  Yield monitors on harvesters are a key component of precision agriculture.
Mass flow estimation is the critical factor to measure, and having this allows
for field productivity analysis, adjustments to machine efficiency, and cost
minimization by ensuring trucks are filled maximally without exceeding weight
limits. Several common technologies used on grain harvesters, including impact
plate sensors, are accurate enough on combines to be valuable but suffer from
issues such as drift. Sugarcane is composed of a mixture of billets and trash,
which is a very dispersed material with much less consistency than grains. In
this study, a 3d point cloud approach is used to estimate volume, from which a
calibration factor is derived [density] to translate to mass. The system was
proved in concept in a controlled environment using bamboo, achieving an R2 of
97.4% when fitting average volume flow per test against average mass flow after
correcting for bulk density changes with volume. The system was also tested on
field data, which was collected from nearly 1700 wagon loads from the southern
U.S. and Brazil over the course of 3 seasons in both green and burnt cane.
Results indicated that the concept is very robust with good accuracy, having
seasonal CVs for density values ranging from 6.9% to 16.2%. The camera concept
proves relatively robust to environmental conditions. The same approach could
be used in sugar beets, potatoes or other sparse/non-flowing crops with highly
varying material properties, where traditional mass flow sensors do not work.
