改进LeNet-5模型在大米分选算法上的应用

针对大米特征提取与分类的算法难以确定的问题，改进LeNet-5卷积神经网络模型并研究其在大米分选问题上的表现。首先对大米原始图像进行预处理、提取单粒大米的图像建立大米图像样本库，然后对原始的LeNet-5模型提出改进方案并进行实验,最后将改进模型与若干传统分类方法、3个轻量化卷积神经网络模型进行对比。改进LeNet-5模型大米形选准确率为97.2%，色选准确率90.6%，处理速度约5 300粒·s^-1，分类效果与分类速度显著优于其他对比方法。实验结果证明，改进的LeNet-5模型可以高效分选碎米与垩白米，且能有效减少实际分选前准备工作的工作量。

Rice classification algorithm research based on modified LeNet-5 model

Aiming at the problem that the algorithm of rice feature extraction and classification is difficult to determine, this paper improves the LeNet-5 convolutional neural network model and studies its performance on rice sorting. In this paper, the rice original image was preprocessed and the image of single grain rice was extracted to establish a rice image sample database. Then the original LeNet-5 model was improved and tested. Finally, the improved model and several traditional classification methods, 3 lightweight convolutional neural network models were compared. The improved LeNet-5 model has a rice shape selection accuracy of 97.2%, a color selection accuracy of 90.6%, and a processing speed of about 5300 particles·s^-1. The experimental results show that the improved LeNet-5 model can efficiently sort broken rice and chalky rice, and can effectively reduce the workload of preparation before actual sorting.