REFERENCES
1. Teixeira, G. C. M.; Junior, J. S. P.; Mattiuz, B.; et al. Spraying of calcium carbonate nanoparticles on pineapple fruit reduces sunburn damage. S. Afr. J. Bot. 2022, 148, 643-51.
2. Zeng, S.; Cai, X.; Guo, W.; Zhang, Z.; Yang, S. Differences in optical properties and internal qualities of ‘Fuji’ apple produced in different areas of the Loess Plateau region. Eur. J. Agron. 2022, 140, 126608.
3. Serrano-Finetti, E.; Castillo, E.; Alejos, S.; León, H. L. Toward noninvasive monitoring of plant leaf water content by electrical impedance spectroscopy. Comput. Electron. Agric. 2023, 210, 107907.
4. Van Haeverbeke M, De Baets B, Stock M. Plant impedance spectroscopy: a review of modeling approaches and applications. Front. Plant. Sci. 2023, 14, 1187573.
5. Astashev, M. E.; Konchekov, E. M.; Kolik, L. V.; Gudkov, S. V. Electric impedance spectroscopy in trees condition analysis: theory and experiment. Sensors 2022, 22, 8310.
6. Cheng, J.; Yu, P.; Huang, Y.; Zhang, G.; Lu, C.; Jiang, X. Application status and prospect of impedance spectroscopy in agricultural product quality detection. Agriculture 2022, 12, 1525.
7. Fathizadeh, Z.; Aboonajmi, M.; Hassan-Beygi, S. R. Nondestructive methods for determining the firmness of apple fruit flesh. Inf. ProcessAgric. 2021, 8, 515-27.
8. Hiruta, T.; Sasaki, K.; Hosoya, N.; Maeda, S.; Kajiwara, I. Firmness evaluation of postharvest pear fruit during storage based on a vibration experiment technique using a dielectric elastomer actuator. Postharvest. Biol. Technol. 2021, 182, 111697.
9. Feng, L.; Zhang, M.; Dong, Z.; Guo, J.; Zhang, H.; Liu, Z. Electrical impedance spectroscopy: potential non-destructive method for aflatoxin B1 in peanut. Food. Meas. 2024, 18, 9353-63.
10. Tang, Y.; Zhang, H.; Liang, Q.; Xia, Y.; Che, J.; Liu, Y. Non-destructive testing of the internal quality of korla fragrant pears based on dielectric properties. Horticulturae 2024, 10, 572.
11. Yu, Y.; Yao, M. Is this pear sweeter than this apple? A universal SSC model for fruits with similar physicochemical properties. Biosyst. Eng. 2023, 226, 116-31.
12. Liu, D.; Wang, E.; Wang, G.; Wang, P.; Wang, C.; Wang, Z. Non-destructive sugar content assessment of multiple cultivars of melons by dielectric properties. J. Sci. Food. Agric. 2021, 101, 4308-14.
13. Feng, L.; Gao, J.; Sui, X.; Weng, T.; Kong, A. Effect of fruit ripeness on electrical impedance spectrum parameters. LWT. 2024, 208, 116751.
14. Yang, Z.; Amin, A.; Zhang, Y.; Wang, X.; Chen, G.; Abdelhamid, M. A. Design of a tomato sorting device based on the multisine-FSR composite measurement. Agronomy 2023, 13, 1778.
15. Velásquez, S.; Franco, A. P.; Peña, N.; Bohórquez, J. C.; Gutierrez, N. Effect of coffee cherry maturity on the performance of the drying process of the bean: sorption isotherms and dielectric spectroscopy. Food. Control. 2021, 123, 107692.
16. Ibba, P.; Falco, A.; Abera, B. D.; Cantarella, G.; Petti, L.; Lugli, P. Bio-impedance and circuit parameters: an analysis for tracking fruit ripening. Postharvest. Biol. Technol. 2020, 159, 110978.
17. An, J.; Luo, X.; Xiong, L.; Tang, X.; Lan, H. Discrimination of inner injury of Korla fragrant pear based on multi-electrical parameters. Foods 2023, 12, 1805.
