  Mass flow estimation is of great importance to several industries, and it can
be quite challenging to obtain accurate estimates due to limitation in expense
or general infeasibility. In the context of agricultural applications, yield
monitoring is a key component to precision agriculture and mass flow is the
critical factor to measure. Measuring mass flow allows for field productivity
analysis, cost minimization, and adjustments to machine efficiency. Methods
such as volume or force-impact have been used to measure mass flow; however,
these methods are limited in application and accuracy. In this work, we use
deep learning to develop and test a vision system that can accurately estimate
the mass of sugarcane while running in real-time on a sugarcane harvester
during operation. The deep learning algorithm that is used to estimate mass
flow is trained using very sparsely annotated images (semi-supervised) using
only final load weights (aggregated weights over a certain period of time). The
deep neural network (DNN) succeeds in capturing the mass of sugarcane
accurately and surpasses older volumetric-based methods, despite highly varying
lighting and material colors in the images. The deep neural network is
initially trained to predict mass on laboratory data (bamboo) and then transfer
learning is utilized to apply the same methods to estimate mass of sugarcane.
Using a vision system with a relatively lightweight deep neural network we are
able to estimate mass of bamboo with an average error of 4.5% and 5.9% for a
select season of sugarcane.
