Note: Descriptions are shown in the official language in which they were submitted.
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AGRICULTURAL OR INDUSTRIAL SUPPLY CHAIN DISTRIBUTED NETWORK USING MULTI-INPUT
DECISION ALGORITHM
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims benefit or priority of
U.S. provisional patent application 62/859,862,
filed June 11, 20191 and U.S. provisional patent application 62/859,865, filed
June 11, 20191 which are
hereby incorporated herein by reference in their entirety.
BACKGROUND
(a) Field
[0002] The subject matter disclosed generally relates
to crop or plant growth monitoring
technologies. More specifically, it relates to a distributed network for
supply chain management and
production yield planning in a context of industrial or agricultural
processes.
(b) Related Prior Art
[0003] There are various ways of organizing the
production of agricultural produce. There is a
need to improve organization, decision making and resource allocations in a
way which is consistent with
the market needs.
BRIEF DESCRIPTION OF THE DRAWINGS
[0004] Further features and advantages of the present
disclosure will become apparent from the
following detailed description, taken in combination with the appended
drawings, in which:
[0005] Fig. 1 is a schematic diagram illustrating the
distributed network, according to an
embodiment of the invention;
[0006] Figs. 2A-2D are close-up views on the schematic
diagram of Fig. 1, illustrating with greater
detail the local production systems, the machine learning module, the edge
computing device and the
diagnostic module, respectively;
[0007] Fig. 3 is a flowchart illustrating a method for
managing a supply chain, according to an
embodiment;
[0008] Fig. 4 is a flowchart illustrating a method for
acquiring and treating data from a crop facility
for yield prediction, according to an embodiment;
[0009] Fig. 5 is a diagram illustrating a method for
monitoring and controlling a plant substrate and
its environment, according to an embodiment; and
[0010] Fig. 6 is a diagram illustrating a computing and
telecommunication infrastructure suitable
for implementing the network and method described herein, according to an
embodiment.
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[0011] It will be noted that throughout the appended
drawings, like features are identified by like
reference numerals.
SUMMARY
[0012] According to a first aspect, there is provided a
method for managing a production of
agricultural produce in a controlled environment comprising at least one local
production system, the
method comprising the steps of: collecting data originating from the at least
one local production system in
which the agricultural produce is produced during a production duration; at a
server, periodically receiving,
from remote data sources, data relative to a market for the agricultural
produce, after a time period which
is less than the production duration; at the server, making a prediction of a
production from the at least one
local production system based on the data collected therefrom; at the server,
performing comparisons of
the prediction with the data relative to the market to make a diagnostic;
determining a level of action for the
diagnostic; and transmitting the diagnostic to an appropriate address of a
device in the at least one local
production system corresponding to the level of action.
[0013] According to an aspect, the method further
comprises formatting the data from the at least
one local production system into a packet.
[0014] According to an aspect, the method further
comprises verifying integrity of the packet
originating from the at least one local production system using a private
blockchain ledger in
communication therewith.
[0015] According to an aspect, the method further
comprises filtering the packet prior to
transmission to perform immediate feedback in the at least one local
production system when the packet
is filtered as requiring the immediate feedback, and thereby avoid
transmitting said packet.
[0016] According to an aspect, collecting data
originating from the at least one local production
system comprises collecting data on soil moisture using a soil moisture
sensor, and transmitting the
diagnostic to the at least one local production system comprises transmitting
a diagnostic for controlling
watering in the at least one local production system.
[0017] According to an aspect, collecting data
originating from the at least one local production
system comprises collecting visual data on the agricultural produce using a
camera, and transmitting the
diagnostic to the at least one local production system comprises transmitting
a diagnostic for controlling a
quantity of nutrients fed to the agricultural produce, a lighting intensity,
or a lighting spectrum.
[0018] According to an aspect, collecting data
originating from the at least one local production
system comprises collecting data on air temperature or air humidity using a
temperature sensor or a
humidity sensor, respectively, and transmitting the diagnostic to the at least
one local production system
comprises transmitting a diagnostic for controlling heating, ventilation and
air conditioning (HVAC) systems
in the at least one local production system.
