Spray irrigation is one of the effective techniques in saving water and increasing crop yield.Large-scale linear move spray irrigation systems are widely used in China.However,the traditional go-stop-go driving method...Spray irrigation is one of the effective techniques in saving water and increasing crop yield.Large-scale linear move spray irrigation systems are widely used in China.However,the traditional go-stop-go driving method causes difficulty in controlling the linear move irrigator.A new control method efficient in operation and the consumption of water,electricity,and labor is needed.Because of the difficulty in real-life examination of the designed systems,virtual reality technology was used to simulate the controlling and driving system in this study.Three-dimensional models of the irrigation system components were built at proper sizes.The three-dimensional images of the farmland as well as the mechanical models of the irrigation system were also built following the principles of ground vehicle dynamics.Application programs were developed to simulate the control system and the driving system.Through simulation an optimal control method was found,which was then used in the field test to control the large scale irrigator to move straight forward with an angle error of less than 0.06°.展开更多
In seed processing for commercial production,the collision or friction between mechanical parts and seed is inevitable.These collisions or friction can cause cracks on the surface of the seed,which can affect germinat...In seed processing for commercial production,the collision or friction between mechanical parts and seed is inevitable.These collisions or friction can cause cracks on the surface of the seed,which can affect germination rates and ultimately reduce crop yields.Using the difference of the seed motion trajectories with different crack degrees in the magnetic field,the grading of the surface crack seed can be realized.In this study,the motion law of seed in a non-uniform magnetic field was analyzed by taking the delinted cottonseed as an object.A magnetic separation device for crop seeds was developed.This device was essentially composed of the feed throat,magnetic roller,conveyor belt,and variable-frequency adjustable-speed motor.The magnetic powder adhering seeds enter the magnetic separation device through the feed throat and are conveyed to the magnetic roller through the conveyor belt.The magnetic roller has different adsorption forces on seeds with different degrees of cracking,and seeds fall into different outlets,thus completing the grading of the seeds.By adjusting working parameters such as the magnetic field strength and the speed of the conveyor belt,magnetic separation could be made.In order to verify the grading effect,the magnetic separation accuracy was taken as the inspection index,the magnetic powder’s mesh number,the mass mixing ratio between magnetic powder and delinted cottonseed,and the rotation speed of the magnetic roller were taken as the inspection factors.The response surface methodology was used to optimize the working parameters of the experimental device.The results showed that the optimal process parameters of the magnetic separation device were as follows:the number of magnetic powder meshes was 250,the mass mixing ratio between magnetic powder and delinted cottonseed was 1:20,and when the rotational speed of the magnetic separation roller was 20 r/min,the detection rate reached 92.5%.The designed magnetic separation device can realize the non-destructive batch detection of seed surface cracks with high work efficiency and has guiding significance for the high-precision and low-damage classification detection and classification of commercial seed.展开更多
The facility-based production method is an important stage in the development of modern agriculture,lifting natural light and temperature restrictions and helping to improve agricultural production efficiency.To addre...The facility-based production method is an important stage in the development of modern agriculture,lifting natural light and temperature restrictions and helping to improve agricultural production efficiency.To address the problems of difficulty and low accuracy in detecting pests and diseases in the dense production environment of tomato facilities,an online diagnosis platform for tomato plant diseases based on deep learning and cluster fusion was proposed by collecting images of eight major prevalent pests and diseases during the growing period of tomatoes in a facility-based environment.The diagnostic platform consists of three main parts:pest and disease information detection,clustering and decision-making of detection results,and platform diagnostic display.Firstly,based on the You Only Look Once(YOLO)algorithm,the key information of the disease was extracted by adding attention module(CBAM),multi-scale feature fusion was performed using weighted bi-directional feature pyramid network(BiFPN),and the overall construction was designed to be compressed and lightweight;Secondly,the k-means clustering algorithm is used to fuse with the deep learning results to output pest identification decision values to further improve the accuracy of identification applications;Finally,a detection platform was designed and developed using Python,including the front-end,back-end,and database of the system to realize online diagnosis and interaction of tomato plant pests and diseases.The experiment shows that the algorithm detects tomato plant diseases and insect pests with mAP(mean Average Precision)of 92.7%,weights of 12.8 Megabyte(M),inference time of 33.6 ms.Compared with the current mainstream single-stage detection series algorithms,the improved algorithm model has achieved better performance;The accuracy rate of the platform diagnosis output pests and diseases information of 91.2%for images and 95.2%for videos.It is a great significance to tomato pest control research and the development of smart agriculture.展开更多
基金This work was primarily funded by the Chinese National High Technology Program(“863”program)(No.2006AA10A305,2008AA100902)the“11th Five Year”program(No.2006BAD11A01,2006BAD11A01)of China.
