Abstract: Radio frequency identification tags are spaced apart from one another in or around a work area for the vehicle. Each of the tags has a corresponding unique identifier. A path planning module establishes a path comprising a path segment between at least two of the radio frequency identification tags. The path segment comprises at least one of a distance between the tags, an angular heading between the tags, and unique identifiers corresponding to the tags. A data processor determines an estimated position of the vehicle based on at least one of odometer data and accelerometer data. A vehicle controller navigates between at least two of the radio frequency identification tags based on the estimated position, the distance and the angular heading. A reader reads the tag identifiers of each tag to track the progress of the vehicle to facilitate execution of or retrieval of any next path segment of the vehicle along the established path.

Abstract: Radio frequency identification tags are spaced apart from one another in or around a work area for the vehicle. Each of the tags has a corresponding unique identifier. A path planning module establishes a path comprising a path segment between at least two of the radio frequency identification tags. The path segment comprises at least one of a distance between the tags, an angular heading between the tags, and unique identifiers corresponding to the tags. A data processor determines an estimated position of the vehicle based on at least one of odometer data and accelerometer data. A vehicle controller navigates between at least two of the radio frequency identification tags based on the estimated position, the distance and the angular heading. A reader reads the tag identifiers of each tag to track the progress of the vehicle to facilitate execution of or retrieval of any next path segment of the vehicle along the established path.

Abstract: An automatic vision guidance system for an agricultural vehicle is disclosed and described. The vision guidance system uses a K-means clustering algorithm in image processing to distinguish between crop and non-crop features. The vision guidance system utilizes moment algorithms to determine the location and orientation of crop rows, from which desired wheel angles are determined and steering is commanded. The vision guidance system may adjust the location and orientation of visually determined crop rows according to a predetermined distance between crop rows. Further, the vision guidance system may utilize a redundant number of regions of interest in determining crop row locations and orientations.

Abstract: An automatic vision guidance system for an agricultural vehicle is disclosed and described. The vision guidance system uses a K-means clustering algorithm in image processing to distinguish between crop and non-crop features. The vision guidance system utilizes moment algorithms to determine the location and orientation of crop rows, from which desired wheel angles are determined and steering is commanded. The vision guidance system may adjust the location and orientation of visually determined crop rows according to a predetermined distance between crop rows. Further, the vision guidance system may utilize a redundant number of regions of interest in determining crop row locations and orientations.

Abstract: An automatic vision guidance system for an agricultural vehicle is disclosed and described. The vision guidance system uses a K-means clustering algorithm in image processing to distinguish between crop and non-crop features. The vision guidance system utilizes moment algorithms to determine the location and orientation of crop rows, from which desired wheel angles are determined and steering is commanded. The vision guidance system may adjust the location and orientation of visually determined crop rows according to a predetermined distance between crop rows. Further, the vision guidance system may utilize a redundant number of regions of interest in determining crop row locations and orientations.

Abstract: An automatic vision guidance system for an agricultural vehicle is disclosed and described. The vision guidance system uses a K-means clustering algorithm in image processing to distinguish between crop and non-crop features. The vision guidance system utilizes moment algorithms to determine the location and orientation of crop rows, from which desired wheel angles are determined and steering is commanded. The vision guidance system may adjust the location and orientation of visually determined crop rows according to a predetermined distance between crop rows. Further, the vision guidance system may utilize a redundant number of regions of interest in determining crop row locations and orientations.

Abstract: An automatic vision guidance system for an agricultural vehicle is disclosed and described. The vision guidance system uses a K-means clustering algorithm in image processing to distinguish between crop and non-crop features. The vision guidance system utilizes moment algorithms to determine the location and orientation of crop rows, from which desired wheel angles are determined and steering is commanded. The vision guidance system may adjust the location and orientation of visually determined crop rows according to a predetermined distance between crop rows. Further, the vision guidance system may utilize a redundant number of regions of interest in determining crop row locations and orientations.

Abstract: A multi-spectral imaging system and method for producing an image is disclosed. More specifically, the imaging system produces an image of vegetation for analysis of crop characteristics, such as nitrogen levels, from an area having vegetation and a non-vegetation background. A light sensing unit detects light reflected at multiple wavelengths. The image is segmented into images at different wavelengths such as at the red, green and near infrared wavelengths. The images are combined into a multi-spectral image and segmented into a vegetation image by eliminating all non-vegetation images by using the images at two wavelengths. The vegetation image is analyzed for nitrogen levels by calculating reflectance values at the green wavelength. The images may be stored for further analysis of crop characteristics.