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[0019] According to an embodiment, there is provided a
method for managing a supply chain
comprising the steps of: feeding at least one local production system with an
algorithm for producing a
good or service over a production duration; by an edge computing device,
receiving and treating data
originating from the at least one local production system; at a server,
periodically receiving, from remote
data sources, data relative to a market for the good or service, after a time
period which is less than the
production duration; at the server, receiving data treated by the edge
computing device to perform
comparisons with the data relative to the market to make a diagnostic; and
transmitting the diagnostic to a
machine learning module for updating the algorithm for producing the good or
service after the time period
which is less than the production duration and feeding the algorithm as
updated to the at least one local
production system.
[0020] According to an aspect, the method further
comprises collecting the data in the at least one
local production system.
[0021] According to an aspect, the method further
comprises verifying integrity of data collected
in the at least one local production system using a private blockchain ledger
in communication therewith.
[0022] According to an aspect, collecting the data in
the at least one local production system
comprises collecting data on soil moisture using a soil moisture sensor, and
transmitting the diagnostic to
the machine learning module comprises transmitting a diagnostic for updating
control of watering in the at
least one local production system.
[0023] According to an aspect, collecting the data in
the at least one local production system
comprises collecting visual data on the good or service using a camera,
respectively, and transmitting the
diagnostic to the machine learning module comprises transmitting a diagnostic
for updating control of a
quantity of nutrients fed to the good or service, a lighting intensity, or a
lighting spectrum.
[0024] According to an aspect, collecting the data in
the at least one local production system
comprises collecting data on air temperature or air humidity using a
temperature sensor or a humidity
sensor, respectively, and transmitting the diagnostic to the machine learning
module comprises
transmitting a diagnostic for updating control of heating, ventilation and air
conditioning (HVAC) systems
in the at least one local production system.
[0025] According to an aspect, the method further
comprises formatting the data collected in the
at least one local production system into a packet
[0026] According to an aspect, the method further
comprises verifying integrity of the packet
originating from the at least one local production system using a private
blockchain ledger in
communication therewith.
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[0027] According to an aspect, the method further
comprises filtering the packet prior to
transmission to perform immediate feedback in the at least one local
production system when the packet
is filtered as requiring the immediate feedback, and thereby avoid
transmitting said packet.
[0028] According to an embodiment, there is provided a
distributed network for supply chain
management comprising: at least one local production system for producing a
good or service over a
production duration; a machine learning module which feeds the at least one
local production system with
an algorithm for producing the good or service; an edge computing device
receiving and treating data
originating from the at least one local production system; and a diagnostic
module in communication with
remote data sources relative to a market for the good or service for a
periodic update after a time period
which is less than the production duration, and further in communication with
the edge computing device
to receive data therefrom, adapted to perform comparisons with the remote data
sources relative to the
market to make a diagnostic and to transmit the diagnostic to the machine
learning module for updating
the algorithm for producing the good or service.
[0029] According to an aspect, the distributed network
further comprises a private blockchain
ledger in communication with the at least one local production system to
verify integrity of data originating
therefrom.
[0030] According to an aspect, the diagnostic module is
operated on a cloud computing facility.
[0031] According to an aspect, wherein the machine
leaming module comprises a convolutional
neural network.
DETAILED DESCRIPTION
[0032] Referring to Fig. 1, there is shown a schematic
diagram illustrating the distributed network
for managing a supply chain in an industrial or agricultural context. With
respect to the agricultural context,
the underlying agricultural process which takes part in the supply chain
should be an agricultural process
in which there is both a substantial flow of information from the status of
the agricultural process and a
possibility to control portions of the agricultural process, i.e., a feedback
can be applied. Therefore, it
applies to agricultural processes which are industrialized and information-
centric in nature.
[0033] An information loop should be present to ensure
that the agricultural processes taking place
in the distributed network are monitored and ensure that they undergo feedback
based on the monitoring
according to the method shown in Fig. 4.
[0034] The distributed network and the corresponding
method apply to a supply chain that involves
the production of a good or service, including the production of agricultural
produce, especially one for
which a feedback can be applied during the production. The feedback implies
that there should be controls
for the production and a way to monitor to production process.
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[0035] Therefore, the distributed network comprises a
local production system 100, comprising
monitoring devices 110 and controls 120, shown in Fig. 2k
[0036] The local production should involve a good or
service, such as agricultural produce (crops),
forwhich a typical duration for production is greater than a characteristic
time period of the market variability
for the good or product. For example, market demand and market prices for
fresh produce (fruit, vegetable,
etc.) typically varies on a weekly basis, while the produce takes several
weeks to grow. The local production
of the produce can therefore undergo feedback depending on market
considerations at the other end.