文摘Spray irrigation is one of the effective techniques in saving water and increasing crop yield.Large-scale linear move spray irrigation systems are widely used in China.However,the traditional go-stop-go driving method causes difficulty in controlling the linear move irrigator.A new control method efficient in operation and the consumption of water,electricity,and labor is needed.Because of the difficulty in real-life examination of the designed systems,virtual reality technology was used to simulate the controlling and driving system in this study.Three-dimensional models of the irrigation system components were built at proper sizes.The three-dimensional images of the farmland as well as the mechanical models of the irrigation system were also built following the principles of ground vehicle dynamics.Application programs were developed to simulate the control system and the driving system.Through simulation an optimal control method was found,which was then used in the field test to control the large scale irrigator to move straight forward with an angle error of less than 0.06°.
基金supported by the National Key R&D Program of China (Grant No.2018YFD0101000)National Natural Science Foundation of China (Grant No.52075150)Innovation Scientists and Technicians Team Projects of Henan Provincial Department of Education (23IRTSTHN015).
文摘In seed processing for commercial production,the collision or friction between mechanical parts and seed is inevitable.These collisions or friction can cause cracks on the surface of the seed,which can affect germination rates and ultimately reduce crop yields.Using the difference of the seed motion trajectories with different crack degrees in the magnetic field,the grading of the surface crack seed can be realized.In this study,the motion law of seed in a non-uniform magnetic field was analyzed by taking the delinted cottonseed as an object.A magnetic separation device for crop seeds was developed.This device was essentially composed of the feed throat,magnetic roller,conveyor belt,and variable-frequency adjustable-speed motor.The magnetic powder adhering seeds enter the magnetic separation device through the feed throat and are conveyed to the magnetic roller through the conveyor belt.The magnetic roller has different adsorption forces on seeds with different degrees of cracking,and seeds fall into different outlets,thus completing the grading of the seeds.By adjusting working parameters such as the magnetic field strength and the speed of the conveyor belt,magnetic separation could be made.In order to verify the grading effect,the magnetic separation accuracy was taken as the inspection index,the magnetic powder’s mesh number,the mass mixing ratio between magnetic powder and delinted cottonseed,and the rotation speed of the magnetic roller were taken as the inspection factors.The response surface methodology was used to optimize the working parameters of the experimental device.The results showed that the optimal process parameters of the magnetic separation device were as follows:the number of magnetic powder meshes was 250,the mass mixing ratio between magnetic powder and delinted cottonseed was 1:20,and when the rotational speed of the magnetic separation roller was 20 r/min,the detection rate reached 92.5%.The designed magnetic separation device can realize the non-destructive batch detection of seed surface cracks with high work efficiency and has guiding significance for the high-precision and low-damage classification detection and classification of commercial seed.
基金the National Key Research and Development Program of China Project(Grant No.2021YFD 2000700)the Foundation for University Youth Key Teacher of Henan Province(Grant No.2019GGJS075)the Natural Science Foundation of Henan Province(Grant No.202300410124).
文摘The facility-based production method is an important stage in the development of modern agriculture,lifting natural light and temperature restrictions and helping to improve agricultural production efficiency.To address the problems of difficulty and low accuracy in detecting pests and diseases in the dense production environment of tomato facilities,an online diagnosis platform for tomato plant diseases based on deep learning and cluster fusion was proposed by collecting images of eight major prevalent pests and diseases during the growing period of tomatoes in a facility-based environment.The diagnostic platform consists of three main parts:pest and disease information detection,clustering and decision-making of detection results,and platform diagnostic display.Firstly,based on the You Only Look Once(YOLO)algorithm,the key information of the disease was extracted by adding attention module(CBAM),multi-scale feature fusion was performed using weighted bi-directional feature pyramid network(BiFPN),and the overall construction was designed to be compressed and lightweight;Secondly,the k-means clustering algorithm is used to fuse with the deep learning results to output pest identification decision values to further improve the accuracy of identification applications;Finally,a detection platform was designed and developed using Python,including the front-end,back-end,and database of the system to realize online diagnosis and interaction of tomato plant pests and diseases.The experiment shows that the algorithm detects tomato plant diseases and insect pests with mAP(mean Average Precision)of 92.7%,weights of 12.8 Megabyte(M),inference time of 33.6 ms.Compared with the current mainstream single-stage detection series algorithms,the improved algorithm model has achieved better performance;The accuracy rate of the platform diagnosis output pests and diseases information of 91.2%for images and 95.2%for videos.It is a great significance to tomato pest control research and the development of smart agriculture.
