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
Based on the study on cultivation models of soybean narrow-row-flat-dense planting under the conditions of different between-row spacing and inter-plant spacing by using the comparison field experiment, and technical ...Based on the study on cultivation models of soybean narrow-row-flat-dense planting under the conditions of different between-row spacing and inter-plant spacing by using the comparison field experiment, and technical studies of the contour following the seeder unit, the anti-block, the lateral and stratified the deep fertilizing, according to the design ideas of planting units integration and variable between-row spacing from 30 to 45 cm, the 2BZJ-3/4 precision planter matched with 18-32 hp tractors has been developed for the popularization of the narrow-row-flat-dense planting soybean technique by means of Virtual Prototyping （VP） technology.展开更多
Background:Sucking insect pests cause severe damage to cotton crop production.The development of insect resistant cotton cultivars is one of the most effective measures in curtailing the yield losses.Considering the r...Background:Sucking insect pests cause severe damage to cotton crop production.The development of insect resistant cotton cultivars is one of the most effective measures in curtailing the yield losses.Considering the role of morphological and biochemical host plant resista nee(HPR)traits in plant defense,12 cotton genotypes/varieties were evaluated for leaf area,leaf glanding,total soluble sugars,total soluble proteins,total phenolics,tannin and total flavonoids against fluctuating populations of whitefly,thrips and jassid under field conditions.Results:The population of these insects fluctuated during the growing seas on and remained above threshold level(whitefly>5,thrips>(8-10)f or jassid>1 per leaf)during late June and early July.Strong and negative association of whitefly(r=-0.825)and jassid(r=-0.929)with seed cotton yield was observed.Mean population of insects were the highest in Glandless-1 followed by NIA-82 and NIA-M30.NIAB-Kiran followed by NI AB-878 and Sadori were the most resistant,with the mean population of 1.41,1.60,1.66(whitefly);2.24,232,2.53(thrips)and 037,0.31,036(jassid),respectively.The resistant variety NIAB-Kiran showed less soluble sugars(8.54 mg.g^(-1)),soluble proteins(27.11 mg.g^(-1))and more phenolic(36.56 mg.g^(-1))and flavonoids(13.10mg.g^(-1))as compared with the susceptible check Glandless-1.Moreover,all insect populations were positively correlated with total soluble sugars and proteins.Whitefly populations exhibited negative response to leaf gossypol glands,total phenolics,tannins and flavonoids.The thrips and jassid populations had a significant and negative correlation with these four biochemical HPR traits.Conclusion:The ide ntified resistant resources and HPR traits can be deployed against sucking in sect pests'complex in future breeding programs of developing insect resistant cotton varieties.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.
基金Supported by National Key Technology R&D Program(2006BAD11A05)National Soybean Industry Technology System
文摘Based on the study on cultivation models of soybean narrow-row-flat-dense planting under the conditions of different between-row spacing and inter-plant spacing by using the comparison field experiment, and technical studies of the contour following the seeder unit, the anti-block, the lateral and stratified the deep fertilizing, according to the design ideas of planting units integration and variable between-row spacing from 30 to 45 cm, the 2BZJ-3/4 precision planter matched with 18-32 hp tractors has been developed for the popularization of the narrow-row-flat-dense planting soybean technique by means of Virtual Prototyping （VP） technology.
文摘Background:Sucking insect pests cause severe damage to cotton crop production.The development of insect resistant cotton cultivars is one of the most effective measures in curtailing the yield losses.Considering the role of morphological and biochemical host plant resista nee(HPR)traits in plant defense,12 cotton genotypes/varieties were evaluated for leaf area,leaf glanding,total soluble sugars,total soluble proteins,total phenolics,tannin and total flavonoids against fluctuating populations of whitefly,thrips and jassid under field conditions.Results:The population of these insects fluctuated during the growing seas on and remained above threshold level(whitefly>5,thrips>(8-10)f or jassid>1 per leaf)during late June and early July.Strong and negative association of whitefly(r=-0.825)and jassid(r=-0.929)with seed cotton yield was observed.Mean population of insects were the highest in Glandless-1 followed by NIA-82 and NIA-M30.NIAB-Kiran followed by NI AB-878 and Sadori were the most resistant,with the mean population of 1.41,1.60,1.66(whitefly);2.24,232,2.53(thrips)and 037,0.31,036(jassid),respectively.The resistant variety NIAB-Kiran showed less soluble sugars(8.54 mg.g^(-1)),soluble proteins(27.11 mg.g^(-1))and more phenolic(36.56 mg.g^(-1))and flavonoids(13.10mg.g^(-1))as compared with the susceptible check Glandless-1.Moreover,all insect populations were positively correlated with total soluble sugars and proteins.Whitefly populations exhibited negative response to leaf gossypol glands,total phenolics,tannins and flavonoids.The thrips and jassid populations had a significant and negative correlation with these four biochemical HPR traits.Conclusion:The ide ntified resistant resources and HPR traits can be deployed against sucking in sect pests'complex in future breeding programs of developing insect resistant cotton varieties.