Other industrial products involving production cycles which are long in
comparison to a characteristic time
period of the market situation can take advantage of the distributed network
too.
[0037] Examples of monitoring devices 110 in the local
production system 100 include, without
limitation, cameras, thermometers, hygrometers, presence sensors, light
sensors, speed sensors,
spectrometers, etc. A plurality and a variety of such devices are normally
provided in the local production
system 100, in accordance with the actual good or service being produced
therein.
[0038] Examples of controls 120 in the local production
system 100 include, without limitation,
actuators, dispensers, valves, pumps, lamps, electric switches, electric
devices, machines, tools, robotic
devices, etc. Again, a plurality and a variety of such devices are normally
provided in the local production
system 100, in accordance with the actual good or service being produced
therein. Parameters of the
controls 120 can be modified or modulated as part of the control being
exerted. For example, the lighting
applied by the lamps may be modulated in spectrum and intensity over time.
[0039] Both the monitoring devices 110 and the controls
120 are expected to have their power
source and also have connections to a network (such as the intemet) or to a
communication channels to a
computing device of some sort (local computer, edge computing device, remote
server, etc.) in order to
feed the data to the computing device and receive instructions therefrom, the
connection being provided
by an appropriate connector (Ethernet connector, VViFi connector, Bluetooth
connector, etc.).
[0040] According to an embodiment, there is provided a
local decision node 200 at the level of the
local production system 100, shown in Fig. 2A. The local decision node 200 can
be embodied as a packet
filter, for example. The computing device being used as a local decision node
200 serves the purpose of
acting locally for simple decision tasks, thereby saving bandwidth and data
storage capacity that would
otherwise be required to transmit the raw data to a remote location (i.e., a
server) to arrive at the same
result. Making decisions on a local computing device is more efficient both in
terms of required
infrastructure and also in terms of time needed to perform the controls on the
local production. Therefore,
the local decision node 200 ads in unison with the local production system
100, comprising monitoring
devices 110 to receive raw data and controls 120 to apply the immediate
control feedback. The local
decision node 200 is therefore very useful to perform routine operations in
the local production workflow,
such as acting on the controls 120 based on the monitoring devices 110 in
cases when the action to perform
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is obvious, such as when some value exceeds some threshold. An action is then
identified without requiring
particular intelligence and the local decision node 200 can then take care of
the action.
[0041] According to an embodiment, there is provided a
machine learning module 300, shown in
Fig. 2B, which feeds the local production system 100 with appropriate rules
for production to be
implemented by the local production controls 120. According to an embodiment,
the machine learning
module 300 comprises a neural network, which is trained using a suitable
database and with operational
rules such as best practices which pertain to the type of local production
being managed.
[0042] For example, if the local production involves
growing produce, the suitable database would
comprise images of the variety of stages in plant growth and fruit/vegetable
color, and the operational rules
which are fed as an input of the machine learning algorithm would be best
practices in growing this type of
produce. In this case, growing produce involves analyzing actual images, and
the machine learning
algorithm can advantageously be implemented in the form of a convolutional
neural network into which the
actual images are fed in the appropriate format.
[0043] According to an embodiment, the data originating
from the local process is transmitted via
a network or via any other type of communication channel to an edge computing
device 400, shown in
Fig. 2G.
[0044] The edge computing device 400 may be adapted to
perform low-level routine tasks as is
the local decision node 200. More advantageously, the edge-computing device
400 is adapted to perform
mid- to high-level tasks to manage the local production system 100. For
example, for any data collected
by the local production monitoring devices 110 that extends beyond the scope
of the local decision node
200, the data should be sent using an appropriate communication channel to the
edge computing device
to be analyzed, such that appropriate feedback instructions are prepared by
the edge computing device
400 and sent back to the controls 120 of the local production system 100 to
put the feedback instructions
into effect.
[0045] The edge computing device 400 is typically
located on the site of the local production
system 100, to avoid having to transit very large quantities of data. Indeed,
the use of cameras or similar
devices acting as monitoring devices 110 involves a significant quantity of
data, and transmitting and/or
storing such data may not be required or advantageous. Using an edge computing
device 400 addresses
this issue.
[0046] Now referring to Fig. 4, the flowchart
illustrates a method for acquiring data from the local
production system and treat the data to perform a yield prediction 3020 and
take action on the controls 120
in a local production system at the appropriate level of action 3030.
