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 process for determining the health of crops in a field and for correcting deficiencies in the health of the crops is disclosed. The process includes georeferencing aerial photographs of at least a portion of the field, the aerial photographs having a particular spatial resolution; determining the green plane in the aerial photographs thus taken; preparing a relative greenness map of the field based upon the nitrogen reference area, the relative greenness map providing crop status information having spatial resolution equivalent to the spatial resolution of the aerial photographs; converting the relative greenness map to a nitrogen recommendation map having spatial resolution equivalent to the spatial resolution of the photographs; and applying nitrogen to the field according to the nitrogen recommendation map, whereby the nitrogen is applied to the field without loss of spatial information. A process for treating crops is also disclosed.

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 process for determining the health of crops in a field and treating the crops in accordance with that determination. The process first establishes, in a field in which the health of the crops is to be determined, at least one predetermined reference area of crops having a determined nutrient, e.g. nitrogen, content. Then, georeferenced aerial photographs are taken of at least a portion of the field. From the photographs, the green plane is determined. From the green plane, a relative greenness map of the field based upon the nitrogen reference area is prepared. The greenness map may be transmitted to a farm vehicle on the field and thereafter, the field may be selectively treated in accordance with the nutrient deficiencies detected and recorded on the greenness map.