Research And Implementation Of Leaf Vegetable Quality Recognition System Based On Machine Vision

Since the 21st century,artificial intelligence has developed rapidly.In view of the low efficiency and accuracy of traditional vegetable sorting equipment,this study attempts to take machine vision technology as a non-destructive testing method,non overlapping leafy vegetables as the research object,on the basis of a large number of experiments and work,analyze the potted leafy vegetables,and realize the effective discrimination and prediction of the quality of the ready-made potted leafy vegetables.The main research methods and contents of this paper are as follows:1.By analyzing the color and morphology of potted cabbage,the kinetic model of color and morphological changes of potted cabbage was established.By comparing sensory evaluation and multivariate dynamics evaluation model,it was concluded that the effects overlapped into pots.The characteristic factors of leaf quality were analyzed,and the chlorophyll content and characteristic variables of potted plants were analyzed by regression analysis.2.Through image acquisition,image processing,color feature extraction,the moving target detection and extraction results of the potted cabbage are obtained,and the image segmentation and the acquisition of the region of interest are futher performed.3.Using the image processing related technology,extracting the morphological features,leaf area features and color features of the leafy image of the interest area obtained in the previous section,selecting the correlation coefficient of the training set,the correlation coefficient of the prediction set,the root mean square error of the training set,and the test set.The root mean square error is the model evaluation index.The CNN deep learning neural network model and other prediction methods are used to quantitatively predict chlorophyll,thereby discriminating the grade of leafy vegetables.4.The detection fusion method based on machine vision technology is applied to the detection of pottery quality to achieve better detection results.Design a machine vision and sorting hardware system suitable for image collection of potted leaves to achieve automated and quality identification and sorting of potted vegetables.The convolution neural network is used in the study of leaf vegetable classification,which not only saves cost and resources,but also improves the accuracy of machine automatic classification,and greatly improves production efficiency and product quality.This study also provides the data base for the research and development of non-destructive testing equipment for the quality of potted leafy vegetables,which is of great significance to improve the automation level of the processing industry of leafy vegetables in China and develop the intelligent sorting system applied in the field of agricultural vegetables in China.