAE
Kiwi Canopy	VMD-PSO-LSTM	1.498 0	0.943 8	2.958 7	1.818 0	4.251 3	2.604 3	2.902 7	1.788 7
	VMD-SSA-LSTM	1.478 2	0.903 2	2.912 4	1.756 9	4.170 3	2.502 7	2.853 6	1.720 9
	VMD-GWO-LSTM	1.481 3	0.915 8	2.897 2	2.302 2	4.186 4	2.549 6	2.855 0	1.922 5
	VMD-SAO-LSTM	1.504 5	0.919 1	3.041 4	1.850 3	4.397 6	2.680 5	2.981 2	1.816 6
	VMD-RIME-LSTM	1.465 8	0.900 7	2.873 9	1.732 1	4.196 3	2.522 8	2.845 3	1.718 5
Grapes canopy	VMD-PSO-LSTM	1.371 2	0.882 4	2.620 0	1.770 5	3.685 6	2.581 5	2.558 9	1.744 8
	VMD-SSA-LSTM	1.401 1	0.879 7	2.580 0	1.774 3	3.667 4	2.570 0	2.549 5	1.741 3
	VMD-GWO-LSTM	1.418 7	0.902 8	2.664 6	1.756 5	3.744 4	2.514 0	2.609 2	1.724 4
	VMD-SAO-LSTM	1.357 5	0.876 6	2.564 9	1.733 8	3.632 2	2.526 8	2.518 2	1.712 4
	VMD-RIME-LSTM	1.378 6	0.897 3	2.611 8	1.731 0	3.635 7	2.459 3	2.542 0	1.695 9

Temp	Comparison models	Next 1 hour			Next 2 hours			Next 3 hours
Temp	Comparison models	RMSE/℃	MAE/℃	R ²	RMSE/℃	MAE/℃	R ²	RMSE/℃	MAE/℃	R ²
Kiwi canopy	LSTM	1.926 3	1.195 8	0.829 5	2.819 4	1.871 4	0.731 9	4.717 0	2.564 3	0.412 1
	VMD-LSTM	1.734 1	1.200 3	0.876 5	2.805 2	1.760 6	0.769 4	4.437 4	2.976 8	0.491 2
	RIME-LSTM	1.933 3	1.234 3	0.849 1	2.842 6	1.930 9	0.890 1	4.196 3	2.522 8	0.477 7
	RIME-VMD-LSTM	1.708 5	1.174 5	0.879 8	3.205 2	2.125 6	0.756 9	4.548 9	3.005 0	0.347 7
	VMD-RIME-LSTM	1.465 8	0.937 9	0.911 7	2.912 4	1.756 9	0.761 6	4.096 6	2.613 6	0.509 6
	Proposed model	1.446 6	0.900 7	0.963 9	1.734 1	2.116 0	0.906 3	3.678 9	2.547 1	0.655 9
Grape canopy	LSTM	1.860 6	1.259 0	0.841 7	2.997 6	2.099 4	0.648 7	4.038 2	2.926 3	0.425 3
	VMD-LSTM	1.610 8	1.194 9	0.923 2	2.734 0	1.936 2	0.663 5	3.753 1	2.611 0	0.465 9
	RIME-LSTM	1.810 8	1.250 5	0.884 8	2.772 0	1.837 6	0.653 0	3.937 5	2.666 9	0.299 9
	RIME-VMD-LSTM	1.574 5	1.125 0	0.888 1	2.686 4	1.852 2	0.674 1	3.731 2	2.598 4	0.371 4
	VMD-RIME-LSTM	1.465 8	0.924 6	0.914 4	2.611 8	1.731 0	0.692 9	4.196 3	2.522 8	0.476 7
	Proposed model	1.378 6	0.897 3	0.973 0	1.810 8	1.250 5	0.914 8	1.810 8	1.250 5	0.644 8

RIME²-VMD-LSTM：基于改进VMD-LSTM的作物冠层温度动态预测模型

RIME²-VMD-LSTM: A Dynamic Prediction Model of Crop Canopy Temperature Based on VMD-LSTM

