Machine picking in cotton is an emerging practice in India,to solve the problems of labour shortages and production costs increasing.Cotton production has been declining in recent years;however,the high density planti...Machine picking in cotton is an emerging practice in India,to solve the problems of labour shortages and production costs increasing.Cotton production has been declining in recent years;however,the high density planting system(HDPS)offers a viable method to enhance productivity by increasing plant populations per unit area,optimizing resource utilization,and facilitating machine picking.Cotton is an indeterminate plant that produce excessive vegeta-tive growth in favorable soil fertility and moisture conditions,which posing challenges for efficient machine picking.To address this issue,the application of plant growth retardants(PGRs)is essential for controlling canopy architecture.PGRs reduce internode elongation,promote regulated branching,and increase plant compactness,making cotton plants better suited for machine picking.PGRs application also optimizes photosynthates distribution between veg-etative and reproductive growth,resulting in higher yields and improved fibre quality.The integration of HDPS and PGRs applications results in an optimal plant architecture for improving machine picking efficiency.However,the success of this integration is determined by some factors,including cotton variety,environmental conditions,and geographical variations.These approaches not only address yield stagnation and labour shortages but also help to establish more effective and sustainable cotton farming practices,resulting in higher cotton productivity.展开更多
In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-base...In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.展开更多
This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy ...This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy reliance on extensive manually annotated datasets for training deep learning models still poses significant limitations to their application in real-world agricultural production environments.To overcome these limitations,we employed domain adaptive learning approach combined with the YOLOv5 model to develop a novel tomato detection model called as TDA-YOLO(tomato detection domain adaptation).We designated the normal illumination scenes in dense planting environments as the source domain and utilized various other illumination scenes as the target domain.To construct bridge mechanism between source and target domains,neural preset for color style transfer is introduced to generate a pseudo-dataset,which served to deal with domain discrepancy.Furthermore,this study combines the semi-supervised learning method to enable the model to extract domain-invariant features more fully,and uses knowledge distillation to improve the model's ability to adapt to the target domain.Additionally,for purpose of promoting inference speed and low computational demand,the lightweight FasterNet network was integrated into the YOLOv5's C3 module,creating a modified C3_Faster module.The experimental results demonstrated that the proposed TDA-YOLO model significantly outperformed original YOLOv5s model,achieving a mAP(mean average precision)of 96.80%for tomato detection across diverse scenarios in dense planting environments,increasing by 7.19 percentage points;Compared with the latest YOLOv8 and YOLOv9,it is also 2.17 and 1.19 percentage points higher,respectively.The model's average detection time per image was an impressive 15 milliseconds,with a FLOPs(floating point operations per second)count of 13.8 G.After acceleration processing,the detection accuracy of the TDA-YOLO model on the Jetson Xavier NX development board is 90.95%,the mAP value is 91.35%,and the detection time of each image is 21 ms,which can still meet the requirements of real-time detection of tomatoes in dense planting environment.The experimental results show that the proposed TDA-YOLO model can accurately and quickly detect tomatoes in dense planting environment,and at the same time avoid the use of a large number of annotated data,which provides technical support for the development of automatic harvesting systems for tomatoes and other fruits.展开更多
Lessons learned from past experiences push for an alternate way of crop production.In India,adopting high density planting system(HDPS)to boost cotton yield is becoming a growing trend.HDPS has recently been considere...Lessons learned from past experiences push for an alternate way of crop production.In India,adopting high density planting system(HDPS)to boost cotton yield is becoming a growing trend.HDPS has recently been considered a replacement for the current Indian production system.It is also suitable for mechanical harvesting,which reducing labour costs,increasing input use efficiency,timely harvesting timely,maintaining cotton quality,and offering the potential to increase productivity and profitability.This technology has become widespread in globally cotton growing regions.Water management is critical for the success of high density cotton planting.Due to the problem of freshwater availability,more crops should be produced per drop of water.In the high-density planting system,optimum water application is essential to control excessive vegetative growth and improve the translocation of photoassimilates to reproductive organs.Deficit irrigation is a tool to save water without compromising yield.At the same time,it consumes less water than the normal evapotranspiration of crops.This review comprehensively documents the importance of growing cotton under a high-density planting system with deficit irrigation.Based on the current research and combined with cotton production reality,this review discusses the application and future development of deficit irrigation,which may provide theoretical guidance for the sustainable advancement of cotton planting systems.展开更多
