联合图像最优特征提取及改进RBF神经网络的苹果质量估计

目的：以阿克苏苹果为例，设计一种联合图像最优特征提取和改进RBF神经网络学习的苹果质量估计方法，以克服人工分级称重成本高、误差大的缺陷。方法：首先，建立苹果图像采集系统，得到苹果前景图像信息；其次，设计苹果图像特征集合最佳子集提取策略，将最佳子集提取过程转化为目标函数优化问题，并利用改进的离散蝗虫优化算法进行求解，从而得到最佳苹果图像特征子集；最后，构建基于RBF神经网络学习的苹果质量估计模型，将最佳特征子集作为网络输入，并采用蝗虫优化算法优化配置RBF神经网络超参数，从而实现对苹果质量的有效估计。结果：所提苹果质量估计方法精度更高，质量估计值平均相对误差率为1.23%。结论：该方法可以有效实现苹果质量预估，也能够推广应用到其他类似轴对称形状的水果质量估计。

Apple weight estimation based on joint image optimal feature extraction and improved RBF neural network

Objective: Taking Aksu apples as an example, a joint image optimal feature extraction and improved RBF neural network learning apple weight estimation method is designed to overcome the high cost and large error of manual grading and weighing. Methods: Firstly, an apple image acquisition system was established to obtain apple foreground image information. Secondly, the optimal subset extraction strategy for apple image feature sets was designed, by transforming the process of extracting the optimal subset into an objective function optimization problem, and an improved discrete locust optimization algorithm was designed to obtain the optimal apple image feature subset. Finally, a weight estimation model for apples based on RBF neural network learning was constructed, with the optimal feature subset as network input. The locust optimization algorithm was used to optimize the configuration of RBF neural network hyperparameters, to achieve effective estimation of apple weight. Results: The proposed apple weight estimation method had higher accuracy, with an average relative error rate of 1.23% for weight estimation. Conclusion: This method can effectively achieve apple weight estimation and can also be applied to other fruits with similar axisymmetric shapes for weight estimation.