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

doi:10.12133/j.smartag.SA202502015

Abstract

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

CLC Number:

TP181

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.

Figures/Tables 24

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Table 2

Results of ADF tested of data preprocessing study

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

Table 2

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References 31

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

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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