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图/表 24

参考文献 31

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Metrics

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Models	Original data			Uniform noise			Gaussian noise
Models	RMSE/℃	MAE/℃	R ²	RMSE/℃	MAE/℃	R ²	RMSE/℃	MAE/℃	R ²
CNN	0.497 2	0.316 2	0.990 0	3.347 3	2.794 1	0.657 6	5.548 9	4.416 5	0.372 4
GRU	0.946 0	0.586 6	0.963 7	3.669 9	3.017 0	0.588 4	7.056 5	5.617 2	-0.015 0
LSTM	0.529 8	0.457 0	0.978 5	13.896 2	3.034 6	0.575 3	6.320 2	5.138 7	0.237 4
ConvLSTM	1.150 7	1.521 4	0.798 0	4.003 4	3.246 3	0.510 2	7.170 9	5.696 1	-0.048 1
PC-LSTM	0.779 1	0.601 0	0.975 4	4.050 9	3.271 2	0.498 5	7.592 3	6.008 1	-0.174 9
Proposed model	0.360 1	0.254 3	0.994 7	1.501 0	1.214 2	0.929 7	3.885 5	3.080 7	0.678 9

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Comparison models	Next 1 hour			Next 2 hours			Next 3 hours
Comparison models	RMSE	MAE	R ²	RMSE	MAE	R ²	RMSE	MAE	R ²
LSTM	1.893 5	1.227 4	0.835 6	2.908 5	1.985 4	0.690 3	4.377 6	2.745 3	0.418 7
VMD-LSTM	1.672 4	1.197 6	0.899 8	2.769 6	1.848 4	0.716 5	4.095 2	2.793 9	0.478 6
RIME-LSTM	1.872 1	1.242 4	0.867 0	2.807 3	1.884 2	0.771 6	4.066 9	2.594 8	0.388 8
RIME-VMD-LSTM	1.641 5	1.149 8	0.883 9	2.945 8	1.988 9	0.715 5	4.140 0	2.801 7	0.359 5
VMD-RIME-LSTM	1.465 8	0.931 3	0.913 1	2.762 1	1.744 0	0.727 3	4.146 4	2.568 2	0.493 2
Proposed model	1.412 6	0.899 0	0.968 4	1.772 4	1.683 2	0.910 5	2.744 9	1.898 8	0.650 4

Comparison models	Next 1 hour			Next 2 hours			Next 3 hours
Comparison models	RMSE	MAE	R ²	RMSE	MAE	R ²	RMSE	MAE	R ²
LSTM	-25.39	-26.75	15.90	-39.06	-15.22	31.91	-37.30	-30.84	55.33
VMD-LSTM	-15.53	-24.93	7.62	-36.00	-8.93	27.09	-32.97	-32.04	35.90
RIME-LSTM	-24.54	-27.64	11.71	-36.86	-10.67	18.01	-32.51	-26.82	67.28
RIME-VMD-LSTM	-13.94	-21.81	9.56	-39.83	-15.37	27.26	-33.70	-32.23	80.89
VMD-RIME-LSTM	-3.63	-3.46	6.06	-35.83	-3.48	25.20	-33.80	-26.07	31.87

Models	Original data			Uniform noise			Gaussian noise
Models	RMSE/℃	MAE/℃	R ²	RMSE/℃	MAE/℃	R ²	RMSE/℃	MAE/℃	R ²
CNN	0.525 4	0.344 4	0.987 8	3.999 9	3.230 0	0.486 5	5.860 3	4.663 9	0.297 0
GRU	1.092 1	0.730 6	0.947 2	4.221 0	3.409 7	0.428 2	7.484 9	6.001 6	-0.146 8
LSTM	3.321 1	1.029 0	0.853 1	18.878 4	3.516 5	0.394 1	54.543 2	5.919 4	-0.116 5
ConvLSTM	1.646 0	1.145 8	0.880 2	6.559 2	5.034 7	-0.380 9	7.622 9	6.137 6	-0.189 5
PC-LSTM	0.889 3	0.669 8	0.965 0	4.778 1	3.864 1	0.267 3	8.596 2	6.878 1	-0.512 7
Proposed model	0.287 0	0.185 7	0.996 3	1.029 4	0.817 9	0.961 3	2.065 0	1.656 8	0.903 4