Research On The Method Of Rice Appearance Quality Detection Based On Machine Learning

China's annual rice production is huge.With the development of computer technology,the application of machine learning in rice detection is more and more widely.Compared with traditional manual detection technology,it is objective and scientific,which is an inevitable trend in the development of automated classification.At present,the rapid detection technology of rice appearance quality in China has achieved good results,but the existing research results are still a long way from the practical application in agricultural production.Therefore,research on rice appearance quality detection technology based on Machine Learning has high theoretical value and practical significance.This article was based on the Sichuan Science and Technology Project "Prototype Design,Development and Application of Rice Quality Inspection System Based on Non-contact Sensing and Big Data Analysis",the following research was carried out:Firstly,the rice image detection system was introduced,including the hardware system,software environment,rice selection and classification regulations.The rice image was processed by grayscale processing,image noise processing,background segmentation and image marking processing.The simulation results were analyzed and the preprocessing algorithm suitable for this paper was selected.Secondly,the method based on BP Neural Network was used to detect the appearance quality of rice,including the quality classification of perfect grain,chalky grain,yellow grain and broken grain.The area,length,width,aspect ratio,hue mean and chalkiness were selected as the input parameters of the network,and tests had verified that they were reasonable and effective as the input data of the neural network.After the training was completed,the average recognition accuracy of BP network after training could reach 91.8%.Finally,the method of rice appearance quality detection based on Convolutional Neural Network was studied,that was,off-line detection was used to detect the appearance quality of rice on mobile devices.Firstly,select a suitable lightweight neural network and optimize the model by pruning the network.Secondly,use the self-built rice data set to train the network on the PC and test the network accuracy.Finally,the network was transplanted to the Android mobile phone to complete the applicationdevelopment.The experimental results showed that the average detection accuracy of the mobile phone was 85%,and the detection results could meet the needs.