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| 1 | Review of research on agricultural vehicle autonomous guidance显示文摘A brief review of research in agricultural vehicle guidance technologies is presented.The authors propose the conceptual framework of an agricultural vehicle autonomous guidance system,and then analyze its device characteristics.This paper introduces navigation sensors,computational methods,navigation planners and steering controllers.Sensors include global positioning systems(GPS),machine vision,dead-reckoning sensors,laser-based sensors,inertial sensors and geomagnetic direction sensors.Computational methods for sensor information are used to extract features and fuse data.Planners generate movement information to supply control algorithms.Actuators transform guidance information into changes in position and direction.A number of prototype guidance systems have been developed but have not yet proceeded to commercialization.GPS and machine vision fused together or one fused with another auxiliary technology is becoming the trend development for agricultural vehicle guidance systems.Application of new popular robotic technologies will augment the realization of agricultural vehicle automation in the future. | Ming Li Kenji Imou Katsuhiro Wakabayashi Shinya Yokoyama | 2009 | International Journal of Agricultural and Biological Engineering2009，2，3： | 21 |
| 2 | 农业机械全方位视觉定位系统的定位算法显示文摘农业机械全方位视觉定位系统根据标识方位角角度估算传感器相对于标识坐标系的绝对位置,包括系统校正、除噪、标识特征提取、方位角度估算和定位算法,其中定位算法是实现农业机械全方位视觉定位系统的关键部分。该文主要研究了4个标识和3个标识的定位算法,并通过室外30m×30m平地上的定点试验和传感器倾斜试验验证定位精度及传感器倾斜对定位精度的影响。试验结果表明,试验点坐标的估算值与实测值之间距离的均方根误差与平均绝对误差分别为14.75和14.06cm,最大绝对误差为25.72cm;倾斜角度越大,对定位精度的影响越大。研究表明本文定位算法是可行的,且算法简单、运行速度快;实用中当传感器倾斜角度大于5°或者凹凸不平明显的作业环境中,有必要考虑传感器倾斜造成的定位误差的补偿。 | 李明 Kenji Imou 刘仲华 吴畏 李军政 吴彬 | 2013 | 农业工程学报2013，29，2： | 8 |
| 3 | Real-time monitoring of optimum timing for harvesting fresh tea leaves based on machine vision显示文摘The harvesting time of fresh tea leaves has a significant impact on product yield and quality.The aim of this study was to propose a method for real-time monitoring of the optimum harvesting time for picking fresh tea leaves based on machine vision.Firstly,the shapes of fresh tea leaves were distinguished from RGB images of the tea-tree canopy after graying with the improved B-G algorithm,filtering with a median filter algorithm,binary processing with the Otsu algorithm,and noise reduction and edge smoothing using open and close operations.Then the leaf characteristics,such as leaf area index,average length,and leaf identification index,were calculated.Based on these,the Bayesian discriminant principle and method were used to construct a discriminant model for fresh tea-leaf collection status.When this method was applied to a RGB tea-tree canopy image acquired at 45°shooting angle,the fresh tea-leaf recognition rate was 90.3%,and the accuracy for fresh tea-leaf harvesting status was 98%by cross validation.Hence,this method provides the basic conditions for future tea-plantation operation and management using information technology,automation,and intelligent systems. | Liang Zhang Hongduo Zhang Yedong Chen Sihui Dai Xumeng Li Kenji Imou Zhonghua Liu Ming Li | 2019 | International Journal of Agricultural and Biological Engineering2019，12，1： | 2 |
| 4 | Recognition and localization of strawberries from 3D binocular cameras for a strawberry picking robot using coupled YOLO/Mask R-CNN显示文摘To solve the problem of high labour costs in the strawberry picking process,the approach of a strawberry picking robot to identify and find strawberries is suggested in this study.First,1000 images including mature,immature,single,multiple,and occluded strawberries were collected,and a two-stage detection Mask R-CNN instance segmentation network and a one-stage detection YOLOv3 target detection network were used to train a strawberry identification model which classified strawberries into two categories:mature and immature.The accuracy ratings for YOLOv3 and Mask R-CNN were 93.4%and 94.5%,respectively.Second,the ZED stereo camera,triangulation,and a neural network were used to locate the strawberry in three dimensions.YOLOv3 identification accuracy was 3.1 mm,compared to Mask R-CNN of 3.9 mm.The strawberry detection and positioning method proposed in this study may effectively be used to supply the picking robot with a precise location of the ripe strawberry. | Heming Hu Yutaka Kaizu Hongduo Zhang Yongwei Xu Kenji Imou Ming Li Jingjing Huang Sihui Dai | 2022 | International Journal of Agricultural and Biological Engineering2022，15，6： | 1 |
| 5 | A dual-spectral camera system for paddy rice seedling row detection显示文摘 | Yutaka Kaizu Kenji Imou | | 0，，： | 1 |
| 6 | Methano or ethanol produced from woody biomass: which is more adwntageous显示文摘 | Fumio Hasegawa Shinya Yokoyama Kenji Imou | 2009 | Bioresource Technology2009，101，： | 1 |
| 7 | A dual-spectral camera system for paddy rice seedling row detection 显示文摘 | YUTAKA KAIZU KENJI IMOU | 2008 | Computers and Electronics in Agriculture2008，，63： | 1 |
| 8 | Automatic Diagnosis of Plant Disease显示文摘 | Yutaka SASAKI Tsuguo OKAMOTO Kenji IMOU | 1999 | Journal of JSAM1999，61，2： | 1 |
| 9 | A dual-spectral camera system for paddy rice seedling row detection显示文摘 | Yutaka Kaizu Kenji Imou | 2008 | Computers and Electronics in Agriculture2008，，1： | 1 |
| 10 | Design and performance test of a novel UAV air-assisted electrostatic centrifugal spraying system显示文摘In order to improve the deposition and uniformity of the pesticide sprayed by the agricultural spraying drone,this study designed a novel spraying system,combining air-assisted spraying system with electrostatic technology.First,an air-assisted electrostatic centrifugal spray system was designed for agricultural spraying drones,including a shell,a diversion shell,and an electrostatic ring.Then,experiments were conducted to optimize the setting of the main parameters that affect the charge-to-mass ratio,and outdoor spraying experiments were carried out on the spraying effect of the air-assisted electrostatic centrifugal spray system.The results showed the optimum parameters were that the centrifugal rotation speed was 10000 r/min,the spray pressure was 0.3 MPa,the fan rotation speed was 14000 r/min,and the electrostatic generator voltage was 9 kV;The optimum charge-to-mass ratio of the spray system was 2.59 mC/kg.The average deposition density of droplets on the collecting platform was 366.1 particles/cm^(2) on the upper layer,345.1 particles/cm^(2) on the middle layer,and 322.5 particles/cm^(2) on the lower layer.Compared to the results of uncharged droplets on the upper,middle,and lower layers,the average deposition density was increased by 34.9%,30.4%,and 30.2%,respectively,and the uniformity of the distribution of the droplets at different collection points was better. | Heming Hu Yutaka Kaizu Jingjing Huang Kenichi Furuhashi Hongduo Zhang Xu Xiao Ming Li Kenji Imou | 2022 | International Journal of Agricultural and Biological Engineering2022，15，5： | 0 |
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