18. Ibba, P.; Crepaldi, M.; Cantarella, G.; et al. Design and validation of a portable AD5933–based impedance analyzer for smart agriculture. IEEE. Access. 2021, 9, 63656-75.
19. Żywica, R.; Pierzynowska-Korniak, G.; Wójcik, J. Application of food products electrical model parameters for evaluation of apple purée dilution. J. Food. Eng. 2005, 67, 413-8.
20. Bian, H.; Tu, P. The simultaneous monitoring of physiological change of apple based on dielectric parameters in static pressure. J. Chin. Inst. Food. Sci. Technol. 2019, 19, 279-85.
21. Cai, C.; Li, X.; Ma, H.; et al. Non-destructive detection of freshness grade for apple fruit based on bio-impedance properties. Trans. Chin. Soc. Agric. Mach. 2013, 44, 147-52.
22. Zhou, Z.; Zhang, Y.; Gu, Z.; Yang, S. X. Deep learning approaches for object recognition in plant diseases: a review. Intell. Robot. 2023, 3, 514-37.
23. Berahmand, K.; Bahadori, S.; Abadeh, M. N.; Li, Y.; Xu, Y. SDAC-DA: semi-supervised deep attributed clustering using dual autoencoder. IEEE. Trans. Knowl. Data. Eng. 2024, 36, 6989-7002.
24. Berahmand, K.; Li, Y.; Xu, Y. DAC-HPP: deep attributed clustering with high-order proximity preserve. Neural. Comput. Applic. 2023, 35, 24493-511.
25. Yongnian, Z.; Yinhe, C.; Yihua, B.; Xiaochan, W.; Jieyu, X. Tomato maturity detection based on bioelectrical impedance spectroscopy. Comput. Electron. Agric. 2024, 227, 109553.
26. Badfar, M.; Yildirim, M.; Chinnam, R. State-of-charge estimation across battery chemistries: a novel regression-based method and insights from unsupervised domain adaptation. J. Power. Sources. 2025, 628, 235760.
27. Liu, D.; Guo, W. Nondestructive determination of soluble solids content of persimmons by using dielectric spectroscopy. Int. J. Food. Prop. 2017, 20, S2596-611.
28. Guo, W.; Fang, L.; Liu, D.; Wang, Z. Determination of soluble solids content and firmness of pears during ripening by using dielectric spectroscopy. Comput. Electron. Agric. 2015, 117, 226-33.
29. Guo, W.; Shang, L.; Zhu, X.; Nelson, S. O. Nondestructive detection of soluble solids content of apples from dielectric spectra with ANN and chemometric methods. Food. Bioprocess. Technol. 2015, 8, 1126-38.
30. Reyes, A.; Yarlequé, M.; Castro, W.; Chuquizuta, S. Determination of dielectric properties of the red delicious apple and its correlation with quality parameters. In 2017 Progress in Electromagnetics Research Symposium - Fall (PIERS - FALL), Singapore, Nov 19-22, 2017; IEEE, 2017; pp. 19-22.
31. Bian, H.; Shi, P.; Tu, P. Determination of physicochemical quality of bruised apple using dielectric properties. Food. Meas. 2020, 14, 2590-9.
32. Wang, D.; Li, L.; Liang, J.; et al. Nondestructive detection of kiwifruit treated with N-(2-chloro-4-pyridyl)-N’-phenylurea by electrical method. J. Food. Process. Preserv. 2020, 44, e14860.
33. Mohammed, M.; Munir, M.; Aljabr, A. Prediction of date fruit quality attributes during cold storage based on their electrical properties using artificial neural networks models. Foods 2022, 11, 1666.
34. Wang, D.; Yang, S. X. Intelligent feature extraction, data fusion and detection of concrete bridge cracks: current development and challenges. Intell. Robot. 2022, 2, 391-406.
35. Weng, Y. K.; Chen, J.; Cheng, C. W.; Chen, C. Use of modern regression analysis in the dielectric properties of foods. Foods 2020, 9, 1472.
36. Niu, Y.; Wang, D.; Ye, L.; et al. Nondestructive detection of kiwifruit infected with Penicillium expansum based on electrical properties. Postharvest. Biol. Technol. 2023, 195, 112150.
