Grape embryo rescue technology is currently the primary method for breeding new seedless grape cultivars.The timing of berry sampling directly impacts the efficacy of this technique.Therefore,achieving efficient,accur...Grape embryo rescue technology is currently the primary method for breeding new seedless grape cultivars.The timing of berry sampling directly impacts the efficacy of this technique.Therefore,achieving efficient,accurate,and non-destructive determination of the optimal sampling time for seedless grape embryo rescue breeding has long been a challenge.This study collected near-infrared spectral data and data on 19 physiological indicators from 2940 grape berries of six grape cultivars at six sampling times to construct a baseline dataset.Remarkably,it was discovered for the first time that pericarp puncture hardness(PPH)is closely associated with the embryo development rate of seedless grape.Subsequently,the optimal sampling times for'Flame Seedless','Ruby Seed-less',and'Jingzaojing'were determined when their PPH reached 720±20 g,990±20 g and 633±20 g,respectively.Then,a total of 840 models for PPH recognition were established and assessed based on their co-efficient of determination(R^(2))and root mean square error(RMSE).The optimal recognition models for three seedless grape cultivars suitable for embryo rescue—'Flame Seedless','Ruby Seedless',and'Jingzaojing'-were identified as follows:D1+PLSR(R^(2)=0.94,RMSE=42.26),D1+MLR(R^(2)=0.79,RMSE=66.31)and D1+PLSR(R^(2)=0.93,RMSE=47.9).Utilizing the established D1+PLSR or D1+MLR models for PPH,a non-destructive and precise method for sampling seedless grapes during embryo rescue was introduced for the first time.This approach led to a notable increase in the embryo development rate by 15%and enhanced the plantlet rate by 14%.Overall,our proposed strategy provides new perspectives for accelerating the breeding process of new seedless grape cultivars.展开更多
To overcome the limitations of traditional dairy cow's rumination detection methods,a video-based analysis on the intelligent monitoring method of cow ruminant behavior was proposed in this study.The Mean Shift al...To overcome the limitations of traditional dairy cow's rumination detection methods,a video-based analysis on the intelligent monitoring method of cow ruminant behavior was proposed in this study.The Mean Shift algorithm was used to track the jaw motion of dairy cows accurately.The centroid trajectory curve of the cow mouth motion was subsequently extracted from the video.In this way,the monitoring of the ruminant behavior of dairy cows was realized.To verify the accuracy of the method,six videos,a total of 99'00",24000 frames were selected.The test results demonstrated that the success rate of this method was 92.03%,despite the interference of behaviors,such as raising or turning of the cow’s head.The results demonstrate that this method,which monitors the ruminant behavior of dairy cows,is effective and feasible.展开更多
基金This work was supported by Shaanxi Province Key Research and Development Plan(2023-YBNY-080)Xi'an Agricultural Technology Research and Development Project(24NYGG0031)the China Agriculture Research System of MOF and MARA(CARS-29-yc-3).
文摘Grape embryo rescue technology is currently the primary method for breeding new seedless grape cultivars.The timing of berry sampling directly impacts the efficacy of this technique.Therefore,achieving efficient,accurate,and non-destructive determination of the optimal sampling time for seedless grape embryo rescue breeding has long been a challenge.This study collected near-infrared spectral data and data on 19 physiological indicators from 2940 grape berries of six grape cultivars at six sampling times to construct a baseline dataset.Remarkably,it was discovered for the first time that pericarp puncture hardness(PPH)is closely associated with the embryo development rate of seedless grape.Subsequently,the optimal sampling times for'Flame Seedless','Ruby Seed-less',and'Jingzaojing'were determined when their PPH reached 720±20 g,990±20 g and 633±20 g,respectively.Then,a total of 840 models for PPH recognition were established and assessed based on their co-efficient of determination(R^(2))and root mean square error(RMSE).The optimal recognition models for three seedless grape cultivars suitable for embryo rescue—'Flame Seedless','Ruby Seedless',and'Jingzaojing'-were identified as follows:D1+PLSR(R^(2)=0.94,RMSE=42.26),D1+MLR(R^(2)=0.79,RMSE=66.31)and D1+PLSR(R^(2)=0.93,RMSE=47.9).Utilizing the established D1+PLSR or D1+MLR models for PPH,a non-destructive and precise method for sampling seedless grapes during embryo rescue was introduced for the first time.This approach led to a notable increase in the embryo development rate by 15%and enhanced the plantlet rate by 14%.Overall,our proposed strategy provides new perspectives for accelerating the breeding process of new seedless grape cultivars.
基金supported by the National Key Technology R&D Program of China(No.2017YFD0701603)the Natural Science Foundation of China(No.60975007).
文摘To overcome the limitations of traditional dairy cow's rumination detection methods,a video-based analysis on the intelligent monitoring method of cow ruminant behavior was proposed in this study.The Mean Shift algorithm was used to track the jaw motion of dairy cows accurately.The centroid trajectory curve of the cow mouth motion was subsequently extracted from the video.In this way,the monitoring of the ruminant behavior of dairy cows was realized.To verify the accuracy of the method,six videos,a total of 99'00",24000 frames were selected.The test results demonstrated that the success rate of this method was 92.03%,despite the interference of behaviors,such as raising or turning of the cow’s head.The results demonstrate that this method,which monitors the ruminant behavior of dairy cows,is effective and feasible.