Background Plant hormones profoundly influence cotton growth,development,and responses to various stresses.Therefore,there is a pressing need for an efficient assay to quantify these hormones in cotton.In this groundb...Background Plant hormones profoundly influence cotton growth,development,and responses to various stresses.Therefore,there is a pressing need for an efficient assay to quantify these hormones in cotton.In this groundbreaking study,we have established QuEChERS-HPLC‒MS/MS method,for the simultaneous detection of multiple plant hormones in cotton leaves,allowing the analysis and quantification of five key plant hormones.Results Sample extraction and purification employed 0.1%acetic acid in methanol and C18 for optimal recovery of plant hormones.The method applied to cotton demonstrated excellent linearity across a concentration range of 0.05–1 mg・L−1,with linear regression coefficients exceeding 0.99.The limits of quantification(LOQs)were 20μg・kg−1 for GA3 and 5μg・kg−1 for the other four plant hormones.Recovery rates for the five plant hormones matrix spiked at levels of 5,10,100,and 1000μg・kg−1 were in the range of 79.07%to 98.97%,with intraday relative standard deviations(RSDs)ranging from 2.11%to 8.47%.The method was successfully employed to analyze and quantify the five analytes in cotton leaves treated with plant growth regulators.Conclusion The study demonstrates that the method is well-suited for the determination of five plant hormones in cotton.It exhibits excellent selectivity and sensitivity in detecting field samples,thus serving as a robust tool for indepth research into cotton physiology.展开更多
Avian influenza virus is one of the main pathogens causing avian influenza.In this experiment,the avian influenza hemagglutinin(HA)gene was transferred into tobacco by Agrobacterium tumefaciens-mediated method using k...Avian influenza virus is one of the main pathogens causing avian influenza.In this experiment,the avian influenza hemagglutinin(HA)gene was transferred into tobacco by Agrobacterium tumefaciens-mediated method using kanamycin as a resistance marker to produce HA type edible vaccine designed for avian influenza hemagglutinin protein.The fusion of cholera toxin B subgene(CTB)and avian influenza HA was initiated by CaMV35S promoter,and then transformed into Nicotiana benthamiana to induce callus and adventitious bud differentiation,bud elongation,and growth and root induction.The total genomic DNA of 28 transgenic tobacco plants was extracted,and the CTB sequence of the CTB-HA fusion gene was used as a template to design primers for PCR amplification.Eight of them were positive,and four of them were expressed at the RNA level after RNA extraction and RT-PCR amplification.In western blot detection of protein extraction,two strains were shown at the position of the target protein.The results provided material support for the preparation of a transgenic plant oral vaccine against HA infection,and provided a theoretical basis for accelerating the development of a transgenic plant vaccine.展开更多
There are many motors in operation or on standby in nuclear power plants,and the startup of group motors will have a great impact on the voltage of the emergency bus.At present,there is no special or inexpensive softw...There are many motors in operation or on standby in nuclear power plants,and the startup of group motors will have a great impact on the voltage of the emergency bus.At present,there is no special or inexpensive software to solve this problem,and the experience of engineers is not accurate enough.Therefore,this paper developed a method and system for the startup calculation of group motors in nuclear power plants and proposed an automatic generation method of circuit topology in nuclear power plants.Each component in the topology was given its unique number,and the component class could be constructed according to its type and upper and lower connections.The subordination and topology relationship of switches,buses,and motors could be quickly generated by the program according to the component class,and the simplified direct power flow algorithm was used to calculate the power flow for the startup of group motors according to the above relationship.Then,whether the bus voltage is in the safe range and whether the voltage exceeds the limit during the startup of the group motor could be judged.The practical example was used to verify the effectiveness of the method.Compared with other professional software,the method has high efficiency and low cost.展开更多
文摘Machine picking in cotton is an emerging practice in India,to solve the problems of labour shortages and production costs increasing.Cotton production has been declining in recent years;however,the high density planting system(HDPS)offers a viable method to enhance productivity by increasing plant populations per unit area,optimizing resource utilization,and facilitating machine picking.Cotton is an indeterminate plant that produce excessive vegeta-tive growth in favorable soil fertility and moisture conditions,which posing challenges for efficient machine picking.To address this issue,the application of plant growth retardants(PGRs)is essential for controlling canopy architecture.PGRs reduce internode elongation,promote regulated branching,and increase plant compactness,making cotton plants better suited for machine picking.PGRs application also optimizes photosynthates distribution between veg-etative and reproductive growth,resulting in higher yields and improved fibre quality.The integration of HDPS and PGRs applications results in an optimal plant architecture for improving machine picking efficiency.However,the success of this integration is determined by some factors,including cotton variety,environmental conditions,and geographical variations.These approaches not only address yield stagnation and labour shortages but also help to establish more effective and sustainable cotton farming practices,resulting in higher cotton productivity.
