# Greenhouse Micro-Climate Prediction Based on Fixed Sensor Placements: A Machine Learning Approach

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## Abstract

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## 1. Introduction

## 2. Review of Related Works

## 3. Data Description and Pre-Processing

## 4. Methodology

#### 4.1. Dense Neural Network (DNN)-Based Regression Model

#### 4.2. Evaluation Metrics

## 5. Results and Discussion

#### 5.1. Temperature and Humidity Prediction RMSE

#### 5.2. Correlation Coefficients

#### 5.3. Effect of Number of Fixed Sensor Locations on the Prediction Accuracy of DNN Model

## 6. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

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**Figure 2.**Architecture of proposed deep-neural-network-based multi-channel regression model for efficient micro-climate prediction using fixed sensor locations.

**Figure 3.**Percentage reduction in error corresponding to temperature measurement using different ${N}_{1}$ and ${N}_{2}$ for (

**a**) March, (

**b**) April, (

**c**), May (

**d**), June (

**e**), July, and (

**f**) October.

**Figure 4.**Layout of greenhouse showings fixed sensor locations (green) as well as optimal sensor locations corresponding to the target months of (

**a**) March (blue), and overlapped locations (red) for temperature measurement (

**b**) July (blue), and overlapped locations (red) for temperature measurement (

**c**) March (blue), and overlapped locations (red) for relative humidity measurement (

**d**) July (blue), and overlapped locations (red) for relative humidity measurement.

**Table 1.**Locations of 10 top-ranked sensors corresponding to different months for temperature (T) and relative humidity (RH).

Rank | Optimal Locations | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

February | March | April | May | June | July | October | ||||||||

T | RH | T | RH | T | RH | T | RH | T | RH | T | RH | T | RH | |

1 | E3 | B4 | G1 | D6 | A4 | E4 | D1 | B2 | E7 | E2 | B4 | D3 | A4 | E4 |

2 | F7 | F5 | C7 | G6 | E7 | D2 | B2 | A3 | C7 | E6 | D5 | B6 | E7 | D2 |

3 | D1 | A1 | B6 | C4 | F7 | E3 | F5 | F6 | F6 | B2 | G7 | C3 | F7 | E3 |

4 | D7 | C1 | A3 | A2 | A1 | A2 | C1 | F7 | D7 | F7 | G6 | D6 | A1 | A2 |

5 | E2 | C5 | D1 | D3 | E6 | E6 | D7 | G6 | G1 | H3 | E1 | D7 | E6 | E6 |

6 | C4 | F2 | E2 | E6 | D2 | E5 | A7 | E3 | E1 | G6 | E7 | F5 | D2 | E5 |

7 | H2 | F3 | D2 | E1 | F6 | A4 | C5 | B4 | G7 | H1 | D7 | A3 | F6 | A4 |

8 | E7 | H5 | B3 | F4 | G7 | D3 | F2 | H1 | B2 | B1 | G1 | C2 | G7 | D3 |

9 | G1 | E2 | C1 | B4 | G6 | A5 | F3 | B1 | A2 | A6 | F7 | F4 | G6 | A5 |

10 | E1 | F1 | E1 | A5 | D6 | F2 | A3 | D7 | E3 | E4 | A1 | G3 | D6 | F2 |

Predicted Month | Temperature Data | Humidity Data | ||||
---|---|---|---|---|---|---|

Train | Test | Validate | Train | Test | Validate | |

March | 16,711 | 5223 | 4178 | 16,252 | 5080 | 4064 |

April | 16,283 | 5089 | 4071 | 16,516 | 5162 | 4130 |

May | 16,639 | 5200 | 4160 | 16,343 | 5108 | 4086 |

June | 15,264 | 4771 | 3816 | 15,270 | 4772 | 3818 |

July | 14,954 | 4674 | 3739 | 15,160 | 4738 | 3791 |

October | 15,549 | 4860 | 3888 | 16,031 | 5010 | 4008 |

**Table 3.**Comparison of temperature prediction with and without DNN model in terms of RMSE for different values of ${N}_{1}$ and ${N}_{2}$.

