Supervised learning is the workhorse for regression and classification tasks,
but the standard approach presumes ground truth for every measurement. In real
world applications, limitations due to expense or general in-feasibility due to
the specific application are common. In the context of agriculture
applications, yield monitoring is one such example where simple-physics based
measurements such as volume or force-impact have been used to quantify mass
flow, which incur error due to sensor calibration. By utilizing semi-supervised
deep learning with gradient aggregation and a sequence of images, in this work
we can accurately estimate a physical quantity (mass) with complex data
structures and sparse ground truth. Using a vision system capturing images of a
sugarcane elevator and running bamboo under controlled testing as a surrogate
material to harvesting sugarcane, mass is accurately predicted from images by
training a DNN using only final load weights. The DNN succeeds in capturing the
complex density physics of random stacking of slender rods internally as part
of the mass prediction model, and surpasses older volumetric-based methods for
mass prediction. Furthermore, by incorporating knowledge about the system
physics through the DNN architecture and penalty terms, improvements in
prediction accuracy and stability, as well as faster learning are obtained. It
is shown that the classic nonlinear regression optimization can be reformulated
with an aggregation term with some independence assumptions to achieve this
feat. Since the number of images for any given run are too large to fit on
typical GPU vRAM, an implementation is shown that compensates for the limited
memory but still achieve fast training times. The same approach presented
herein could be applied to other applications like yield monitoring on grain
combines or other harvesters using vision or other instrumentation.

本文研究利用半监督深度学习和图像处理技术，对农业收获量进行精准预测，成功捕获细杆随机叠放的复杂密度物理特性，并优于传统预测方法，该方法具有普遍适用性。