37. Bian, H.; Tu, P.; Hua-li, X.; Shi, P. Quality predictions for bruised apples based on dielectric properties. J. Food. Process. Preserv. 2019, 43, e14006.
38. Aparisi P, Fortes Sanchez E, Contat Rodrigo L, Masot Peris R, Laguarda-miro N. A rapid electrochemical impedance spectroscopy and sensor-based method for monitoring freeze-damage in tangerines. IEEE. Sensors. J. 2021, 21, 12009-18.
39. Guo, W.; Shang, L.; Wang, M.; Zhu, X. Soluble solids content detection of postharvest apples based on frequency spectrum of dielectric parameters. Trans. Chin. Soc. Agric. Mach. 2013, 44, 132-7.
40. Shang, L.; Guo, W.; Nelson, S. O. Apple variety identification based on dielectric spectra and chemometric methods. Food. Anal. Methods. 2015, 8, 1042-52.
41. Wang, R.; Wang, D.; Ren, X.; Ma, H. Nondestructive detection of apple watercore disease based on electric features. Trans. Chin. Soc. Agric. Eng. 2018, 34, 129-36.
42. Hinton, G. E.; Salakhutdinov, R. R. Reducing the dimensionality of data with neural networks. Science 2006, 313, 504-7.
43. Vincent, P.; Larochelle, H.; Bengio, Y.; Manzagol, P. A. Extracting and composing robust features with denoising autoencoders. In Proceedings of the 25th international conference on Machine learning, 2008; pp. 1096-103.
44. Berahmand, K.; Daneshfar, F.; Salehi, E. S.; Li, Y.; Xu, Y. Autoencoders and their applications in machine learning: a survey. Artif. Intell. Rev. 2024, 57, 10662.
45. Cacciarelli, D.; Kulahci, M. Hidden dimensions of the data: PCA vs autoencoders. Qual. Eng. 2023, 35, 741-50.
46. Tsakiridis, N. L.; Samarinas, N.; Kokkas, S.; Kalopesa, E.; Tziolas, N. V.; Zalidis, G. C. In situ grape ripeness estimation via hyperspectral imaging and deep autoencoders. Comput. Electron. Agric. 2023, 212, 108098.
47. Plakias, S.; Boutalis, Y. S. A novel information processing method based on an ensemble of auto-encoders for unsupervised fault detection. Comput. Ind. 2022, 142, 103743.
48. Yuan, X.; Qi, S.; Wang, Y.; Xia, H. A dynamic CNN for nonlinear dynamic feature learning in soft sensor modeling of industrial process data. Control. Eng. Pract. 2020, 104, 104614.
49. Meng, L.; Ding, S.; Xue, Y. Research on denoising sparse autoencoder. Int. J. Mach. Learn. Cyber. 2017, 8, 1719-29.
50. Li, P.; Pei, Y.; Li, J. A comprehensive survey on design and application of autoencoder in deep learning. Appl. Soft. Comput. 2023, 138, 110176.
51. Ahmed, S. A software framework for predicting the maize yield using modified multi-layer perceptron. Sustainability 2023, 15, 3017.
52. Shang, L.; Wang, J.; Schäfer, D.; et al. Surrogate modelling of a detailed farm-level model using deep learning. J. Agric. Econ. 2024, 75, 235-60.
53. Zhang, H.; Liu, Y.; Tang, Y.; Lan, H.; Niu, H.; Zhang, H. Non-destructive detection of the fruit firmness of Korla fragrant pear based on electrical properties. Int. J. Agric. Biol. Eng. 2022, 15, 216-21.
54. Cai, C.; Li, Y.; Ma, H.; Li, X. Nondestructive classification of internal quality of apple based on dielectric feature selection. Trans. Chin. Soc. Agric. Eng. 2013, 29, 279-87. https://www.cabidigitallibrary.org/doi/full/10.5555/20143067192. (accessed 2025-02-21)
55. Drezner, Z.; Turel, O.; Zerom, D. A modified Kolmogorov–Smirnov test for normality. Commun. Stat. Simul. Comput. 2010, 39, 693-704.
