Crop quality sensor based on specular reflectance

1. A crop quality sensor for a harvesting machine, comprising:

an illumination source;

an imaging device;

a processor;

software executing on the processor;

wherein the imaging device is mounted in a clean grain elevator of a harvesting machine such that it can capture images of a crop sample moving through the clean grain elevator;

wherein the illumination source is mounted in proximity to the imaging device and positioned for illuminating the crop sample in the clean grain elevator;

wherein the crop sample is such that individual kernels of the crop sample have a shiny outer casing and a dull inner surface when broken open;

wherein an image is taken with the imaging device of the illuminated crop sample;

wherein the software is executing on the processor;

wherein the software is used to analyze the image to identify outlines of individual kernels and to identify which of those outlines contain a specular highlight; and

wherein a presence of a specular highlight within an outline is indicative that the kernel is whole and unbroken and an absence of such a specular highlight is indicative of a broken kernel.

2. The crop quality sensor of claim 1 , wherein the software is further used to identify outlier outlines that do not conform to an expected shape or size of a kernel, and which do not contain a specular highlight within the outlier outline, wherein the absence of such a specular highlight is indicative of non-crop material.

3. A method of determining a quality of a crop being harvested, comprising the steps of:

mounting an imaging device in a clean grain elevator of a harvesting machine such that it can capture images of a crop sample moving through the clean grain elevator;

mounting an illumination source in proximity to the imaging device such that it is positioned for illuminating the crop sample in the clean grain elevator;

taking an image of the crop sample with the imaging device of the illuminated crop sample;

sending the captured image to a processor, wherein the processor is executing optical analysis software;

analyzing the image with the optical analysis software to determine a percentage of broken kernels present in the crop sample;

wherein the crop sample is such that individual kernels of the crop sample have a shiny outer casing and a dull inner surface when broken open;

wherein the software is used to analyze the image to identify outlines of individual kernels and to identify which of those outlines contain a specular highlight; and

wherein a presence of a specular highlight within an outline is indicative that the kernel is whole and unbroken and an absence of such a specular highlight is indicative of a broken kernel.

4. The method according to claim 3 , which includes the additional steps of:

said software identifying outlier outlines that do not conform to an expected shape or size of a kernel, and which do not contain a specular highlight within the outlier outline; and

the absence of such a specular highlight indicating a non-crop material.

5. The crop quality sensor of claim 1 wherein said harvesting machine includes:

a threshing assembly with a concave and a rotor.

6. The crop quality sensor of claim 5 wherein said harvesting machine includes:

a conveyor chain extending forwardly from said harvesting machine and configured for conveying crop from a field to said threshing assembly.

7. The crop quality sensor of claim 1 wherein said harvesting machine includes:

a cleaning shoe positioned below said threshing assembly and configured for receiving crop material from said rotor; and

said cleaning shoe including crop material sensors providing an output corresponding to crop material sensed on said cleaning shoe.

8. The crop quality sensor of claim 1 wherein said crop quality sensor includes:

said processor being programmed with an algorithm for detecting damaged crop material identified by either nonstandard shapes or the absence of specular highlights within their outlines.

9. The crop quality sensor of claim 1 wherein said crop quality sensor includes:

said imaging device including an optical data capture sensor.

10. The crop quality data sensor of claim 1 wherein said crop quality sensor includes:

an imaging device including photosite array data analyzed for HUE, saturation and intensity.

11. The crop quality data sensor of claim 1 , which includes a moisture sensor using near-infrared (NIR) wavelengths.

12. The crop quality data sensor of claim 1 , which includes:

a radar subsystem for moisture-detection based on frequency.