[0047] The yield prediction 3020 is performed using
machine learning which ingests a great
quantity and variety of data collected from the local production measuring
devices 110 (each one having
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its data tagged with their origin address in the metadata). The images,
temperature, hygrometry and other
data are compared to references to assess the level of maturity or health
status of the plants. This
comparison can be made using a knowledge base or can be made in an opaque
manner, i.e., using neural
networks to make prediction in conformity with the output taught by training
with large datasets.
[0048] The local production monitoring devices 110
should comprise devices to capture images
of the plants at the local level, i.e., cameras. The image of a plant at a
given instant can be used to assess
the level of maturity and general health condition of the plant and of its
fruits.
[0049] The local production monitoring devices 110
should further comprise devices to capture
physical data about the plants at the local level, i.e., devices to measure
temperature, air humidity, soil
humidity. These devices can include additional devices such as devices to
measure electrical conductivity
of the plants, spectrometers, and other more specialized tools. The variety of
collected data and the fact
that the data are collected across a large number of plants spanning over
different levels of action 3030
makes the yield prediction a multi-input prediction. The fact that the data
collected from remote sources
relative to the market are taken into account to make a decision (decision to
perform feedback using the
controls 120 on a given level of action) makes the decision a multi-input
decision.
[0050] All these devices which form the local
production monitoring devices 110 should be
connected to a network or to a computing device for analyzing the data
collected by the local production
monitoring devices 110.
[0051] Typically, as shown in Fig. 4, the data
undergoes a step of packet formatting. Data is
arranged in a specific format by the local production monitoring devices 110.
[0052] As shown in Fig. 4, there can be a step of
packet filtering which follows packet formatting.
Between these two steps, security checks on the data are performed, possibly
using the blockchain ledger
700.
[0053] Metadata related to the local production
monitoring devices 110 (e.g., device identifier,
facility identifier, location, timestamp, conditions in which data is
collected, etc.) is added to the other data
(collected by the devices 110) being transmitted.
[0054] The step of packet filtering would be performed
by the local decision node 200. This step
allows making security checks easily, to have the packets transmitted over the
network or not.
[0055] From the packets filtered as "good", statistics
can be computed. These statistics are sent
to the diagnostic module 500, or prepared by the diagnostic module 500, for
yield predictions based on the
statistics.
[0056] Fig. 2D illustrates a diagnostic module 500
which is used to receive relevant data from the
edge computing device and eventually provide feedback instructions. The
diagnostic module 500 is
normally run on a server (dedicated, cloud, etc.) which is located at a remote
location with respect to the
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local production system 100 and therefore comprises a communication channel
(such as the intemet
network) for communication with the edge computing device 400 or with a
plurality of edge computing
devices 400.
[0057] More precisely, the diagnostic module 500 is
used to receive, as an input, the output from
the edge computing device 400 and market data, and provides as an output
instructions or data for eventual
application in the local process controlling devices. The market data can
include, for example, market
prices for the goods produced by the local processes, or indicators of
customer demand.
[0058] The diagnostic module 500 can collect the market
data by querying a database on a server
or any other equivalent (dedicated server, cloud storage, etc.), either as a
private server or a server from
another party granting access to the diagnostic module 500. Other techniques
may be applicable, such as
a subscription to an RSS feed, web scraping, or reception of post queries from
a third party to feed new
market data to the diagnostic module 500. The same applies for customer demand
information, which can
be derived from real-time or up-to-date sales data from partnering
supermarkets, for example, and
accessed using similar means.
[0059] According to an embodiment, the data originating
from the local monitoring devices 110
can be verified for integrity. For example, a blockchain ledger 700, which is
preferably a private blockchain
ledger, can be implemented to verify the integrity and origin of the data
originating from the local monitoring
devices. This implementation implies that a direct communication channel
between the local monitoring
devices 110 and the blockchain ledger 700 should be provided. As shown in Fig.
2C, the data can be
collected prior to being treated by the local decision node 200, and be
transmitted to the blockchain ledger
700 at this level for verification, after which it is effectively transmitted
to the local decision node 200 for
treatment. Otherwise, this can be assessed using other methods such as crypted
communications.
Ensuring the integrity and origin of data is useful to prevent industrial
hacking or sabotage, or to prevent
adverse effects that could result from other technical problems that can occur
in the communication
channels between the local monitoring devices and the other systems.
