Study on Location of Temperature Sensors in Tomato Solar Greenhouse in North China

doi:10.13304/j.nykjdb.2021.0929

Abstract

Abstract:

In order to improve the accuracy of greenhouse environmental monitoring and reduce the cost of environmental perception， this study designed a multi-point environmental monitoring system. Aiming at the difficulty of temperature management the increase of tomato disease rate in autumn and winter， the temperature in tomato greenhouse in Shandong province was continuously monitored. Based on the measured data， gaussian function was used to fit the daily variation curve of greenhouse temperature by least square multi-peak method， and the discrete data points were converted into continuous Gaussian function， and the optimal monitoring position of tomato in each growth cycle was obtained by integral calculation and comparison. The results showed that multi-peak fitting analysis of greenhouse temperature with gaussian function as the target had significant effect， and the lowest goodness of fit was 0.919； the absolute difference of integral area of temperature curve between monitoring points N₁₅ and N₁₀ and the greenhouse average temperature was the smallest in seedling and flowering and fruit-setting periods of tomato， so the best monitoring points at this growth period were N₁₅ and N₁₀； the locations of N₁₀and N₃monitoring points at tomato fruit period were closest to the average change level of greenhouse as a whole， which were the best monitoring position at this period. In conclusion， in different growth period of tomato， the monitoring point N₁₀ in the middle of the greenhouse could better represent the overall level of the greenhouse. This method laid a foundation for the accurate monitoring of the greenhouse environment.

Key words: solar greenhouse, sensor layout, multi-peak fitting, gaussian function

摘要：

为提高温室环境监测的精准性，降低环境感知成本，设计了多点环境监测系统。针对秋冬季温室温度管理难度大导致的番茄病害率增加等现象，对山东省番茄温室内的温度进行持续监测，依据实测数据选用高斯函数对温室温度的日变化曲线进行最小二乘多峰拟合，将离散的数据点转化为连续的高斯函数，通过积分比较得到番茄各生长周期的最佳监测位置。结果表明，以高斯函数为目标对温室温度进行多峰拟合分析，效果较显著，最小决定系数为0.919；番茄苗期与开花坐果期，监测点N₁₅、N₁₀与温室平均温度之间温度曲线积分面积的绝对差最小，因此该生长时期的最佳监测点为N₁₅和N₁₀；番茄结果期，N₁₀和N₃监测点所在位置最接近温室整体平均变化水平，为该周期的最佳监测位置。综上，在番茄不同生长周期中，温室中部监测点N₁₀更能代表温室整体水平，该方法为温室环境精准监测奠定了基础。

关键词: 日光温室, 传感器布设, 多峰拟合, 高斯函数

CLC Number:

S214.3

Yuting YU, Pingzeng LIU, Xiuli WANG, Yan ZHANG, Shen WANG, Chengbao SONG, Feng MA. Study on Location of Temperature Sensors in Tomato Solar Greenhouse in North China[J]. Journal of Agricultural Science and Technology, 2023, 25(3): 107-118.

于玉婷, 柳平增, 王秀丽, 张艳, 王珅, 宋成宝, 马峰. 北方番茄日光温室温度传感器布设位置研究[J]. 中国农业科技导报, 2023, 25(3): 107-118.

Figures/Tables 14

Fig. 1 Top view of monitoring site layout

Fig. 2 Side view of monitoring points

Fig. 3 Average temperature distribution of tomato in each growth cycle

Fig. 4 Peak search for temperature curve

Table 1 Multi-peak fitting parameters

参数Parameter	值Value	标准误差Standard error	显著性Statistical significance	相关性Correlation
y₀	20.725	0.036	<0.001^***	0.799^***
X_c1	718.104	0.205	<0.001^***	0.895^***
A₁	98.517	3.778	<0.001^***	0.937^***
W₁	11.536	0.401	<0.001^***	0.898^***
X_c2	726.635	0.152	<0.001^***	0.754^***
A₂	34.727	5.060	<0.001^***	0.981^***
W₂	6.273	0.432	<0.001^***	0.887^***
X_c3	736.666	0.173	<0.001^***	0.853^***
A₃	142.610	5.640	<0.001^***	0.964^***
W₃	14.746	0.585	<0.001^***	0.952^***
X_c4	760.133	0.314	<0.001^***	0.961^***
A₄	216.719	20.278	<0.001^***	0.997^***
W₄	18.656	0.668	<0.001^***	0.968^***
X_c5	780.404	1.268	<0.001^***	0.979^***
A₅	135.128	43.710	0.002^**	0.999^***
W₅	27.927	4.569	<0.001^***	0.994^***
X_c6	808.323	5.353	<0.001^***	0.993^***
A₆	105.444	29.975	<0.001^***	0.996^***
W₆	42.972	6.527	<0.001^***	0.983^***

