A Method of Rice Seed Classification Based on Deep Learning
Rice is an important cereal crops,and its seed quality,especially its purity,directly affects the yield and quality of rice.Traditional seed classification methods mainly rely on artificial vision,which is inefficient and has a high error rate.In order to improve the accuracy and efficiency of rice seed classification,a method of rice seed classification based on deep learning was proposed.In this study,a dataset containing 80,000 rice seed images of five different rice varieties was self-captured and constructed.The method first used convolutional neural network(CNN)to extract features from rice seed images,and then used self-built RiceFastNet to classify the extracted features.The results showed that this method still achieved a classification accuracy of over 96％when identifying rice seeds of different varieties with high appearance similarity,which is superior to traditional seed classification methods.The new method established in this study has the potential and advantages in improving the accuracy of rice seed detection.
万方数据
维普
