Crop boundary detection in images

#This disclosure relates generally to image feature detection, and in particular but not exclusively, relates to use of machine learning in image feature detection.

#Approximately 11% of earth's land surface is presently used in crop production. Despite the importance of agriculture for human survival, environmental impact, national implications, commercial enterprises, the markets, and the like, there is no consistent, reliable, and/or precise identification of all the agricultural fields within a geographical region, county, state, country, continent, planet wide, or portions of any of the above. If more information about agricultural fields were known, seed and fertilizer companies, for example, may better determine available markets for their products in different geographical regions; crop insurance companies may more accurately and cost-effectively assess premiums; banks may more accurately provide farm loans; and/or governments may better assess taxes, allocate subsidies, determine regional food capacity, plan infrastructure, and the like.

#To the extent that mapping data related to agricultural land may exist, such data tends to be inconsistent, inaccurate, out of date, and/or otherwise incomplete for many practical uses. For example, a governmental entity may survey or sample a small portion of the total agricultural lands and/or farmers within a geographical region and extrapolate the small data set to approximate the field locations, sizes, shapes, crop types, counts, etc. of all the agricultural lands actually in existence within the geographical region. Due to the labor-intensive nature of gathering such data, the agricultural land data tends to be updated infrequently (or too infrequently for many commercial purposes).

#Non-limiting and non-exhaustive embodiments of the invention are described with reference to the following figures, wherein like reference numerals refer to like parts throughout the various views unless otherwise specified. Not all instances of an element are necessarily labeled so as not to clutter the drawings where appropriate. The drawings are not necessarily to scale, emphasis instead being placed upon illustrating the principles being described.

#FIG. 1 depicts a block diagram illustrating a network view of an example system incorporated with the crop boundary detection technology of the present disclosure, according to some embodiments.

#FIG. 2 depicts a flow diagram illustrating an example process that may be implemented by the system of FIG. 1, according to some embodiments.

#Embodiments of a system, apparatus, and method for crop boundary detection in images are described herein. In some embodiments, a method includes obtaining a plurality of image sets associated with a geographical region and a time period, wherein each image set of the plurality of image sets comprises multi-spectral and time series images that depict a respective particular portion of the geographical region during the time period; predicting presence of a crop at particular locations within the particular portion of the geographical region associated with an image set of the plurality of image sets; determining crop boundary locations within the particular portion of the geographical region based on the predicted presence of the crop at the particular locations; and generating a crop indicative image comprising at least one image of the multi-spectral and time series images of the image set overlaid with indication of crop areas, wherein the crop areas are defined by the determined crop boundary locations.

#In the following description numerous specific details are set forth to provide a thorough understanding of the embodiments. One skilled in the relevant art will recognize, however, that the techniques described herein can be practiced without one or more of the specific details, or with other methods, components, materials, etc. In other instances, well-known structures, materials, or operations are not shown or described in detail to avoid obscuring certain aspects.

#Reference throughout this specification to “one embodiment” or “an embodiment” means that a particular feature, structure, or characteristic described in connection with the embodiment is included in at least one embodiment of the present invention. Thus, the appearances of the phrases “in one embodiment” or “in an embodiment” in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments.