Nondestructive Detection of Soluble Solids Content in Tomatoes Using Electrical Parameters and Machine Learning

doi:10.16420/j.issn.0513-353x.2023-0645

Acta Horticulturae Sinica ›› 2024, Vol. 51 ›› Issue (2): 385-395.doi: 10.16420/j.issn.0513-353x.2023-0645

• New Technologies·New Methods • Previous Articles Next Articles

Nondestructive Detection of Soluble Solids Content in Tomatoes Using Electrical Parameters and Machine Learning

WANG Tingting¹, TAN Zhanming¹^,^*(), CHENG Yunxia¹, MA Xinchao¹, WANG Yongming²

¹ Xinjiang Production & Construction Corps Key Laboratory of Protected Agriculture，College of Horticulture and Forestry，Tarim University，Alar，Xinjiang 843300，China
² Institute of Agricultural Sciences，First Division of Xinjiang Corps，Alar，Xinjiang 843300，China

Received:2023-11-28 Revised:2024-01-15 Online:2024-02-25 Published:2024-02-27
Contact: TAN Zhanming

Abstract

Abstract:

This study aims to establish a model for predicting the soluble solids content of tomatoes using electrical parameters and machine learning techniques. An LCR meter was employed to measure nine electrical parameters of tomatoes at frequencies of 0.1，1，10，100，and 1 000 kHz，including parallel equivalent capacitance，parallel equivalent resistance，and quality factor. Key electrical characteristic variables were identified through Pearson correlation analysis. Based on these variables，three non-destructive models for predicting the soluble solids content of tomatoes were constructed：a Back Propagation Neural Network（BpNN），Multiple Linear Regression（MLR），and Support Vector Regression （SVR）. The results indicate that at 10 kHz frequency，five electrical parameters-quality factor，loss factor，deflection angle，parallel equivalent capacitance，and parallel equivalent resistance-showed significant correlation with the soluble solids content of tomatoes. By using these five electrical parameters as input variables and the soluble solids content as the output variable，the SVR model exhibited the best predictive performance，coefficient of determination R² equal to 0.951，Root Mean Square Error equal to 0.122，Mean Absolute Error equal to 0.082）. This study provides a new method for the rapid and non-destructive detection of soluble solids content in post-harvest tomatoes.

Key words: tomato, soluble solids content, electrical characteristics, machine learning, non-destructive testing

WANG Tingting, TAN Zhanming, CHENG Yunxia, MA Xinchao, WANG Yongming. Nondestructive Detection of Soluble Solids Content in Tomatoes Using Electrical Parameters and Machine Learning[J]. Acta Horticulturae Sinica, 2024, 51(2): 385-395.

Figures/Tables 14

Fig. 1 Electrical parameter detection schematic of soluble solid content in tomato

Fig. 2 BP neural network signal propagation

Fig. 3 SVR model mapping principle

Table 1 Pearson correlation coefficient

\|r\|	相关程度 Degree of relevance	\|r\|	相关程度 Degree of relevance
0.8 ~ 1.0	极强相关 Extremely strongly correlated	0.2 ~ 0.4	弱相关 Weak correlation
0.6 ~ 0.8	强相关 Strong correlation	0.0 ~ 0.2	极弱相关或无相关 Very weak or no correlation
0.4 ~ 0.6	中等程度相关 Moderately relevant

Fig. 4 Pearson correlation coefficients between electrical parameters and SSC of tomato at different frequency bands

Fig. 5 BpNN model training flowchart Y：Yes；N：No. The same below.

Table 2 BpNN paragraph settings

网络参数 Network parameter	设置或取值 Set or take a value
Epoch	100
Batch_size	10
Learning_rate	0.1
Activation function	Relu
Number of layers	3

Fig. 6 BpNN prediction value compared to true value of soluble solids content in tomato

Table 3 Comparison of the effects of different input variables in the MLR model of soluble solids content in tomato

输入变量 Input variables	R²	RMSE	MAE
Q	0.513	0.560	0.518
Q + D	0.562	0.480	0.424
Q + D + θ	0.680	0.387	0.358
Q + D + θ + R_p	0.813	0.324	0.372
Q + D + θ + R_p + C_p	0.856	0.252	0.116

Fig. 7 Comparison of predicted value and true value of MLR model of soluble solids content in tomato

Fig. 8 SVR model training flowchart

Table 4 SVR parameter settings

参数 Pparameter	设置或取值 Set or take a value
惩罚系数C	1.8
Kernel	Linear
Gamma	Auto
Tol	0.001
Shrinkin	Ture
Cache_size	200
Max_iter	-1

Fig. 9 Comparison of predicted value and true value of SVR model of soluble solids content in tomato

Table 5 Comparison of the three models of soluble solids content in tomato

模型 Model	R²	RMSE
BpNN	0.865	0.143	0.153
MLR	0.856	0.252	0.116
SVR	0.951	0.122	0.082

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Nondestructive Detection of Soluble Solids Content in Tomatoes Using Electrical Parameters and Machine Learning

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