Crop phenotyping plays a critical role in precision agriculture by enabling the accurate assessment of plant traits,supporting improved crop management,breeding programs,and yield optimization.However,cowpea leaves pr...Crop phenotyping plays a critical role in precision agriculture by enabling the accurate assessment of plant traits,supporting improved crop management,breeding programs,and yield optimization.However,cowpea leaves present unique challenges for automated phenotyping due to their diverse shapes,complex vein structures,and variations caused by environmental conditions.This research presents a deep learning-based approach for the classification of cowpea leaf images to support crop phenotyping tasks.Given the limited availability of annotated datasets,data augmentation techniques were employed to artificially expand the original small dataset while preserving essential leaf characteristics.Various image processing methods were applied to enrich the dataset,ensuring better feature representation without significant information loss.A deep neural network,specifically the MobileNet architecture,was utilized for its efficiency in capturing multi-scale features and handling image data with limited computational resources.The performance of the model trained on the augmented dataset was evaluated,achieving an accuracy of 94.12%on the cowpea leaf classification task.These results demonstrate the effectiveness of data augmentation in enhancing model generalization and learning capabilities.展开更多
为了提高钢结构工程质量管理的智能化水平,论文提出了一种基于物联网、大数据分析和人工智能技术的智能监督系统。论文将提出的优化系统工具与现有的两种结构健康监测工具SHMTools(Structural Health Monitoring Tools)和mFUSE(WATLAB F...为了提高钢结构工程质量管理的智能化水平,论文提出了一种基于物联网、大数据分析和人工智能技术的智能监督系统。论文将提出的优化系统工具与现有的两种结构健康监测工具SHMTools(Structural Health Monitoring Tools)和mFUSE(WATLAB Function Sequencer)进行了对比并开展了不同数据量下的实验研究。对比实验结果如下:在预警响应时间方面,优化系统的响应时间从1000数据量的0.65 s提升到3000数据量的0.69 s;在反馈处理效率方面,优化系统从1000数据量的84.20%提升到3000数据量的88.50%,而SHMTools和mFUSE系统分别为75.32%~78.15%和78.65%~81.55%;在系统稳定性方面,优化系统在3000数据量下依然保持96.55%的高稳定性;在资源利用率方面,优化系统从1000数据量的82.45%下降到3000数据量的79.10%。本研究在钢结构工程质量监控智能化方面进行创新探索,为钢结构工程质量和安全管理提供更可靠的技术支持。展开更多
文摘Crop phenotyping plays a critical role in precision agriculture by enabling the accurate assessment of plant traits,supporting improved crop management,breeding programs,and yield optimization.However,cowpea leaves present unique challenges for automated phenotyping due to their diverse shapes,complex vein structures,and variations caused by environmental conditions.This research presents a deep learning-based approach for the classification of cowpea leaf images to support crop phenotyping tasks.Given the limited availability of annotated datasets,data augmentation techniques were employed to artificially expand the original small dataset while preserving essential leaf characteristics.Various image processing methods were applied to enrich the dataset,ensuring better feature representation without significant information loss.A deep neural network,specifically the MobileNet architecture,was utilized for its efficiency in capturing multi-scale features and handling image data with limited computational resources.The performance of the model trained on the augmented dataset was evaluated,achieving an accuracy of 94.12%on the cowpea leaf classification task.These results demonstrate the effectiveness of data augmentation in enhancing model generalization and learning capabilities.
文摘为了提高钢结构工程质量管理的智能化水平,论文提出了一种基于物联网、大数据分析和人工智能技术的智能监督系统。论文将提出的优化系统工具与现有的两种结构健康监测工具SHMTools(Structural Health Monitoring Tools)和mFUSE(WATLAB Function Sequencer)进行了对比并开展了不同数据量下的实验研究。对比实验结果如下:在预警响应时间方面,优化系统的响应时间从1000数据量的0.65 s提升到3000数据量的0.69 s;在反馈处理效率方面,优化系统从1000数据量的84.20%提升到3000数据量的88.50%,而SHMTools和mFUSE系统分别为75.32%~78.15%和78.65%~81.55%;在系统稳定性方面,优化系统在3000数据量下依然保持96.55%的高稳定性;在资源利用率方面,优化系统从1000数据量的82.45%下降到3000数据量的79.10%。本研究在钢结构工程质量监控智能化方面进行创新探索,为钢结构工程质量和安全管理提供更可靠的技术支持。