[0060] The diagnostic module 500 is adapted to
determine if there are actions that need to be
performed at the level of any one of the local production system(s) 100. More
specifically, the diagnostic
module 500 is trained to compare the activity monitored by the local
production monitoring devices 110, or
other information outputted by the edge computing device 400, and compare the
monitored activity with
the raw market data or other market indicators to determine if actions need to
be applied by the local
production controls 120.
[0061] More specifically, it can use a knowledge base
and/or a machine learning algorithm to
determine if the local production system 100 is well positioned to meet market
demand. More
advantageously, it can predict the production volume of the local production
and compare with a market
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prediction at a particular date in the future, and produce instructions for
the controls 120 accordingly to
either meet specific criteria or to optimize sales.
[0062] Advantageously, the diagnostic module 500 can
receive data from a plurality of local
production systems 100, each being distinct from the others and presumably
remote from each other. The
diagnostic module 500 can therefore make global or total predictions taking
into account all of the local
production systems 100. If actions need to be performed, the instructions are
given to the controls 120 of
the local production system 100 for which the feedback is the most likely to
be effective, or the one for
which the expected monetary benefit is the highest. This is useful in cases
where the cost of transportation
of the produced good is high or if there is a significant disparity in the
production cost between different
local production systems 100.
[0063] According to an embodiment, the instructions
sent by the diagnostic module 500 are not
practical, low-level instructions to the controls 120, and rather comprise a
prediction or representation of
the plurality of local productions systems 100 and a prediction or
representation of the market data. The
diagnostic module 500 then proceeds to feeding these pieces of information
into the machine learning
module 300, where the newly fed pieces of information are used by the machine
learning module 300 as
new data which modify the machine learning algorithm. Once the machine
learning module 300 has
integrated the new data and the new machine learning algorithm is ready, it is
fed to the local production
systems 100 for application by the local production systems 100 under the new
version of the machine
learning algorithm.
[0064] This feedback loop is how the controls 120 at
the local level are adapted to fit the
constraints from the market data that are being collected and compared at the
level of the diagnostic
module 500, as discussed in relation with Figs. 3-4.
[0065] The method for operating the distributed network
for supply chain management is shown
in Fig. 3, and comprising the following steps:
[0066] step 310 - feeding at least one local production
system with an algorithm for producing a
good or service over a production duration;
[0067] step 320 - verifying integrity of data
originating from the at least one local production system
using a private blockchain ledger in communication therewith
[0068] step 330 - by an edge computing device,
receiving and treating data originating from the at
least one local production system;
[0069] step 340 - at a server, periodically receiving,
from remote data sources, data relative to a
market for the good or service, after a time period which is less than the
production duration;
[0070] step 350 - at the server, receiving data treated
by the edge computing device to perform
comparisons with the data relative to the market to make a diagnostic;
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[0071] step 360 - transmitting the diagnostic to a
machine learning module for updating the
algorithm for producing the good or service after the time period which is
less than the production duration
and feeding the algorithm as updated to the at least one local production
system (and then, go back to step
310, as the whole process is iterative for continuous improvement).
[0072] Now referring back to Fig. 4, there is shown
that when the packet is filtered as containing
data which is indicative that something is not good for the plant, a treatment
prescription (Rx) can be
applied to the plant directly, without having to send to data through a
network, as the data collected is
sufficient to immediately dictate a treatment.
[0073] The treatment prescription (Rx) can otherwise be
instructed by the diagnostic module after
having compared the yield prediction 3020 with the market demand.
[0074] As shown in Fig. 4, the feedback is applied at
the appropriate level of granularity on the
facilities, i.e., level of action 3030. For example, actions can be taken for
a whole farm, for a single growth
chamber, for a single machine, or for a single plant. The local production
controls 120 can therefore be
addressed (i.e., each instance of the controls 120 has its own address such as
an IP address) at the level
which is right to act on a level comprises the desired group of plants in
order to be more effective. For
example, nutrients can be given to a given farm for which the yield prediction
concludes that the yield is
likely to be insufficient for the market demand, such that the productivity of
that farm increases to meet the
demand. If it is detected that a plant has a disease, or the plants in a
single growth chamber have a disease,
then a treatment can be applied to the plant or growth chamber, accordingly.
Resources are therefore
allocated in an optimal fashion and in accordance with a planning of the yield
of a facility. The yield of a
facility is also tailored, using the feedback applied through the controls, to
meet the market demand,
especially in a regional area of the facility (i.e., the market that the
facility caters).
