Effects of two kinds of magnesium compound with fertilizer on Daylily （Hemerocallis citrina Baroni） growth, yield, and soil nutrients in red soil were studied. The results indicated that significant effects of magne...Effects of two kinds of magnesium compound with fertilizer on Daylily （Hemerocallis citrina Baroni） growth, yield, and soil nutrients in red soil were studied. The results indicated that significant effects of magnesium applied to soils were observed in increasing Daylily （Hemerocallis citrina Baroni） yield, improving its growth, and strengthening its antivirus property as well as increasing the amount of exchangeable Mg, N, P, and K in red soil. In particular, the effects of magnesium compound fertilizer Ⅱ （MCF2） with higher Mg content were better than that of the others, which increased Daylily （Hemerocallis citrina Baroni） yield by 57.4, 32.8, and 14.5% compared to that of control treatment （CK）, chemical fertilizer with nitrogen, phosphorus, potassium treatment （CF）, and magnesium compound fertilizer Ⅰ treatment （MCF1） with lower Mg content. It increased soil Alkali N, available P, exchangeable K, and exchangeable Mg by 94.9, 46.5, 31.1, and 35.3%, respectively, compared with that of CK treatment. Therefore, the application of magnesium compound with fertilizer is an optimum method for improving red soil quality.展开更多
Hemerocallis citrina Baroni is rich in nutritional value,with a clear trend of increasing market demand,and it is a pillar industry for rural economic development.Hemerocallis citrina Baroni exhibits rapid growth,a sh...Hemerocallis citrina Baroni is rich in nutritional value,with a clear trend of increasing market demand,and it is a pillar industry for rural economic development.Hemerocallis citrina Baroni exhibits rapid growth,a shortened harvest cycle,lacks a consistent maturity identification standard,and relies heavily on manual labor.To address these issues,a new method for detecting the maturity of Hemerocallis citrina Baroni,called LTCB YOLOv7,has been introduced.To begin with,the layer aggregation network and transition module are made more efficient through the incorporation of Ghost convolution,a lightweight technique that streamlines the model architecture.This results in a reduction of model parameters and computational workload.Second,a coordinate attention mechanism is enhanced between the feature extraction and feature fusion networks,which enhances the model precision and compensates for the performance decline caused by lightweight design.Ultimately,a bi-directional feature pyramid network with weighted connections replaces the Concatenate function in the feature fusion network.This modification enables the integration of information across different stages,resulting in a gradual improvement in the overall model performance.The experimental results show that the improved LTCB YOLOv7 algorithm for Hemerocallis citrina Baroni maturity detection reduces the number of model parameters and floating point operations by about 1.7 million and 7.3G,respectively,and the model volume is compressed by about 3.5M.This refinement leads to enhancements in precision and recall by approximately 0.58%and 0.18%respectively,while the average precision metrics mAP@0.5 and mAP@0.5:0.95 show improvements of about 1.61%and 0.82%respectively.Furthermore,the algorithm achieves a real-time detection performance of 96.15 FPS.The proposed LTCB YOLOv7 algorithm exhibits strong performance in detecting maturity in Hemerocallis citrina Baroni,effectively addressing the challenge of balancing model complexity and performance.It also establishes a standardized approach for maturity detection in Hemerocallis citrina Baroni for identification and harvesting purposes.展开更多
文摘Effects of two kinds of magnesium compound with fertilizer on Daylily （Hemerocallis citrina Baroni） growth, yield, and soil nutrients in red soil were studied. The results indicated that significant effects of magnesium applied to soils were observed in increasing Daylily （Hemerocallis citrina Baroni） yield, improving its growth, and strengthening its antivirus property as well as increasing the amount of exchangeable Mg, N, P, and K in red soil. In particular, the effects of magnesium compound fertilizer Ⅱ （MCF2） with higher Mg content were better than that of the others, which increased Daylily （Hemerocallis citrina Baroni） yield by 57.4, 32.8, and 14.5% compared to that of control treatment （CK）, chemical fertilizer with nitrogen, phosphorus, potassium treatment （CF）, and magnesium compound fertilizer Ⅰ treatment （MCF1） with lower Mg content. It increased soil Alkali N, available P, exchangeable K, and exchangeable Mg by 94.9, 46.5, 31.1, and 35.3%, respectively, compared with that of CK treatment. Therefore, the application of magnesium compound with fertilizer is an optimum method for improving red soil quality.
基金funded by the Shanxi Provincial Science and Technology Department Surface Project(Grant No.202303021211330)Innovation Platform Project of Science and Technology Innovation Program of Higher Education Institutions in Shanxi Province(Grant No.2022P009)+2 种基金Shanxi Province Basic Research Program Projects(Grant No.202303021212244)the Datong City Shanxi Province Key Research&Development(Agriculture)Program Projects(Grants No.2023006,2023015)the 2024 Basic Research Program of Shanxi Province(Free Exploration Category)Program Projects(Grant No.202403021221181).
文摘Hemerocallis citrina Baroni is rich in nutritional value,with a clear trend of increasing market demand,and it is a pillar industry for rural economic development.Hemerocallis citrina Baroni exhibits rapid growth,a shortened harvest cycle,lacks a consistent maturity identification standard,and relies heavily on manual labor.To address these issues,a new method for detecting the maturity of Hemerocallis citrina Baroni,called LTCB YOLOv7,has been introduced.To begin with,the layer aggregation network and transition module are made more efficient through the incorporation of Ghost convolution,a lightweight technique that streamlines the model architecture.This results in a reduction of model parameters and computational workload.Second,a coordinate attention mechanism is enhanced between the feature extraction and feature fusion networks,which enhances the model precision and compensates for the performance decline caused by lightweight design.Ultimately,a bi-directional feature pyramid network with weighted connections replaces the Concatenate function in the feature fusion network.This modification enables the integration of information across different stages,resulting in a gradual improvement in the overall model performance.The experimental results show that the improved LTCB YOLOv7 algorithm for Hemerocallis citrina Baroni maturity detection reduces the number of model parameters and floating point operations by about 1.7 million and 7.3G,respectively,and the model volume is compressed by about 3.5M.This refinement leads to enhancements in precision and recall by approximately 0.58%and 0.18%respectively,while the average precision metrics mAP@0.5 and mAP@0.5:0.95 show improvements of about 1.61%and 0.82%respectively.Furthermore,the algorithm achieves a real-time detection performance of 96.15 FPS.The proposed LTCB YOLOv7 algorithm exhibits strong performance in detecting maturity in Hemerocallis citrina Baroni,effectively addressing the challenge of balancing model complexity and performance.It also establishes a standardized approach for maturity detection in Hemerocallis citrina Baroni for identification and harvesting purposes.
