The present disclosure relates to a plant stress detection system
and, more particularly, relates to an apparatus and method for determining biotic
5 and/or abiotic plant stress by measuring electrical parameters around a growth
medium (i.e. soil) throughout the growth cycle of the plant to prevent crop loss.
BACKGROUND
[0002] Generally, crop loss due to an inefficient sampling of plants in
cultivation land is one of the serious issues in the agriculture sector. The crop loss
10 may be due to conditions such as disease (i.e. plant stress) or under-development
due to lack of water supply or fertilizers going undetected or uncontrolled usage
of pesticides. Hence, sampling of the plants for early detection of the plant stress
is crucial to prevent financial losses incurred to farmers or cultivators.
Conventionally, the health status of the plants is identified by mere visual
15 inspection based on colour, texture and other physical appearances of the plants,
which are not always accurate.
[0003] Further, the detection of plant stress is done by various techniques
such as thermography, multispectral imaging, hyper-spectral imaging, and the
like. Such imaging techniques require expensive equipment that is not affordable
20 by the farmers. More advanced techniques such as deep learning techniques (e.g.,
convolutional neural networks) can be used for stress detection. These data-driven
techniques include image-based identification of changes in plants' conditions in
the agricultural field and process the image data for identifying the plant stress.
These techniques need computationally intensive operations, sophisticated
25 computing devices, complexity in dataset acquisition, and maintaining the dataset
for improvising the accuracy in determining the plant stress. Moreover, all the
techniques available now are developed for a specific type of stress or disease and
3
cannot detect generic stress in plants.
[0004] Therefore, there is a need for a cost-effective apparatus to
efficiently determine the plant stress in the agriculture field, in addition to
providing other technical advantages.
5 SUMMARY
[0005] This summary is provided only for the purposes of introducing the
concepts presented in a simplified form. This is not intended to identify essential
features of the claimed invention or limit the scope of the invention in any
manner.
10 [0006] Various embodiments of the present disclosure provide an
apparatus for plant stress detection. The apparatus including a plurality of
electrodes is partially inserted into a plant growth medium. Each electrode of the
plurality of electrodes inserted into the plant growth medium is spaced apart from
each other. The apparatus includes at least one sensor configured to determine
15 environmental factors of a surrounding environment of the plant. Further, the
apparatus includes a control unit configured to monitor plant health characteristics
throughout a growth cycle of a plant. The control unit is configured to provide a
control command to the plurality of electrodes through a switching circuit
electrically coupled to the control unit, for determining electrical parameters
20 based, at least in part, on charge carrier concentration in the plant growth medium.
The control command corresponds to an electric pulse (EP). Further, the control
unit is configured to receive the environmental factors of the surrounding
environment of the plant from the at least one sensor. The apparatus includes a
computing device communicably coupled to the control unit. The computing
25 device is configured to compute a decision score indicative of the plant stress
based, at least in part, on the electrical parameters measured for the plant growth
medium and the environmental factors.
[0007] In another embodiment, a method for plant stress detection is
4
disclosed. The method includes providing a control command to a plurality of
electrodes partially inserted into a plant growth medium, for determining
electrical parameters based, at least in part, on charge carrier concentration in the
plant growth medium. The control command corresponds to an electric pulse
5 (EP). The method includes receiving environmental factors of a surrounding
environment of the plant from at least one sensor. The method further includes
detecting anomaly in the electrical parameters measured for the plant growth
medium based at least on a training data. The training data includes standard
electrical parameter values associated with the plant in a normal growth cycle.
10 Further, the method includes computing a decision score indicative of the plant
stress based on the identified anomaly in the electrical parameters.
BRIEF DESCRIPTION OF THE DRAWINGS
[0008] For understanding of exemplary embodiments of the present
disclosure, reference is now made to the following descriptions taken in
15 connection with the accompanying figures in which:
[0009] FIG. 1 illustrates a simplified block diagram of an apparatus for
determining plant stress (both biotic and abiotic stress), in accordance with one
embodiment of the present disclosure;
[0010] FIG. 2 illustrates a simplified block diagram representation of a
20 computing device, in accordance with an example embodiment of the present
disclosure;
[0011] FIG. 3A illustrates a graph depicting variation of electrical
parameters during growth cycle of a plant, in accordance with an example
embodiment of the present disclosure;
25 [0012] FIG. 3B illustrates a graph depicting decision score generated for
the plant growth characteristics throughout the growth cycle of the plant, in
accordance with an example embodiment of the present disclosure;
5
[0013] FIG. 4 illustrates a flow diagram of a method for determining the
plant stress, in accordance with an embodiment of the present disclosure; and
[0014] FIG. 5 illustrates a simplified block diagram of a control unit, in
accordance with an example embodiment of the present disclosure.
5 [0015] The figures referred to in this description depict embodiments of
the disclosure for the purposes of illustration only. One skilled in the art will
readily recognize from the following description that alternative embodiments of
the systems and methods illustrated herein may be employed without departing
from the principles of the disclosure described herein.
10 DESCRIPTION
[0016] In the following description, for purposes of explanation, numerous
specific details are set forth in order to provide a broad understanding of the
present disclosure. It will be apparent, however, to one skilled in the art that the
present disclosure can be practiced without these specific details. In other
15 instances, systems and methods are shown in block diagram form only in order to
avoid obscuring the present disclosure.