[0075] As mentioned above, the feedback can
advantageously be provided by updating the
machine teaming algorithms which are applied at a given level of action in the
facility. Otherwise, the
instructions can also be applied more directly by instructing the controls 120
with a well-defined action
instead of updating the algorithm that runs them.
[0076] Example
[0077] A practical example of a production which would
benefit from the method would include
strawberry production. The at least one local production systems would include
a plurality of dispersed and
independent facilities in which strawberries are produced, each being
monitored and controlled using the
distributed network.
[0078] The machine learning module 300 would first
integrate images of strawberries at various
stages thereof, and agronomical knowledge would be integrated into a knowledge
base to know how to
properly act upon the strawberry plants depending on their state. For example,
the strawberries may need
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different substances or type of lighting depending on their stage, and some
visual features of the
strawberries may indicate diseases which may require action.
[0079] By feeding the algorithm from the machine
learning module 300 to the local facilities,
strawberry plants can be properly monitored and controlled by implementing the
machine learning
algorithm into the local instruments (water dispenser, nutrient dispensers,
lamps, etc.). Relevant
information can be sent to the edge computing device for treatment.
[0080] Reports from the edge computing device 400 are
sent at a sufficient frequency to the
diagnostic module, typically on the cloud or dedicated server. The diagnostic
module 500 would be
integrated with other partners, such as supermarket chains, to query or
receive market data, such as prices
for the strawberry, demands from various supermarkets, etc. A comparison would
then be made to see if
there is enough production available from the various facilities.
[0081] For example, the edge computing device 400 may
have sent to the diagnostic module a
report indicating that a disease has spread in a particular facility. The
diagnostic module 500 may then
compare with predictions for market data and determine that replacing or
treating the plants would result
in a deficit because the strawberries would be ready at a time where prices
are low and the market is
saturated. The diagnostic module could also determine that the market will be
underserved in a particular
area, with expected high prices and unmet demand, and recommend treating the
plants at a particular
facility which is located in that area.
[0082] To do so, the diagnostic module will integrate
such a determination or diagnostic into
instructions which are fed to the machine learning module 300, which will take
them into account and
update the algorithm. After the update, the updated algorithm is transmitted
to the at least one facility for
application therein.
[0083] Market data are normally collected periodically
at a high frequency, after a time period (i.e.,
inverse of frequency) which is smaller than a production time for the
strawberries, to ensure that the
updates of the algorithm are applied and have an effect in a given production
batch.
[0084] In the end, instructions are transmitted to the
IP address of the device which belongs to the
appropriate level of action for the feedback, ranging from the plant level to
the farm-wide level.
[0085] Fig. 5 illustrates the example in greater
detail. Each box in the diagram represents a
parameterwhich can beaded upon, i.e., represents a control 120, or a parameter
which can be measured,
i.e., represents a monitoring device 110. Arrows represent relationships
between these parameters.
[0086] For example, measurements are taken and actions
are taken with respect to the crops,
centered on the plant substrate which receives water and nutrient and is being
monitored. As an example
of control 120, a mixer in a facility is controlled to feed each specific
plant substrate in the facility. The mixer
receives water, which can be a mixture between city water and wastewater from
the facility itself. After
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being conditioned properly based on its contents, the water is mixed with
nutrients according to an
appropriate recipe (quantities) of nitrogen, phosphorus and potassium
compounds. A distribution system
comprising piping is controlled using pumps to feed the plant substrate with
water (watering) and with
nutrients in appropriate quantities. The other main parameter to control is
lighting, which is controlled both
in intensity and in spectrum to give appropriate lighting for the plants to
grow and to yield produce (such
as fruit).
[0087] Still referring to Fig. 5, the plant substrate
is monitored using monitoring devices 110. A
temperature sensor, a pH sensor, a humidity sensor and a CO2 sensor are used
and provide
measurements which can be analyzed using the network and method described
above to ensure a proper
flow of information through the network, i.e., actions are taken at the local
level without having to transmit
unnecessary data, while other data is transmitted remotely to monitor the
facility as a whole and to update
the instructions based on a high-level diagnostic of the situation (adequation
between expected production
and market) and also based on agronomic best practices.
[0088] Finally, each environment (not only the plant
substrate but also the ambient air) can be
monitored in temperature and humidity to ensure proper control of the HVAC
systems (heating, ventilation
and air conditioning) to maintain the environment within suitable air
parameters, consistently with the
operation of the illumination system.
