As the agricultural internet of things(IoT)technology has evolved,smart agricultural robots needs to have both flexibility and adaptability when moving in complex field environments.In this paper,we propose the concep...As the agricultural internet of things(IoT)technology has evolved,smart agricultural robots needs to have both flexibility and adaptability when moving in complex field environments.In this paper,we propose the concept of a vision-based navigation system for the agricultural IoT and a binocular vision navigation algorithm for smart agricultural robots,which can fuse the edge contour and the height information of rows of crop in images to extract the navigation parameters.First,the speeded-up robust feature(SURF)extracting and matching algorithm is used to obtain featuring point pairs from the green crop row images observed by the binocular parallel vision system.Then the confidence density image is constructed by integrating the enhanced elevation image and the corresponding binarized crop row image,where the edge contour and the height information of crop row are fused to extract the navigation parameters(θ,d)based on the model of a smart agricultural robot.Finally,the five navigation network instruction sets are designed based on the navigation angleθand the lateral distance d,which represent the basic movements for a certain type of smart agricultural robot working in a field.Simulated experimental results in the laboratory show that the algorithm proposed in this study is effective with small turning errors and low standard deviations,and can provide a valuable reference for the further practical application of binocular vision navigation systems in smart agricultural robots in the agricultural IoT system.展开更多
The fruit and vegetable picking has posed a great challenge on the production and markets during the harvest season.Manual picking cannot fully meet the rapid requirements of each market,mainly due to the high labor-i...The fruit and vegetable picking has posed a great challenge on the production and markets during the harvest season.Manual picking cannot fully meet the rapid requirements of each market,mainly due to the high labor-intensive and time-consuming tasks,even the aging and shortage of agricultural labor force in recent years.Alternatively,smart robotics can be an efficient solution to increase the planting areas for the markets in combination with changes in cultivation,preservation,and processing technology.However,some improvements still need to be performed on these picking robots.To document the progress in and current status of this field,this study performed a bibliometric analysis.This analysis evaluated the current performance characteristics of various fruit and vegetable picking robots for better prospects in the future.Five perspectives were proposed covering the robotic arms,end effectors,vision systems,picking environments,and picking performance for the large-scale mechanized production of fruits and vegetables in modern agriculture.The current problems of fruit and vegetable picking robots were summarized.Finally,the outlook of the fruit and vegetable picking robots prospected from four aspects:structured environment for fruit planting,the algorithm of recognition and positioning,picking efficiency,and cost-saving picking robots.This study comprehensively assesses the current research status,thus helping researchers steer their projects or locate potential collaborators.展开更多
基金the National Natural Science Foundationof China(No.31760345).
文摘As the agricultural internet of things(IoT)technology has evolved,smart agricultural robots needs to have both flexibility and adaptability when moving in complex field environments.In this paper,we propose the concept of a vision-based navigation system for the agricultural IoT and a binocular vision navigation algorithm for smart agricultural robots,which can fuse the edge contour and the height information of rows of crop in images to extract the navigation parameters.First,the speeded-up robust feature(SURF)extracting and matching algorithm is used to obtain featuring point pairs from the green crop row images observed by the binocular parallel vision system.Then the confidence density image is constructed by integrating the enhanced elevation image and the corresponding binarized crop row image,where the edge contour and the height information of crop row are fused to extract the navigation parameters(θ,d)based on the model of a smart agricultural robot.Finally,the five navigation network instruction sets are designed based on the navigation angleθand the lateral distance d,which represent the basic movements for a certain type of smart agricultural robot working in a field.Simulated experimental results in the laboratory show that the algorithm proposed in this study is effective with small turning errors and low standard deviations,and can provide a valuable reference for the further practical application of binocular vision navigation systems in smart agricultural robots in the agricultural IoT system.
基金the Basic Public Welfare Research Project of Zhejiang Province(No.LGN20E050007,No.LGG19E050023)Xinjiang Boshiran Intelligent Agricultural Machinery Co.,Ltd.
文摘The fruit and vegetable picking has posed a great challenge on the production and markets during the harvest season.Manual picking cannot fully meet the rapid requirements of each market,mainly due to the high labor-intensive and time-consuming tasks,even the aging and shortage of agricultural labor force in recent years.Alternatively,smart robotics can be an efficient solution to increase the planting areas for the markets in combination with changes in cultivation,preservation,and processing technology.However,some improvements still need to be performed on these picking robots.To document the progress in and current status of this field,this study performed a bibliometric analysis.This analysis evaluated the current performance characteristics of various fruit and vegetable picking robots for better prospects in the future.Five perspectives were proposed covering the robotic arms,end effectors,vision systems,picking environments,and picking performance for the large-scale mechanized production of fruits and vegetables in modern agriculture.The current problems of fruit and vegetable picking robots were summarized.Finally,the outlook of the fruit and vegetable picking robots prospected from four aspects:structured environment for fruit planting,the algorithm of recognition and positioning,picking efficiency,and cost-saving picking robots.This study comprehensively assesses the current research status,thus helping researchers steer their projects or locate potential collaborators.