基金Shanxi Province Higher Education Science and Technology Innovation Fund Project(2022-676)Shanxi Soft Science Program Research Fund Project(2016041008-6)。
文摘In order to improve the efficiency of cloud-based web services,an improved plant growth simulation algorithm scheduling model.This model first used mathematical methods to describe the relationships between cloud-based web services and the constraints of system resources.Then,a light-induced plant growth simulation algorithm was established.The performance of the algorithm was compared through several plant types,and the best plant model was selected as the setting for the system.Experimental results show that when the number of test cloud-based web services reaches 2048,the model being 2.14 times faster than PSO,2.8 times faster than the ant colony algorithm,2.9 times faster than the bee colony algorithm,and a remarkable 8.38 times faster than the genetic algorithm.
基金The National Natural Science Foundation of China (32371993)The Natural Science Research Key Project of Anhui Provincial University(2022AH040125&2023AH040135)The Key Research and Development Plan of Anhui Province (202204c06020022&2023n06020057)。
文摘This study aimed to address the challenge of accurately and reliably detecting tomatoes in dense planting environments,a critical prerequisite for the automation implementation of robotic harvesting.However,the heavy reliance on extensive manually annotated datasets for training deep learning models still poses significant limitations to their application in real-world agricultural production environments.To overcome these limitations,we employed domain adaptive learning approach combined with the YOLOv5 model to develop a novel tomato detection model called as TDA-YOLO(tomato detection domain adaptation).We designated the normal illumination scenes in dense planting environments as the source domain and utilized various other illumination scenes as the target domain.To construct bridge mechanism between source and target domains,neural preset for color style transfer is introduced to generate a pseudo-dataset,which served to deal with domain discrepancy.Furthermore,this study combines the semi-supervised learning method to enable the model to extract domain-invariant features more fully,and uses knowledge distillation to improve the model's ability to adapt to the target domain.Additionally,for purpose of promoting inference speed and low computational demand,the lightweight FasterNet network was integrated into the YOLOv5's C3 module,creating a modified C3_Faster module.The experimental results demonstrated that the proposed TDA-YOLO model significantly outperformed original YOLOv5s model,achieving a mAP(mean average precision)of 96.80%for tomato detection across diverse scenarios in dense planting environments,increasing by 7.19 percentage points;Compared with the latest YOLOv8 and YOLOv9,it is also 2.17 and 1.19 percentage points higher,respectively.The model's average detection time per image was an impressive 15 milliseconds,with a FLOPs(floating point operations per second)count of 13.8 G.After acceleration processing,the detection accuracy of the TDA-YOLO model on the Jetson Xavier NX development board is 90.95%,the mAP value is 91.35%,and the detection time of each image is 21 ms,which can still meet the requirements of real-time detection of tomatoes in dense planting environment.The experimental results show that the proposed TDA-YOLO model can accurately and quickly detect tomatoes in dense planting environment,and at the same time avoid the use of a large number of annotated data,which provides technical support for the development of automatic harvesting systems for tomatoes and other fruits.
