Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often...Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often handpicked and need more delicate operations in intelligent picking machines.Compared with traditional image processing techniques,deep learning models have stronger feature extraction capabilities,and better generalization and are more suitable for practical tea shoot harvesting.However,current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks.We propose a tea shoot instance segmentation model based on multi-scale mixed attention(Mask2FusionNet)using a dataset from the tea garden in Hangzhou.We further analyzed the characteristics of the tea shoot dataset,where the proportion of small to medium-sized targets is 89.9%.Our algorithm is compared with several mainstream object segmentation algorithms,and the results demonstrate that our model achieves an accuracy of 82%in recognizing the tea shoots,showing a better performance compared to other models.Through ablation experiments,we found that ResNet50,PointRend strategy,and the Feature Pyramid Network(FPN)architecture can improve performance by 1.6%,1.4%,and 2.4%,respectively.These experiments demonstrated that our proposed multi-scale and point selection strategy optimizes the feature extraction capability for overlapping small targets.The results indicate that the proposed Mask2FusionNet model can perform the shoot segmentation in unstructured environments,realizing the individual distinction of tea shoots,and complete extraction of the shoot edge contours with a segmentation accuracy of 82.0%.The research results can provide algorithmic support for the segmentation and intelligent harvesting of premium tea shoots at different scales.展开更多
近年来,集体食品安全事件频频发生,公众对食品安全的关注度持续升高。营区食堂作为负责部队后勤保障和营养供给的内部机构,维系着军人的体质健康和营养均衡,因此保障营区食堂的食品安全显得尤为重要和迫切。本文通过在营区食堂建立和实...近年来,集体食品安全事件频频发生,公众对食品安全的关注度持续升高。营区食堂作为负责部队后勤保障和营养供给的内部机构,维系着军人的体质健康和营养均衡,因此保障营区食堂的食品安全显得尤为重要和迫切。本文通过在营区食堂建立和实施危害分析与关键控制点(Hazard Analysis and Critical Control Point,HACCP)体系,分析营区食堂餐食生产各工序中的食品安全危害,确定关键控制点和关键限值,并建立HACCP计划表,以期为营区食堂的食品安全管理提供参考。此外,HACCP计划的实施离不开日常监督检查,完善其检查机制将会对食品安全管理产生积极的效应,具有一定的借鉴意义。展开更多
基金This research was supported by the National Natural Science Foundation of China No.62276086the National Key R&D Program of China No.2022YFD2000100Zhejiang Provincial Natural Science Foundation of China under Grant No.LTGN23D010002.
文摘Tea leaf picking is a crucial stage in tea production that directly influences the quality and value of the tea.Traditional tea-picking machines may compromise the quality of the tea leaves.High-quality teas are often handpicked and need more delicate operations in intelligent picking machines.Compared with traditional image processing techniques,deep learning models have stronger feature extraction capabilities,and better generalization and are more suitable for practical tea shoot harvesting.However,current research mostly focuses on shoot detection and cannot directly accomplish end-to-end shoot segmentation tasks.We propose a tea shoot instance segmentation model based on multi-scale mixed attention(Mask2FusionNet)using a dataset from the tea garden in Hangzhou.We further analyzed the characteristics of the tea shoot dataset,where the proportion of small to medium-sized targets is 89.9%.Our algorithm is compared with several mainstream object segmentation algorithms,and the results demonstrate that our model achieves an accuracy of 82%in recognizing the tea shoots,showing a better performance compared to other models.Through ablation experiments,we found that ResNet50,PointRend strategy,and the Feature Pyramid Network(FPN)architecture can improve performance by 1.6%,1.4%,and 2.4%,respectively.These experiments demonstrated that our proposed multi-scale and point selection strategy optimizes the feature extraction capability for overlapping small targets.The results indicate that the proposed Mask2FusionNet model can perform the shoot segmentation in unstructured environments,realizing the individual distinction of tea shoots,and complete extraction of the shoot edge contours with a segmentation accuracy of 82.0%.The research results can provide algorithmic support for the segmentation and intelligent harvesting of premium tea shoots at different scales.
文摘近年来,集体食品安全事件频频发生,公众对食品安全的关注度持续升高。营区食堂作为负责部队后勤保障和营养供给的内部机构,维系着军人的体质健康和营养均衡,因此保障营区食堂的食品安全显得尤为重要和迫切。本文通过在营区食堂建立和实施危害分析与关键控制点(Hazard Analysis and Critical Control Point,HACCP)体系,分析营区食堂餐食生产各工序中的食品安全危害,确定关键控制点和关键限值,并建立HACCP计划表,以期为营区食堂的食品安全管理提供参考。此外,HACCP计划的实施离不开日常监督检查,完善其检查机制将会对食品安全管理产生积极的效应,具有一定的借鉴意义。
