Energy-Efficient Uncertainty-Aware Biomass Composition Prediction at the
  Edge
Muhammad Zawish Paul Albert Flavio Esposito Steven Davy Lizy Abraham
Clover fixates nitrogen from the atmosphere to the ground, making
grass-clover mixtures highly desirable to reduce external nitrogen
fertilization. Herbage containing clover additionally promotes higher food
intake, resulting in higher milk production. Herbage probing however remains
largely unused as it requires a time-intensive manual laboratory analysis.
Without this information, farmers are unable to perform localized clover sowing
or take targeted fertilization decisions. Deep learning algorithms have been
proposed with the goal to estimate the dry biomass composition from images of
the grass directly in the fields. The energy-intensive nature of deep learning
however limits deployment to practical edge devices such as smartphones. This
paper proposes to fill this gap by applying filter pruning to reduce the energy
requirement of existing deep learning solutions. We report that although pruned
networks are accurate on controlled, high-quality images of the grass, they
struggle to generalize to real-world smartphone images that are blurry or taken
from challenging angles. We address this challenge by training filter-pruned
models using a variance attenuation loss so they can predict the uncertainty of
their predictions. When the uncertainty exceeds a threshold, we re-infer using
a more accurate unpruned model. This hybrid approach allows us to reduce energy
consumption while retaining a high accuracy. We evaluate our algorithm on two
datasets: the GrassClover and the Irish clover using an NVIDIA Jetson Nano edge
device. We find that we reduce energy reduction with respect to
state-of-the-art solutions by 50% on average with only 4% accuracy loss.