Assessing the Capabilities of UV-NIR Spectroscopy for Predicting Macronutrients in Hydroponic Solutions with Single-Task and Multi-Task Learning
Abstract
:1. Introduction
2. Materials and Methods
2.1. Sample Preparation
2.2. Spectral Collection and Preprocessing
2.3. Learning Algorithms
2.4. Modelling and Evaluation
3. Results and Discussion
3.1. Characteristics of the Content of Nutrients
3.2. Spectral Response
3.3. Model Performance
3.3.1. Prediction Results
3.3.2. Comparison of Different Algorithms
3.3.3. Interpretation of the Prediction Mechanism
3.3.4. Comparison with Previous Studies
4. Conclusions
- N and Ca could be predicted with good accuracy (RPD > 2), K could be predicted with moderate accuracy (1.4 < RPD < 2), and P, Mg, and S could not be successfully predicted (RPD < 1.4);
- Significant spectral absorptions mainly caused by N could be found around 230 nm and 302 nm, and regression features were thereby generated. Other macronutrients did not show any obvious absorption characteristic along UV-NIR, but K and Ca have significant shared features with N;
- Multi-task algorithms usually showed stronger learning ability compared to single-task algorithms, especially with RMTL, which could improve prediction performance for relevant tasks, namely predicting K and Ca and identifying the irrelevant (outlier) tasks—predicting P, Mg, and S.
Supplementary Materials
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Mother Solution | Chemical Reagents | Application Amounts (mg/L) | ||||
---|---|---|---|---|---|---|
Basic NF1/ Tomato | Basic NF2/ Sweet Potato | Basic NF3/ Lettuce | Basic NF4/ Grape | Basic NF5/ Watermelon | ||
A | KNO3 | 500 | 750 | 600 | 404 | 238 |
MgSO4·7H2O | 300 | 450 | 433 | 246 | 500 | |
NH4H2PO4 | 50 | 75 | 133 | - | - | |
KH2PO4 | 150 | 225 | - | 136 | 185 | |
B | Ca(NO3)2·4H2O | 900 | 2700 | 900 | 590 | 910 |
Units | Mean | STD | Min | Median | Max | |
---|---|---|---|---|---|---|
pH | 6.96 | 0.31 | 6.17 | 6.95 | 7.59 | |
EC | µS/cm | 288.95 | 149.66 | 72.09 | 257.90 | 864.00 |
N | mg/L | 111.81 | 80.89 | 12.54 | 91.91 | 433.43 |
P | mg/L | 24.08 | 15.84 | 0.46 | 22.11 | 71.50 |
K | mg/L | 122.00 | 74.00 | 2.70 | 115.82 | 354.13 |
Ca | mg/L | 103.77 | 86.20 | 3.05 | 86.95 | 457.63 |
Mg | mg/L | 20.86 | 14.03 | 0.17 | 18.13 | 64.75 |
S | mg/L | 24.19 | 14.47 | 0.23 | 22.85 | 57.55 |
pH | EC | N | P | K | Ca | Mg | S | |
---|---|---|---|---|---|---|---|---|
pH | 1.00 | |||||||
EC | −0.72 | 1.00 | ||||||
N | −0.66 | 0.99 | 1.00 | |||||
P | −0.85 | 0.66 | 0.61 | 1.00 | ||||
K | −0.67 | 0.88 | 0.86 | 0.65 | 1.00 | |||
Ca | −0.64 | 0.95 | 0.97 | 0.60 | 0.73 | 1.00 | ||
Mg | −0.61 | 0.76 | 0.70 | 0.62 | 0.56 | 0.71 | 1.00 | |
S | −0.52 | 0.57 | 0.48 | 0.45 | 0.40 | 0.47 | 0.90 | 1.00 |
Algorithm | Macronutrient | Parameter 1 | N 2 | Calibration | Validation | Accuracy Category | |||
---|---|---|---|---|---|---|---|---|---|
RPD | SSR/SST | RPD | SSR/SST | ||||||
PLS | N | 5 | 898 | 11.16 | 0.99 | 9.46 | 0.91 | A | |
P | 2 | 898 | 1.60 | 0.61 | 1.04 | 0.41 | C | ||
K | 4 | 898 | 3.16 | 0.90 | 1.70 | 0.87 | B | ||
Ca | 5 | 898 | 6.94 | 0.98 | 3.93 | 0.86 | A | ||
Mg | 2 | 898 | 1.46 | 0.52 | 1.08 | 0.38 | C | ||
S | 2 | 898 | 1.35 | 0.44 | 0.91 | 0.59 | C | ||
LASSO | N | 0.03 | 12 | 9.62 | 0.93 | 10.12 | 0.90 | A | |
P | 0.08 | 21 | 2.38 | 0.59 | 1.03 | 0.35 | C | ||
K | 0.09 | 18 | 2.98 | 0.70 | 1.66 | 0.68 | B | ||
Ca | 0.03 | 25 | 6.33 | 0.90 | 3.27 | 0.72 | A | ||
Mg | 0.2 | 10 | 1.57 | 0.33 | 1.32 | 0.19 | C | ||
S | 0.16 | 13 | 1.58 | 0.35 | 0.96 | 0.30 | C | ||
DMTL | N | 29 | 7 | 31 | 8.28 | 0.90 | 7.50 | 0.84 | A |
P | 51 | 2.90 | 0.69 | 1.04 | 0.40 | C | |||
K | 49 | 4.02 | 0.80 | 1.68 | 0.82 | B | |||
Ca | 44 | 4.82 | 0.85 | 3.43 | 0.69 | A | |||
Mg | 46 | 2.84 | 0.67 | 1.15 | 0.45 | C | |||
S | 59 | 2.80 | 0.64 | 0.95 | 0.55 | C | |||
RMTL | N | 5 | 21 | 898 | 51.06 | 0.99 | 8.58 | 0.91 | A |
P | 898 | 40.49 | 0.99 | 1.07 | 0.49 | C | |||
K | 898 | 37.65 | 0.98 | 1.73 | 0.93 | B | |||
Ca | 898 | 44.03 | 0.99 | 4.35 | 0.81 | A | |||
Mg | 898 | 38.76 | 1.00 | 1.28 | 0.44 | C | |||
S | 898 | 32.09 | 1.03 | 1.09 | 0.58 | C |
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Qi, H.; Li, B.; Nie, J.; Luo, Y.; Yuan, Y.; Zhou, X. Assessing the Capabilities of UV-NIR Spectroscopy for Predicting Macronutrients in Hydroponic Solutions with Single-Task and Multi-Task Learning. Agronomy 2024, 14, 1974. https://doi.org/10.3390/agronomy14091974
Qi H, Li B, Nie J, Luo Y, Yuan Y, Zhou X. Assessing the Capabilities of UV-NIR Spectroscopy for Predicting Macronutrients in Hydroponic Solutions with Single-Task and Multi-Task Learning. Agronomy. 2024; 14(9):1974. https://doi.org/10.3390/agronomy14091974
Chicago/Turabian StyleQi, Haijun, Bin Li, Jun Nie, Yizhi Luo, Yu Yuan, and Xingxing Zhou. 2024. "Assessing the Capabilities of UV-NIR Spectroscopy for Predicting Macronutrients in Hydroponic Solutions with Single-Task and Multi-Task Learning" Agronomy 14, no. 9: 1974. https://doi.org/10.3390/agronomy14091974