Predicted Month | RMSE | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

${\mathit{N}}_{\mathbf{1}}=\mathbf{1},{\mathit{N}}_{\mathbf{2}}=\mathbf{1}$ | ${\mathit{N}}_{\mathbf{1}}=\mathbf{5},{\mathit{N}}_{\mathbf{2}}=\mathbf{5}$ | ${\mathit{N}}_{\mathbf{1}}=\mathbf{10},{\mathit{N}}_{\mathbf{2}}=\mathbf{10}$ | ${\mathit{N}}_{\mathbf{1}}=\mathbf{15},{\mathit{N}}_{\mathbf{2}}=\mathbf{15}$ | ${\mathit{N}}_{\mathbf{1}}=\mathbf{20},{\mathit{N}}_{\mathbf{2}}=\mathbf{20}$ | |||||||||||

DNN Model | W/O DNN Model | RMSE Reduction (%) | DNN Model | W/O DNN Model | RMSE Reduction (%) | DNN Model | W/O DNN Model | RMSE Reduction (%) | DNN Model | W/O DNN Model | RMSE Reduction (%) | DNN Model | W/O DNN Model | RMSE Reduction (%) | |

March | 3.4313 | 4.486 | 23.5 | 2.2177 | 4.3059 | 48.49 | 1.7744 | 4.295 | 58.69 | 1.7274 | 4.27676 | 59.61 | 1.813 | 4.253 | 57.37 |

April | 3.769 | 4.7207 | 20.16 | 2.8217 | 5.2158 | 45.9 | 2.4798 | 5.0001 | 50.41 | 2.3984 | 5.0265 | 52.28 | 2.2835 | 4.9931 | 54.27 |

May | 4.879 | 10.3591 | 52.9 | 3.6129 | 10.0993 | 64.22 | 2.8061 | 10.051 | 72.08 | 2.9978 | 10.2512 | 70.76 | 2.8256 | 10.3058 | 72.58 |

June | 4.5209 | 16.91844 | 73.28 | 3.4017 | 16.2493 | 79.06 | 2.9317 | 16.4949 | 82.23 | 2.8681 | 16.1764 | 82.26 | 2.8209 | 16.1334 | 82.52 |

July | 3.555 | 13.3485 | 73.37 | 3.0707 | 13.7859 | 77.72 | 2.7632 | 13.7796 | 79.95 | 2.5978 | 13.6444 | 80.96 | 2.528 | 13.6213 | 81.44 |

October | 3.9781 | 7.2066 | 44.8 | 3.2783 | 7.5076 | 56.33 | 2.6372 | 7.5388 | 65.01 | 2.6396 | 7.4724 | 64.68 | 2.4957 | 7.4801 | 66.64 |

**Table 4.**Comparison of relative humidity prediction with and without DNN model in terms of RMSE for different values of ${N}_{1}$ and ${N}_{2}$.

Predicted Month | RMSE | ||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

${\mathit{N}}_{\mathbf{1}}=\mathbf{1},{\mathit{N}}_{\mathbf{2}}=\mathbf{1}$ | ${\mathit{N}}_{\mathbf{1}}=\mathbf{5},{\mathit{N}}_{\mathbf{2}}=\mathbf{5}$ | ${\mathit{N}}_{\mathbf{1}}=\mathbf{10},{\mathit{N}}_{\mathbf{2}}=\mathbf{10}$ | ${\mathit{N}}_{\mathbf{1}}=\mathbf{15},{\mathit{N}}_{\mathbf{2}}=\mathbf{15}$ | ${\mathit{N}}_{\mathbf{1}}=\mathbf{20},{\mathit{N}}_{\mathbf{2}}=\mathbf{20}$ | |||||||||||

DNN Model | W/O DNN Model | RMSE Reduction (%) | DNN Model | W/O DNN Model | RMSE Reduction (%) | DNN Model | W/O DNN Model | RMSE Reduction (%) | DNN Model | W/O DNN Model | RMSE Reduction (%) | DNN Model | W/O DNN Model | RMSE Reduction (%) | |

March | 14.2951 | 16.6314 | 14.05 | 12.0131 | 16.1924 | 25.81 | 10.5982 | 16.1097 | 34.21 | 10.7687 | 16.13353 | 33.25 | 10.4168 | 16.0724 | 35.19 |

April | 14.1778 | 22.7773 | 37.75 | 12.7681 | 22.8833 | 44.2 | 13.0693 | 22.18422 | 41.09 | 10.5447 | 21.63 | 51.25 | 10.8462 | 21.8471 | 50.35 |

May | 17.6504 | 26.2172 | 32.68 | 17.0076 | 26.8919 | 36.76 | 15.3399 | 26.9629 | 43.11 | 14.4252 | 26.359 | 45.27 | 13.81256 | 26.35056 | 47.82 |

June | 15.5105 | 32.1841 | 51.81 | 14.0204 | 32.4554 | 56.8 | 13.6979 | 32.4173 | 57.75 | 13.28 | 32.1747 | 58.73 | 13.4313 | 32.08896 | 58.14 |

July | 11.7474 | 24.05563 | 51.17 | 11.77418 | 24.092 | 51.13 | 10.9207 | 24.2066 | 54.89 | 10.4535 | 23.9932 | 56.43 | 10.61337 | 23.9488 | 55.68 |

October | 14.1119 | 23.4509 | 39.82 | 13.0888 | 23.126 | 43.4 | 12.363 | 23.1641 | 46.63 | 12.677 | 23.6228 | 46.34 | 12.6537 | 23.714 | 46.64 |

**Table 5.**Performance comparison of proposed DNN framework for different ${N}_{1}$ and ${N}_{2}$ in terms of Pearson correlation coefficient.