文摘Lessons learned from past experiences push for an alternate way of crop production.In India,adopting high density planting system(HDPS)to boost cotton yield is becoming a growing trend.HDPS has recently been considered a replacement for the current Indian production system.It is also suitable for mechanical harvesting,which reducing labour costs,increasing input use efficiency,timely harvesting timely,maintaining cotton quality,and offering the potential to increase productivity and profitability.This technology has become widespread in globally cotton growing regions.Water management is critical for the success of high density cotton planting.Due to the problem of freshwater availability,more crops should be produced per drop of water.In the high-density planting system,optimum water application is essential to control excessive vegetative growth and improve the translocation of photoassimilates to reproductive organs.Deficit irrigation is a tool to save water without compromising yield.At the same time,it consumes less water than the normal evapotranspiration of crops.This review comprehensively documents the importance of growing cotton under a high-density planting system with deficit irrigation.Based on the current research and combined with cotton production reality,this review discusses the application and future development of deficit irrigation,which may provide theoretical guidance for the sustainable advancement of cotton planting systems.
基金National Key R&D Program of China(2022YFD1400300)Agricultural Science and Technology Innovation Program of Chinese Academy of Agricultural SciencesChina Agriculture Research System.
文摘Background Plant hormones profoundly influence cotton growth,development,and responses to various stresses.Therefore,there is a pressing need for an efficient assay to quantify these hormones in cotton.In this groundbreaking study,we have established QuEChERS-HPLC‒MS/MS method,for the simultaneous detection of multiple plant hormones in cotton leaves,allowing the analysis and quantification of five key plant hormones.Results Sample extraction and purification employed 0.1%acetic acid in methanol and C18 for optimal recovery of plant hormones.The method applied to cotton demonstrated excellent linearity across a concentration range of 0.05–1 mg・L−1,with linear regression coefficients exceeding 0.99.The limits of quantification(LOQs)were 20μg・kg−1 for GA3 and 5μg・kg−1 for the other four plant hormones.Recovery rates for the five plant hormones matrix spiked at levels of 5,10,100,and 1000μg・kg−1 were in the range of 79.07%to 98.97%,with intraday relative standard deviations(RSDs)ranging from 2.11%to 8.47%.The method was successfully employed to analyze and quantify the five analytes in cotton leaves treated with plant growth regulators.Conclusion The study demonstrates that the method is well-suited for the determination of five plant hormones in cotton.It exhibits excellent selectivity and sensitivity in detecting field samples,thus serving as a robust tool for indepth research into cotton physiology.
基金Supported by the Natural Science Foundation of Heilongjiang Province(LH2021C032)。
文摘Avian influenza virus is one of the main pathogens causing avian influenza.In this experiment,the avian influenza hemagglutinin(HA)gene was transferred into tobacco by Agrobacterium tumefaciens-mediated method using kanamycin as a resistance marker to produce HA type edible vaccine designed for avian influenza hemagglutinin protein.The fusion of cholera toxin B subgene(CTB)and avian influenza HA was initiated by CaMV35S promoter,and then transformed into Nicotiana benthamiana to induce callus and adventitious bud differentiation,bud elongation,and growth and root induction.The total genomic DNA of 28 transgenic tobacco plants was extracted,and the CTB sequence of the CTB-HA fusion gene was used as a template to design primers for PCR amplification.Eight of them were positive,and four of them were expressed at the RNA level after RNA extraction and RT-PCR amplification.In western blot detection of protein extraction,two strains were shown at the position of the target protein.The results provided material support for the preparation of a transgenic plant oral vaccine against HA infection,and provided a theoretical basis for accelerating the development of a transgenic plant vaccine.
基金Key Project of National Natural Science Foundation of China(52237008)Beijing Municipal Education Commission Research Program Funding Project(KM202111232022)。
文摘There are many motors in operation or on standby in nuclear power plants,and the startup of group motors will have a great impact on the voltage of the emergency bus.At present,there is no special or inexpensive software to solve this problem,and the experience of engineers is not accurate enough.Therefore,this paper developed a method and system for the startup calculation of group motors in nuclear power plants and proposed an automatic generation method of circuit topology in nuclear power plants.Each component in the topology was given its unique number,and the component class could be constructed according to its type and upper and lower connections.The subordination and topology relationship of switches,buses,and motors could be quickly generated by the program according to the component class,and the simplified direct power flow algorithm was used to calculate the power flow for the startup of group motors according to the above relationship.Then,whether the bus voltage is in the safe range and whether the voltage exceeds the limit during the startup of the group motor could be judged.The practical example was used to verify the effectiveness of the method.Compared with other professional software,the method has high efficiency and low cost.
