ass日本风韵熟妇pics男人扒开女人屁屁桶到爽|扒开胸露出奶头亲吻视频|邻居少妇的诱惑|人人妻在线播放|日日摸夜夜摸狠狠摸婷婷|制服 丝袜 人妻|激情熟妇中文字幕|看黄色欧美特一级|日本av人妻系列|高潮对白av,丰满岳妇乱熟妇之荡,日本丰满熟妇乱又伦,日韩欧美一区二区三区在线

To address problems of low mechanization level and labor-intensive sorting tasks before packaging of hydroponic lettuce, an automatic sorting system for abnormal hydroponic lettuce was designed in combination with the deep learning method. The automatic sorting system was composed of an information perception sub-system, an information processing sub-system, and a sorting action execution sub-system. Hydroponic lettuce classification was based on the difference between abnormal and normal leaves. Three cameras from bottom to top were used to capture images. Real-time processing of hydroponic lettuce images was realized based on semantic segmentation DeepLabV3+. The image segmentation model had mIoU of 83.26%, PA of 99.24% and image processing velocity of (193.4±4)ms/frame. To realize sorting of abnormal hydroponic lettuce, a bracket-type hydroponic lettuce sorting sub-system was designed based on phenotype and harvesting mode of the hydroponic lettuce. Quadratic orthogonal rotational-combinational experiments were designed. Experiments on factoring in horizontal and longitudinal support rod angles and stepping motor speed were conducted to obtain the highest sorting success rate. Regression mathematical models between factors and index were multi-objectively optimized by using Design-Expert software. Optimal combination of parameters was obtained, including the horizontal support rod angle of 146°, the longitudinal support angle of 150°, and the stepping motor speed of 11r/min. Perform test was carried out according to the optimal combination of parameters. The sorting success rate of the sorting action execution sub-system was 98%, and the sorting success rate of the abnormal hydroponic lettuce automatic sorting system was 95%, which met technical standard requirements of lettuce refrigerated transportation.