Search

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Machine vision-based guidance of agricultural machinery operates flexibly in complex field. The classical test methods for agricultural guidance systems are based on real field test. There are many problems for the classical test methods, such as high test cost, strong dependence on crop growing period, long test period, and being easy to damage crops. To solve those problems, a novel test method based on virtual reality for binocular vision based guidance system was presented. A virtual system was built with this method. The virtual test system is composed of the modules of test scene, physics engine of tractor, and control of path tracking. The test scene module consists of crop rows, road and four-wheel tractor, which provides image data for pathway detection and road roughness for the tractor. Models of the test scene were created with 3ds Max and Multigen-Creator as modeling tools and with Vege Prime as visual simulation tool. The physics engine of tractor was used to simulate the dynamics of tractor accurately and quickly according to the real tractor parameters and the information of the test scene. The position and attitude of the tractor were solved and rendered in Vega Prime. A simplified model was used to solve the dynamics of the tractor, including the vehicle model, tire model, and road solution model. To reduce the computational cost, the vehicle model was simplified to a model of 11 degrees of freedom, which are 6 degrees of freedom for the attitude of tractor body, 4 degrees of freedom for wheel rolling, and 1 degree of freedom for front wheel steering. The tire model was built based on the model of Dugoff-I to obtain the parameters of tire easily. The road model was built based on the modules of vpGroundClamp and tripod for collision detection, which solves the road roughness of each wheel. The control of path tracking consists of pathway determination, computation of turning angle of front wheel, and control of turning angle of front wheel. A reported and validated crop row detection method based on binocular vision was used to detect centerlines of crop rows. The initial alignment of tractor is located in the middle of the crop rows. Thus the centerline of that middle crop row would be the pathway during the path tracking. A computational model of the front wheel angle was built based on the pure pursuit method. The control of front wheel angle was designed based on the classical increment proportion-integral-derivative (PID) algorithm. Parameters of the PID controller were optimized with the genetic algorithm. Results of tracking a sinusoidal signal with the time of 5 s and 5° amplitude show that the control system responses quickly and overshoot is small. The software of the virtual test system was developed based on the C++ language in Visual Studio 2008. A tractor with the systems of front steering, rear driving and rear braking was used as the operation machine, the cotton at seedling stage was used as the target crop, and the crop row field was taken as the test scene. Virtual tests of tracking the curved crop rows at the tractor speed of 0.5, 1, 1.5, 2, 2.5, and 3 m/s were conducted. Results show that, the virtual test system simulates the crop field and tractor well in the virtual reality environment and can conduct the tests of tractor guidance based on binocular vision. The proposed method could provide theoretical basis and experimental data for the experiment and improvement of the guidance system. Results of path tracking are satisfying for the tractor speed within 2 m/s, and the amplitude, absolute average value and standard deviation of the position deviation are less than 0.347, 0.072, and 0.141 m, respectively; the amplitude, absolute average value and standard deviation of the direction deviation are less than 11.570°, 2.622°, and 4.462°, respectively. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Machine vision is an important method for the navigation of agricultural machinery. The crop row detection is a key aspect of the navigation based on machine vision for pathway determination. The binocular vision based technique can locate the spatial position of crop rows, which is more effective for crop field with high weeds pressures than monocular vision based technique. The feature point detection and stereo matching are essential aspects of binocular vision algorithm, which will affect the accuracy and efficiency of crop row detection. To enhance the accuracy and efficiency of crop row detection for complex crop field, a new crop row detection method based on Census transform was presented in this paper. The presented method consisted of 3 modules that were image preprocessing, feature point detection and crop row detection. The image preprocessing module was composed of grayscale transformation and feature point detection. To separate crop rows from backgrounds, an improved function of excess green minus excess red(Ex G- Ex R) was used to transform RGB(red, green, blue) color image to grayscale image, in which only the Ex G features were transformed by the classical Ex G- Ex R function. The improved Ex G- Ex R function was compared with the ExG and the classical ExG- Ex R method. The comparative results showed that the improved ExG- Ex R function was more effective to suppress the background noise. The smallest univalue segment assimilating nucleus(SUSAN) detector was used to detect corner points, which could describe the contour of crop rows. The module of feature point detection consisted of stereo matching and three-dimensional(3D) coordinate point calculation. An accurate stereo matching algorithm based on Census transformation was used to calculate the disparity of SUSAN corner point. The Census transformation of the SUSAN corner point was the primitive, and the sum of absolute difference(SAD) function was the similar metric. To reduce the computing burden and improve the accuracy, the sizes of Census mask and SAD mask were both 5×5 pixel. As the binocular camera(BB2-08S2C-38) used in this paper was assembled with 2 parallel monocular cameras, the 3D coordinates of SUSAN corner points were calculated based on their disparities. If the coordinates in the width and height axes of an SUSAN corner point were within the range of width and height thresholds, the point would be extracted to be a feature point of crop rows. As the crop rows were always parallel according to agronomic arrangement, the amount of crop rows in image could be estimated based on the frequency histogram of width distribution. The distance between adjacent crop rows was assigned to the interval, and the range of coordinates in width axis of feature points was assigned to the number of groups in the histogram. The feature points on each crop row were distributed with the shape of ellipse. After obtaining the amount of crop rows, feature points were used to locate the long axis of their distribution based on the principle component analysis(PCA) method. The long axis of the feature points was used to fit the centerline of the corresponding crop row. An SAD based crop row detection algorithm was set as a comparative algorithm. Videos of cotton field that were without infestations, with shadows, with weeds and in turnrows were used to test the 2 algorithms. Results showed that, the proposed algorithm was more robust to the changes of conditions and consumed a little more time; in the situations without turnrows, the accuracy of crop row detection was no less than 92.58%; for the deviation angle of detected centerlines of crop rows, the absolute mean value and standard deviation value were no more than 1.166 and 2.628° respectively; for the processing time, the mean value and standard deviation value were no more than 0.292 and 0.025 s. The accuracy and efficiency of presented method can satisfy the requirement of field operations. [ABSTRACT FROM AUTHOR]

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Transactions of the Chinese Society of Agricultural Engineering is the property of Chinese Society of Agricultural Engineering and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)