Detection of Localized Damage in Tomato Based on Bioelectrical Impedance Spectroscopy
Abstract
:1. Introduction
2. Materials and Methods
2.1. Impedance Measurement Experiments
2.1.1. Principle of Tomato Four-Electrode Impedance Spectroscopy
2.1.2. Experimental Setup for Damage Measurement
2.2. Pressing Test
2.3. Scanning Electron Microscope Experiment
2.4. Modeling of Localized Injury Classification in Tomato
2.5. Evaluation Criteria for Localized Impairment Models
3. Results and Analysis
4. Summary and Outlook
4.1. Summary
- (1)
- A circular four-electrode BIS sensor is designed for the nondestructive measurement of localized damage in tomato. A localized damage measurement platform for tomatoes is constructed by combining this sensor. A comparison of the impedance measurements obtained from the sensor with those obtained from the needle sensor proposed by previous scholars reveals a similar trend, with the impedance decreasing with the increasing damage degree. This validates the effectiveness of the circular four-electrode BIS sensor for tomato in characterizing damage.
- (2)
- Multiple features, including biological variables, fitted electrical parameters, and tomato ripeness, are subjected to Spearman feature selection, resulting in a downscaled feature set comprising 85% or more of the total features. This downscaled feature set is then inputted into the classification model. A total of 1616 sets of data obtained from the experiments are divided into three subsets: the training set, the validation set, and the test set. The ratio of the training set to the validation set and the validation set to the test set is 8:1:1, respectively. The classification accuracies of the tomato in each damage class are as follows, in descending order: the results demonstrate that 97.531%, 98.137%, and 98.765%, respectively, are the optimal classification algorithms, with Spearman-SVM-ANN being the most effective in detecting tomato damage.
4.2. Outlook
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Features | Eigenvalue | Cumulative Percentage (%) | Relevance |
---|---|---|---|
Features 9 | 0.7632 | 40.338 | 40.338 |
Features 5 | 0.2921 | 55.777 | 15.439 |
Features 6 | 0.2908 | 71.146 | 15.369 |
Features 7 | 0.1007 | 77.444 | 6.297 |
Features 1 | 0.0917 | 82.764 | 5.320 |
Features 10 | 0.0891 | 87.611 | 4.847 |
Classification Algorithms | Training Time (s) | Training Set ACC | Validation Set ACC | Test Set ACC |
---|---|---|---|---|
Spearman-SVM | 5.262 | 99.065% | 98.137% | 97.531% |
Spearman-ANN | 5.748 | 99.056% | 98.758% | 98.137% |
Spearman-SVM-ANN | 3.294 | 99.381% | 98.758% | 98.765% |
Forecasting Team Member Information | ||||||
---|---|---|---|---|---|---|
LV1 | LV2 | LV3 | LV4 | Total | ||
count | LV1 | 48 | 6 | 0 | 0 | 54 |
LV2 | 3 | 490 | 1 | 0 | 494 | |
LV3 | 0 | 1 | 264 | 0 | 265 | |
LV4 | 0 | 0 | 1 | 802 | 803 | |
% | LV1 | 88.9% | 11.1% | 0% | 0% | 100% |
LV2 | 0.6% | 99.2% | 0.2% | 0% | 100% | |
LV3 | 0% | 0.4% | 99.6% | 0% | 100% | |
LV4 | 0% | 0% | 0.1% | 99.9% | 100% |
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Zhang, Y.; Chen, Y.; Chang, Z.; Zhao, J.; Wang, X.; Xian, J. Detection of Localized Damage in Tomato Based on Bioelectrical Impedance Spectroscopy. Agronomy 2024, 14, 1822. https://doi.org/10.3390/agronomy14081822
Zhang Y, Chen Y, Chang Z, Zhao J, Wang X, Xian J. Detection of Localized Damage in Tomato Based on Bioelectrical Impedance Spectroscopy. Agronomy. 2024; 14(8):1822. https://doi.org/10.3390/agronomy14081822
Chicago/Turabian StyleZhang, Yongnian, Yinhe Chen, Zhenwei Chang, Jie Zhao, Xiaochan Wang, and Jieyu Xian. 2024. "Detection of Localized Damage in Tomato Based on Bioelectrical Impedance Spectroscopy" Agronomy 14, no. 8: 1822. https://doi.org/10.3390/agronomy14081822