实验室管理系统旨在提供全面的解决方案,以管理实验室的设备、仪表、材料、实验项目、研究人员和学生等重要信息。实验室管理系统不仅简化了实验室资源的跟踪和管理,而且增强了实验项目的组织和监控,从而确保研究过程的透明度和数据的...实验室管理系统旨在提供全面的解决方案,以管理实验室的设备、仪表、材料、实验项目、研究人员和学生等重要信息。实验室管理系统不仅简化了实验室资源的跟踪和管理,而且增强了实验项目的组织和监控,从而确保研究过程的透明度和数据的完整性。该系统采用Java技术栈,包括SpringBoot、Spring Security和Spring Data JPA,作为后端开发框架,结合HTML、CSS、JavaScript以及React作为前端技术,构建了一个用户友好且功能强大的实验室管理平台。通过数据库设计和优化,系统实现了实验室资源的高效管理、实验项目的追踪和评估、用户权限的灵活控制以及数据的可视化分析。展开更多
Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them reliably.Unknown plant species can be challenging to classify and segment using deep learning.Whi...Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them reliably.Unknown plant species can be challenging to classify and segment using deep learning.While using deep learning architectures has helped improve classification accuracy,the resulting models often need to be more flexible and require a large dataset to train.For the sake of taxonomy,this research proposes a hybrid method for categorizing guava,potato,and java plumleaves.Two new approaches are used to formthe hybridmodel suggested here.The guava,potato,and java plum plant species have been successfully segmented using the first model built on the MobileNetV2-UNET architecture.As a second model,we use a Plant Species Detection Stacking Ensemble Deep Learning Model(PSD-SE-DLM)to identify potatoes,java plums,and guava.The proposed models were trained using data collected in Punjab,Pakistan,consisting of images of healthy and sick leaves from guava,java plum,and potatoes.These datasets are known as PLSD and PLSSD.Accuracy levels of 99.84%and 96.38%were achieved for the suggested PSD-SE-DLM and MobileNetV2-UNET models,respectively.展开更多
摘要实验室管理系统旨在提供全面的解决方案,以管理实验室的设备、仪表、材料、实验项目、研究人员和学生等重要信息。实验室管理系统不仅简化了实验室资源的跟踪和管理,而且增强了实验项目的组织和监控,从而确保研究过程的透明度和数据的完整性。该系统采用Java技术栈,包括SpringBoot、Spring Security和Spring Data JPA,作为后端开发框架,结合HTML、CSS、JavaScript以及React作为前端技术,构建了一个用户友好且功能强大的实验室管理平台。通过数据库设计和优化,系统实现了实验室资源的高效管理、实验项目的追踪和评估、用户权限的灵活控制以及数据的可视化分析。
基金funding this work through the Research Group Program under the Grant Number:(R.G.P.2/382/44).
摘要Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them reliably.Unknown plant species can be challenging to classify and segment using deep learning.While using deep learning architectures has helped improve classification accuracy,the resulting models often need to be more flexible and require a large dataset to train.For the sake of taxonomy,this research proposes a hybrid method for categorizing guava,potato,and java plumleaves.Two new approaches are used to formthe hybridmodel suggested here.The guava,potato,and java plum plant species have been successfully segmented using the first model built on the MobileNetV2-UNET architecture.As a second model,we use a Plant Species Detection Stacking Ensemble Deep Learning Model(PSD-SE-DLM)to identify potatoes,java plums,and guava.The proposed models were trained using data collected in Punjab,Pakistan,consisting of images of healthy and sick leaves from guava,java plum,and potatoes.These datasets are known as PLSD and PLSSD.Accuracy levels of 99.84%and 96.38%were achieved for the suggested PSD-SE-DLM and MobileNetV2-UNET models,respectively.