Fig. 5 Seedling period fitting effect

Table 2 Analysis of temperature monitoring points in seedling period

编号Number	面积S	决定系数R²	最大残差平方和SSE_max	面积绝对差 $\| Δ S \|$
N₁	727.402	0.997	8.598	115.159
N₂	683.263	0.996	9.055	71.019
N₃	544.102	0.994	8.609	68.142
N₄	641.791	0.994	10.048	29.547
N₅	725.744	0.995	11.630	113.501
N₆	799.352	0.988	33.832	187.109
N₇	616.048	0.992	11.076	3.805
N₈	550.691	0.990	9.936	61.553
N₉	525.044	0.989	9.574	87.200
N₁₀	608.684	0.995	7.974	3.560
N₁₁	543.080	0.995	5.714	69.163
N₁₂	667.199	0.995	10.071	54.956
N₁₃	702.717	0.995	9.734	90.474
N₁₄	656.773	0.992	13.350	44.530
N₁₅	615.179	0.995	7.424	2.936
N₁₆	673.215	0.989	19.837	60.972
N₁₇	590.151	0.993	7.214	22.093
N₁₈	588.561	0.990	7.327	23.683
N₁₉	838.599	0.985	7.395	226.355
N₂₀	640.910	0.992	12.561	28.666

Table 2 Analysis of temperature monitoring points in seedling period

编号Number	面积S	决定系数R²	最大残差平方和SSE_max	面积绝对差 $\| Δ S \|$
N₁	727.402	0.997	8.598	115.159
N₂	683.263	0.996	9.055	71.019
N₃	544.102	0.994	8.609	68.142
N₄	641.791	0.994	10.048	29.547
N₅	725.744	0.995	11.630	113.501
N₆	799.352	0.988	33.832	187.109
N₇	616.048	0.992	11.076	3.805
N₈	550.691	0.990	9.936	61.553
N₉	525.044	0.989	9.574	87.200
N₁₀	608.684	0.995	7.974	3.560
N₁₁	543.080	0.995	5.714	69.163
N₁₂	667.199	0.995	10.071	54.956
N₁₃	702.717	0.995	9.734	90.474
N₁₄	656.773	0.992	13.350	44.530
N₁₅	615.179	0.995	7.424	2.936
N₁₆	673.215	0.989	19.837	60.972
N₁₇	590.151	0.993	7.214	22.093
N₁₈	588.561	0.990	7.327	23.683
N₁₉	838.599	0.985	7.395	226.355
N₂₀	640.910	0.992	12.561	28.666

Fig. 6 Fitting effect diagram of flowering and fruit-setting period

Table 3 Analysis of temperature monitoring points during flowering and fruit-setting period

编号Number	面积S	决定系数R²	最大残差平方和SSE_max	面积绝对差 $\| Δ S \|$
N₁	1 248.018	0.994	22.318	51.999
N₂	1 054.503	0.995	18.199	141.516
N₃	933.838	0.997	7.431	262.181
N₄	1 298.808	0.995	17.050	102.789
N₅	975.805	0.997	17.562	220.214
N₆	1 347.933	0.994	21.070	151.914
N₇	1 060.641	0.996	19.455	135.378
N₈	1 037.902	0.993	15.896	158.117
N₉	1 122.535	0.993	16.110	73.484
N₁₀	1 209.888	0.994	19.456	13.869
N₁₁	1 230.460	0.994	14.158	34.441
N₁₂	1 312.735	0.996	19.258	116.716
N₁₃	1 344.941	0.994	14.682	148.921
N₁₄	1 331.852	0.997	13.724	135.833
N₁₅	1 195.037	0.997	10.142	0.982
N₁₆	1 325.261	0.996	15.528	129.242
N₁₇	1 116.874	0.992	16.555	79.145
N₁₈	1 120.108	0.996	9.720	75.911
N₁₉	1 104.571	0.996	11.020	91.448
N₂₀	1 241.593	0.996	16.521	45.575

Table 3 Analysis of temperature monitoring points during flowering and fruit-setting period

编号Number	面积S	决定系数R²	最大残差平方和SSE_max	面积绝对差 $\| Δ S \|$
N₁	1 248.018	0.994	22.318	51.999
N₂	1 054.503	0.995	18.199	141.516
N₃	933.838	0.997	7.431	262.181
N₄	1 298.808	0.995	17.050	102.789
N₅	975.805	0.997	17.562	220.214
N₆	1 347.933	0.994	21.070	151.914
N₇	1 060.641	0.996	19.455	135.378
N₈	1 037.902	0.993	15.896	158.117
N₉	1 122.535	0.993	16.110	73.484
N₁₀	1 209.888	0.994	19.456	13.869
N₁₁	1 230.460	0.994	14.158	34.441
N₁₂	1 312.735	0.996	19.258	116.716
N₁₃	1 344.941	0.994	14.682	148.921
N₁₄	1 331.852	0.997	13.724	135.833
N₁₅	1 195.037	0.997	10.142	0.982
N₁₆	1 325.261	0.996	15.528	129.242
N₁₇	1 116.874	0.992	16.555	79.145
N₁₈	1 120.108	0.996	9.720	75.911
N₁₉	1 104.571	0.996	11.020	91.448
N₂₀	1 241.593	0.996	16.521	45.575