[0089] Although not shown in Fig. 5, images acquired
from cameras can be used as part of the
monitoring, as already mentioned above.
[0090] Fig. 6 is a diagram illustrating a computing and
telecommunication infrastructure suitable
for implementing the network and method described herein. Most aspects have
already been mentioned
above but are shown in a more formal way in Fig. 6. It should be understood
that the network and method
described herein seek to allow a distributed production (i.e., geographically
distributed), which can allow a
better matching between the local production and the local consumption, and
therefore considerably
reduce the transportation requirements of the produce. However, doing this
creates new considerations,
such as the amount of disseminated data to be acquired and analyzed. In a
single traditional outdoor field,
or greenhouse, the cultivator can see the production very easily. In a
distributed production setting, all
production units are remote from each other and a visual inspection may not
suffice. Moreover, getting
more quantitative measurements is useful from an agronomical perspective
because production can be
analyzed. Therefore, there is a challenge of transmitting and analyzing
information because data is
produced at various locations and can be collected at very high rates
(especially images from cameras,
which have a heavy file size that is hard to treat efficiently) which are
difficult to transmit and to analyze
properly when considering a network of several production units to be
monitored and controlled from a
centralized location.
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[0091] The structure of computing and data processing
devices and the way data is transmitted
according to certain criteria reduce the load of required data transmission
and makes this challenge more
bearable. In other words, thanks to an iterative update of the instructions
based on machine learning,
instructions are implemented at a local level and reviewed repeatedly. Data-
intensive acquisitions, such as
images, can be treated locally based on these instructions, avoiding
transmission of such data and
centralized analysis. Mid-level information is transmitted to middleware, such
as edge computing, which
have higher processing power and is able to treat a filtered quantity of data.
High-level analysis of overall
production and market considerations are treated centrally and only relevant
information is sent to the
remote, central processing unit. The same applies to the diagnostic, which is
not based on a large quantity
of data, but rather on high-level data which is easier to treat.
[0092] Still referring to Fig. 6, it is shown that data
capture takes place using sensors (temperature
of the plant/substrate and of the air/enclosure; air humidity, soil moisture,
soil pH, infrared sensors);
cameras (infrared or optical spectrum), and any other type of data which can
be entered by an operator
via a user interface. All the data is sent to a layer of data communication
which, in practice, is implemented
by appropriate hardware for data communication such as the implementation of a
local area network (LAN),
comprising WSN, VVi-Fi, Bluetooth, Zigbee, LORA or RFID devices and
implementing the appropriate
protocols. IP protocols are also used (including UD, TCP, HTTPS, QUIC loT,
MOTT) to switch to a wide-
area network (WAN) implementing a cellular network, or using fiber, hybrid
fiber-coaxial, or xDSL
technologies along with the VVi-Fi connection.
[0093] This data can be sent through the WAN to a data
interpretation layer embodied by a
computer, in particular a client computer of any type (tabletop, laptop,
tablet, phone, wearable computer,
etc.), for display to the on-site operator.
[0094] Instead of having the data transmitted
immediately from its site of capture to the WAN for
display to the user, the data can be sent to a middleware for analysis and
eventual transmission to the
WAN for display to the user, as shown in Fig. 6.
[0095] The middleware comprises edge computing for
receiving the data and performing, at the
middleware level, the monitoring of environmental data, climate data,
irrigation and nutrient data; process
local images, merge or apply operations on the sensor data, and act as a local
decision node, a local
database and an loT data broker with the outside. Operations such as
formatting, matching, querying and
getting responses, storing and processing data can be done at this level.
[0096] When necessary, the information from the edge
computing can be transmitted to a remote
cloud computing installation which is useful for high-level management (such
as a dashboard), API
management, event database, any necessary cloud-based image processing or
training and the
application of remote decisions.
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[0097] Both the edge computing and cloud computing
systems can communicate together and
their output can be used to trigger a diagnostic which interacts with the
automated processes that take
place, and can be sed to send alerts, diagnostic or status information to the
operator through the WAN
described above.
[0098] A layer of security is added on top of the
infrastructure, implementing, for example, the
blockchain ledger described above, especially at the level of data
transmission.
[0099] While preferred embodiments have been described
above and illustrated in the
accompanying drawings, it will be evident to those skilled in the art that
modifications may be made without
departing from this disclosure. Such modifications are considered as possible
variants comprised in the
scope of the disclosure.
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