# Optimization of CART Models Using Metaheuristics for Predicting Peach Firmness
## 2 Literature Review  
The precise prediction of fruit harvest date and extension of its shelf life can be obtained by early assessment of fruit maturity. To accurately predict peach firmness based on the measured dielectric properties, a backpropagation neural network was used in [2]. In addition, the conducted correlation analysis returned high correlation coefficients between dielectric properties and peach firmness. The research described in [8] focuses on investigating the dielectric properties of banana fruit to develop a rapid and non-destructive assessment method and to control the fruit ripening treatment. Similarly, in [9], the dielectric properties of Fuji apples with red-dot disease are investigated. To identify the category of the fruit, [PERSON] et al. developed a 13-layer convolutional neural network (CNN) [3]. An apple bruse detection system based on hyperspectral imaging in the shortwave infrared range is described in [10]. The research uses partial least squares discriminant analysis to discriminate bruised pixel spectra from sound pixel spectra. The successful prediction of banana fruit maturity based on measured impedance using a simple linear regression model is conducted in [11]. The research also proves that the measured impedance and banana maturity are correlated. The research described in [4; 12] uses image data to predict melon and apple maturity, respectively. In [4], the discriminant analysis model is used for prediction, while the backpropagation neural network model is used in [12]. For the purpose of predicting peach firmness, the backpropagation neural network, multiple linear regression, and simple linear regression model are developed in [5]. The experiment results in [5] show that the multiple linear regression model is the most accurate one. The research described in [1] extends [5] by including additional features in the dataset as well as including a new regression tree model that is optimized using various nature-inspired metaheuristic algorithms to additionally optimize the model. Multiple linear regression, default regression tree, and regression tree models optimized using the genetic algorithm, bat algorithm, differential evolution, and particle swarm optimization metaheuristic algorithms are used for predicting peach firmness in [1]. This research builds on [1] by introducing artificial neural networks, adaptive-network-based fuzzy inference systems, bagging, boosting, and random forest ensemble models. With respect to the NFL theorem, four new metaheuristic algorithms are applied for regression tree optimization. Since metaheuristics are not problem-specific, they can be applied in a variety of applications. To optimize the energy consumption in different fields, the bat algorithm is used in [13], while in [14; 15], the differential evolution and particle swarm optimization algorithms are used, respectively. The variation in the PSO algorithm is applied in [16] to improve the design of parabolic dish concentrators, while in [17] it is used to optimize the gateway placement in wireless mesh networks. To optimize the speed control of a brushless direct current drive, the bat algorithm is used in [18]. Similar to [19], the genetic algorithm is used for tuning the control gains of the fuzzy controller to minimize the energy consumption of unmanned aerial vehicles. The metaheuristic algorithms are also applied in the optimization of the machine learning models, including classification and decision trees. The paper in [20] describes a machine learning model based on an extreme learning machine (ELM) optimized by the bat algorithm. A convolutional neural network and bat algorithm are used in [21], where the bat algorithm is utilized in the feature selection process, which increases the accuracy of the overall model. A method described in [22] employs differential evolution algorithms to optimize the initial weights of the convolutional neural network, aiming to achieve near-global optimal solutions and expedite network convergence.