Predicted Month | Correlation Coefficients | |||||||||
---|---|---|---|---|---|---|---|---|---|---|

${\mathit{N}}_{\mathbf{1}}=\mathbf{1},{\mathit{N}}_{\mathbf{2}}=\mathbf{1}$ | ${\mathit{N}}_{\mathbf{1}}=\mathbf{5},{\mathit{N}}_{\mathbf{2}}=\mathbf{5}$ | ${\mathit{N}}_{\mathbf{1}}=\mathbf{10},{\mathit{N}}_{\mathbf{2}}=\mathbf{10}$ | ${\mathit{N}}_{\mathbf{1}}=\mathbf{15},{\mathit{N}}_{\mathbf{2}}=\mathbf{15}$ | ${\mathit{N}}_{\mathbf{1}}=\mathbf{20},{\mathit{N}}_{\mathbf{2}}=\mathbf{20}$ | ||||||

Temperature | Humidity | Temperature | Humidity | Temperature | Humidity | Temperature | Humidity | Temperature | Humidity | |

March | 0.87 | 0.85 | 0.93 | 0.93 | 0.96 | 0.94 | 0.96 | 0.94 | 0.95 | 0.95 |

April | 0.82 | 0.89 | 0.92 | 0.91 | 0.95 | 0.93 | 0.95 | 0.95 | 0.95 | 0.94 |

May | 0.69 | 0.76 | 0.82 | 0.78 | 0.89 | 0.82 | 0.89 | 0.84 | 0.9 | 0.86 |

June | 0.79 | 0.76 | 0.87 | 0.80 | 0.92 | 0.81 | 0.92 | 0.82 | 0.91 | 0.82 |

July | 0.56 | 0.66 | 0.78 | 0.72 | 0.82 | 0.75 | 0.85 | 0.79 | 0.84 | 0.78 |

October | 0.73 | 0.8 | 0.85 | 0.83 | 0.911 | 0.84 | 0.92 | 0.84 | 0.92 | 0.84 |

**Table 6.**Effect of number of sensors (${N}_{1}$) on the temperature prediction accuracy of DNN model in terms of RMSE.

Predicted Month | RMSE | ||
---|---|---|---|

${\mathit{N}}_{\mathbf{1}}=\mathbf{5},{\mathit{N}}_{\mathbf{2}}=\mathbf{10}$ | ${\mathit{N}}_{\mathbf{1}}=\mathbf{10},{\mathit{N}}_{\mathbf{2}}=\mathbf{10}$ | ||

DNN Model | DNN Model | W/O DNN Model | |

March | 2.1337 | 1.7744 | 4.295 |

April | 2.811 | 2.4798 | 5.0001 |

May | 3.5302 | 2.8061 | 10.051 |

June | 3.5121 | 2.9317 | 16.4949 |

July | 3.2077 | 2.7632 | 13.7796 |

October | 3.305 | 2.6372 | 7.5388 |

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## Share and Cite

**MDPI and ACS Style**

Ajani, O.S.; Usigbe, M.J.; Aboyeji, E.; Uyeh, D.D.; Ha, Y.; Park, T.; Mallipeddi, R.
Greenhouse Micro-Climate Prediction Based on Fixed Sensor Placements: A Machine Learning Approach. *Mathematics* **2023**, *11*, 3052.
https://doi.org/10.3390/math11143052

**AMA Style**

Ajani OS, Usigbe MJ, Aboyeji E, Uyeh DD, Ha Y, Park T, Mallipeddi R.
Greenhouse Micro-Climate Prediction Based on Fixed Sensor Placements: A Machine Learning Approach. *Mathematics*. 2023; 11(14):3052.
https://doi.org/10.3390/math11143052

**Chicago/Turabian Style**

Ajani, Oladayo S., Member Joy Usigbe, Esther Aboyeji, Daniel Dooyum Uyeh, Yushin Ha, Tusan Park, and Rammohan Mallipeddi.
2023. "Greenhouse Micro-Climate Prediction Based on Fixed Sensor Placements: A Machine Learning Approach" *Mathematics* 11, no. 14: 3052.
https://doi.org/10.3390/math11143052