Fig. 7 Fitting effect diagram during the fruiting period

Table 4 Analysis of temperature monitoring points in fruiting period

编号Number	面积S	决定系数R²	最大残差平方和SSE_max	面积绝对差 $\| Δ S \|$
N₁	475.692	0.950	75.134	138.328
N₂	476.545	0.962	82.435	137.476
N₃	524.669	0.978	36.277	89.352
N₄	907.467	0.987	22.413	293.446
N₅	845.389	0.997	5.476	231.369
N₆	1 089.876	0.997	7.109	475.856
N₇	310.710	0.951	92.737	303.311
N₈	980.921	0.993	13.506	366.901
N₉	868.557	0.995	11.550	254.537
N₁₀	660.607	0.981	45.321	46.586 1
N₁₁	878.264	0.995	9.142	264.243
N₁₂	411.155	0.977	39.670	202.866
N₁₃	476.474	0.975	24.590	137.547
N₁₄	1 031.656	0.989	24.649	417.636
N₁₅	492.496	0.990	11.890	121.524
N₁₆	855.332	0.982	30.418	241.311
N₁₇	360.998	0.986	8.831	253.022
N₁₈	818.191	0.996	8.547	204.171
N₁₉	727.926	0.994	16.132	113.905
N₂₀	352.663	0.919	119.734	261.358

Table 4 Analysis of temperature monitoring points in fruiting period

编号Number	面积S	决定系数R²	最大残差平方和SSE_max	面积绝对差 $\| Δ S \|$
N₁	475.692	0.950	75.134	138.328
N₂	476.545	0.962	82.435	137.476
N₃	524.669	0.978	36.277	89.352
N₄	907.467	0.987	22.413	293.446
N₅	845.389	0.997	5.476	231.369
N₆	1 089.876	0.997	7.109	475.856
N₇	310.710	0.951	92.737	303.311
N₈	980.921	0.993	13.506	366.901
N₉	868.557	0.995	11.550	254.537
N₁₀	660.607	0.981	45.321	46.586 1
N₁₁	878.264	0.995	9.142	264.243
N₁₂	411.155	0.977	39.670	202.866
N₁₃	476.474	0.975	24.590	137.547
N₁₄	1 031.656	0.989	24.649	417.636
N₁₅	492.496	0.990	11.890	121.524
N₁₆	855.332	0.982	30.418	241.311
N₁₇	360.998	0.986	8.831	253.022
N₁₈	818.191	0.996	8.547	204.171
N₁₉	727.926	0.994	16.132	113.905
N₂₀	352.663	0.919	119.734	261.358

Table 5 Measured data from symmetrical monitoring sites

时期Period	时间Time	N₁₀	N₁₁	N₃	N₁₃	N₅	N₁₅
苗期 Seeding period	8∶00	18.97	18.84	17.72	18.64	18.87	18.72
	12∶00	27.12	25.76	26.16	28.09	28.64	26.77
	14∶00	28.66	28.18	28.84	30.03	30.39	28.81
	20∶00	21.19	21.34	20.92	21.10	20.08	21.32
开花坐果期 Flowering and fruit-setting period	8∶00	15.79	15.72	15.30	15.62	16.10	15.62
	12∶00	31.90	27.72	25.88	29.59	35.22	30.80
	14∶00	27.56	27.15	25.70	27.46	29.84	28.32
	20∶00	18.39	18.44	18.70	18.73	18.65	18.41
结果期 Fruiting period	8∶00	8.86	8.88	8.56	9.06	8.76	8.60
	12∶00	28.69	34.34	27.55	28.96	32.80	29.64
	14∶00	34.98	35.23	30.57	30.64	35.06	32.96
	20∶00	15.25	15.05	14.48	15.24	15.08	15.03

Table 6 Measured data of greenhouse temperature monitoring points

时期Period	时间Time	平均值Average value	N₁₀	N₁	N₉	N₁₉	N₂₀
苗期 Seeding period	8∶00	18.88	18.97	19.08	19.14	18.72	19.09
	12∶00	26.80	27.12	27.91	26.16	25.24	27.84
	14∶00	28.55	28.66	30.07	27.59	26.58	28.08
	20∶00	21.45	21.19	21.09	21.93	22.11	21.53
开花坐果期 Flowering and fruit bearing period	8∶00	15.73	15.79	16.18	15.43	15.23	15.68
	12∶00	31.16	31.90	34.05	31.34	28.91	34.16
	14∶00	28.03	27.56	29.40	27.15	27.42	29.40
	20∶00	18.68	18.39	18.84	18.86	18.64	18.88
结果期 Fruiting period	8∶00	8.74	8.86	8.94	8.83	8.80	8.06
	12∶00	30.01	28.69	25.66	34.11	34.43	26.61
	14∶00	33.40	34.98	29.06	35.71	36.69	31.36
	20∶00	15.00	15.25	15.12	15.21	15.14	14.34

Fig. 8 Optimal sensor layout area

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