Non-destructive Detection And Grading Of The Quality Of Red Grape Strings Based On Visual Technology

Grapes are rich in nutritional value and are known as the top of the world's four major fruits.China is a major grape producer in the world.Grapes are spike-shaped fruits,and their firmness,maturity,and damage have a great impact on the growth,grading and sales of grapes.At present,the domestic grading methods for grapes mainly rely on the manual grading of fruit growers.The labor cost is high,the grading time is long,the grading standards are not uniform,and the grading effects are uneven.Therefore,the grape industry urgently needs a technical method of real-time intelligent detection and classification.The degree of maturity reflects the taste and sweetness of the grapes,the degree of damage represents the integrity of the grape clusters,and the firmness reflects the appearance of the grape clusters.In this paper,red skewer is the research object,combined with two research methods of machine vision and deep learning,the compactness,maturity and damage detection grading models of red earth grapes are established respectively,and the red earth grapes is comprehensively classified according to actual needs.,Import the grading model into Android phones to facilitate fruit farmers and consumers to perform more accurate and objective grading and identification of red earth grapes.The specific research content is as follows:(1)Set up and improve the test platform of Hongti image acquisition.Adjusted and tested the light source of the red earth grapes collection equipment.The original red earth grapes image acquisition device light source is located on both sides of the shooting window.Because of the need to form a unique light spot on the surface of the red earth grapes during the later processing of the red earth grapes.Therefore,the light source model and the relative position of the light source and the camera are adjusted to ensure the post-image shooting effect.(2)A classification model for the compactness of red earth grapes strings based on machine learning is established.Use integrated image acquisition equipment to take multi-directional shooting of red earth grapes stalks,extract the area parameters of red earth grapes stalks,the sum of distances between red earth grapes grains and the centroid of red earth grapes,and other characteristic parameters,through linear discriminant analysis,integrated learning algorithm and support The three methods of vector machine have established different discriminant models for the compactness of the red earth grapes string.The model based on the support vector machine is the best,and the discriminant accuracy rate reaches 94.6%.(3)Different classification models of compactness,maturity and damage of red earth grapes bunches based on deep learning have been established.By comparing the test results of the Mobile Net V3?large and YOLO V5 m models established for the three qualities,it is found that the YOLO V5 m model is slightly inferior to the Mobile Net V3?large model in terms of the memory size of the model and the computing speed of the model,but its running speed can fully meet the mobile phone side The need for real-time detection,and the YOLO V5 m model's test results are far better than the Mobile Net V3?large model in terms of model recognition accuracy and recall rate.(4)A comprehensive classification model of red earth grapes bunches based on deep learning is established.Integrating three indicators of compactness,maturity,and damage,a multi-quality simultaneous detection and classification model was established.In order to match the actual production and sales of the orchard,the growth period red earth grapes are classified into three categories: sale,to grow,and weed out,and select the best obtained from compactness,maturity,and damage.Predictive model YOLO V5 model,establish a comprehensive classification model,this model is far stronger than other models in flexibility and speed,and has obvious advantages in the rapid deployment of the model.(5)Realize the import and recognition of the Android mobile phone of the comprehensive classification model of red earth grapes bunch.The comprehensive classification model is transplanted to Android phones,and the portable mobile phone is used to realize the real-time detection and classification of red earth grapes.The final effect is that when the mobile phone shoots the red earth grapes,the detection positioning frame will appear in the video,and the identification and classification will be performed.The result is displayed in the upper left corner of the positioning box in real time,and the result is straightforward.It is conducive to fruit farmers and consumers in the red earth grapes harvesting period according to the objective and unified classification standards for harvesting,and it also simplifies the consumer's selection mode of red earth grapes.