IEEE Consumer Electronics Magazine - July/August 2021 - 73

Figure 3. Merkle Tree.
Figure 2. Example of a $$35 attachable
smartphone microscope [24].
A Merkle Tree is a way of combining multiple
fixed-length hashes together in a tree-like structure
(two at a time) with each generating a new
fixed-length hash from two prior hashes as
shown in Figure 3. This feature is commonly
used for blockchain integrity28,29 and is easily
verified with a fast rehash of the data.30 In this
way, only the daily root hash needs to be written
to a public blockchain to verify data integrity,
with intermediate hashes recorded in the system
database for auditability.
When it comes time to harvest the honey, the
beekeeper submits a request for a harvest
voucher via their registered messaging system.
In the request, they report how many hives they
harvested per yard and the total volume of
honey produced in each yard. They also note
any special features, such as " this yard was sourwood
honey " or " this yard is organic " to be verified.
They next submit a number of additional
images of the honey harvest process and, in particular,
the pollen signatures of that yard. There
needs to be enough images to give a valid statistical
pollen distribution for that yard for future
verification.
VOLUME VERIFICATION
The system uses the data to verify that the
amount and type of honey reported by the beekeeper
is plausible given the collected data. This
process includes checking the volume produced,
varietal reported, and management actions taken.
The volume verification is based primarily on
the location, plants of the area, time stamps, and
internal hive images collected throughout the
season. With location information obtained from
July/August 2021
the metadata of images, actual volumes can be
compared to a statistical range of estimated volumes.
These volume checks include making sure
a) the harvest volume per hive is consistent with
historical averages for this area; b) the harvest
volume is consistent with what is reported by
other beekeepers in similar phytoclimatic
regions that same year; and that c) the harvest
volume is consistent with algorithmic projections
of the honey that could have been produced
given known conditions on the ground,
including proxies for hive health.
The first two volume checks are self-explanatory,
but the third deserves some additional
description. The first and second algorithmic
projections are based on the Flight and Cluster
hours the bees would have experienced during
the season based on the locations, weather, and
daylight. Bees only fly to gather nectar when
weather conditions are right. These conditions
include a temperature between 55 (12.8C) and
100F (37.8C), winds less than 15 MPH (24.1
KM/H), no rain, and adequate daylight.31
Given the location data from relevant images,
sunrise and sunset can be calculated.32 Then,
the nearest public weather station's data are
pulled and analyzed, and finally the flight hours
the bees would have experienced can be calculated.
The images from inside the hive can be
used to estimate hive strength and honey storage
space vectors over time (limited storage
space will affect yield, and too much space may
impact the ability of the colony to defend from
pests and may signify the presence of diseases).
With hive strength, space, and flight hour information,
an estimate of the amount of honey produced
can verify yield.
This process can be enhanced with data from
hive scales or bee counters to refine the
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