Tomato maturity detection based on bioelectrical impedance spectroscopy

Abstract

This paper proposes a method for detecting tomato maturity to address issues related to post-harvest storage and transportation. The method utilizes bioimpedance spectroscopy to study tomato maturity, construct the Double-R-Cole equivalent circuit model, and obtain electrical parameters through fitting the Levenberg-Marquardt optimization algorithm. We analyze the change rule of electrical parameters in different ripening periods, use Fisher’s discriminant to reduce the dimensionality of features such as biological variables, fitted electrical parameters, and storage days of tomato, and combine the advantages of support vector machine and random forest to classify the input features. The classification algorithm utilizes the gorilla troop optimization algorithm to address issues with traditional iterative algorithms, such as difficulty assigning initial values and susceptibility to local optima. The study finds that: the Levenberg-Marquardt algorithm fitted an R^2 mean value of 0.997 and the significance level of p < 0.001 between the two constant-phase components of the fitted electrical parameters and the number of storage days proved that the established Double-R-Cole model could effectively characterize the postharvest situation of tomato; the Fisher’s discriminant based SVM-RF-GTO’s maturity classification algorithm achieves 97.26 % effectiveness in tomato maturity classification. This research provides valuable insights for tomato postharvest storage and transportation.