[0017] Reference in this specification to “one embodiment” or “an
embodiment” means that a particular feature, structure, or characteristic described
in connection with the embodiment is included in at least one embodiment of the
20 present disclosure. The appearances of the phrase “in one embodiment” in various
places in the specification are not necessarily all referring to the same
embodiment, nor are separate or alternative embodiments mutually exclusive of
other embodiments. Moreover, various features are described which may be
exhibited by some embodiments and not by others. Similarly, various
25 requirements are described which may be requirements for some embodiments but
not for other embodiments.
[0018] Moreover, although the following description contains many
6
specifics for the purposes of illustration, anyone skilled in the art will appreciate
that many variations and/or alterations to said details are within the scope of the
present disclosure. Similarly, although many of the features of the present
disclosure are described in terms of each other, or in conjunction with each other,
5 one skilled in the art will appreciate that many of these features can be provided
independently of other features. Accordingly, this description of the present
disclosure is set forth without any loss of generality to, and without imposing
limitations upon, the present disclosure.
OVERVIEW
10 [0019] Various embodiments of the present disclosure provide an
apparatus and method for plant stress detection. In an embodiment, the apparatus
includes a plurality of electrodes partially inserted into a plant growth medium.
The apparatus includes at least one sensor configured to determine environmental
factors of a surrounding environment of the plant. Further, the apparatus includes
15 a control unit configured to monitor plant health characteristics throughout a
growth cycle of a plant.
[0020] The control unit is electrically coupled to the at least one sensor
and the plurality of electrodes. The control unit is configured to provide a control
command to the electrodes via a switching circuit electrically coupled to the
20 control unit, for determining electrical parameters based, at least in part, on charge
carrier concentration in the plant growth medium. Further, the control unit
receives the electrical parameters (e.g., electrical resistance value) measured by a
measuring unit and the environmental factors measured by the at least one sensor.
Thereafter, the control unit transmits the electrical parameters and the
25 environmental factors to a computing device communicably coupled to the control
unit. The computing device is configured to compute a decision score indicative
of the plant stress based, at least in part, on the electrical parameters measured for
the plant growth medium and the environmental factors. More specifically, the
computing device is configured to generate a graphical representation of the
7
electrical parameters measured for the plant growth medium at different intervals
for identifying anomalies in the electrical parameters. The anomalies are identified
based on a training data. The training data includes standard electrical parameter
values associated with the plant in a normal growth cycle. Thereafter, the
5 computing device computes the decision score indicative of the plant stress based
on the identified anomaly in the electrical parameters. In one scenario, the plant is
determined to be stressed, if the decision score is determined to be higher than a
predefined tolerance limit. In another scenario, the plant is determined to be in the
normal growth cycle, if the decision score is determined to be lower than the
10 predefined tolerance limit.
[0021] Various example embodiments of the present disclosure are
described hereinafter with reference to FIG. 1 to FIG. 5.
[0022] FIG. 1 illustrates a simplified block diagram of an apparatus (100)
for determining plant stress, in accordance with one embodiment of the present
15 disclosure. It should be noted that the apparatus (100) is a proof of concept setup
for detecting the plant stress in a small scale i.e. in the potted plants (as shown in
FIG. 1), and therefore it should not be considered to limit the scope of the present
disclosure. The apparatus (100) can be used for determining the plant stress in a
large area (e.g., in an agricultural land). Further, the components of the apparatus
20 (100) provided herein may not be exhaustive and the apparatus (100) may include
more or fewer components than those depicted and explained in FIG. 1.
[0023] The apparatus (100) includes potted plant (102) (exemplarily
depicted to be ‘four potted plants’) for which the plant stress is to be determined
by monitoring the growth characteristics throughout the growth cycle of each of
25 the plants (102). Further, the apparatus (100) includes a plurality of electrodes
(104). The electrodes (104) are partially inserted into a plant growth medium (i.e.
soil) (106), where remaining portion of the electrodes (104) is exposed for
enabling the electrical connection with other components of the apparatus (100).
Each electrode of the electrodes (104) inserted into the plant growth medium
8
(106) (hereinafter interchangeably referred to as ‘the soil (106)’) is spaced apart
from each other by a predefined distance. It should be understood that the each
electrode (104) inserted into the soil (106) around the plants (102) is spaced apart
from each other, so as to prevent malfunction of the apparatus (100), while
5 determining the plant stress. The electrodes (104) may be fabricated using
graphite, copper, or a combination of any other conducting material as per design
feasibility and requirement.
[0024] The apparatus (100) includes a control unit (108) configured to
monitor plant health characteristics throughout a growth cycle of the plants (102)
10 by measuring the electrical parameters which will be explained further in detail.
[0025] The control unit (108) is electrically connected to a switching
circuit (110), at least one sensor (114) and a measuring unit (112) of the apparatus
(100). The switching circuit (110), configured with multiple input/output pins (I/O
pins), is electrically coupled to one of the electrodes (104) placed in the each plant
15 (102). For example, the switching circuit (110) may be a relay module. The
measuring unit (112) includes a positive port (112a) and a negative port (112b).
The positive port (112a) of the measuring unit (112) (e.g., a digital multimeter) is
electrically coupled to the switching circuit (110) via an electrical probe and the
negative port (112b) of the measuring unit (112) is electrically connected to one
20 electrode (i.e. the electrodes (104)) placed in each plant (102). This enables a
series connection for measuring electrical parameters based on the flow of electric
current through the electrodes (104) inserted into the soil (106) which will be
explained further in detail.
[0026] In operation, the control unit (108) is configured to provide a
25 control command to the switching circuit (110). The control command
corresponds to an electric pulse (EP). The control command enables the switching
circuit (110) to determine charge carrier concentration in the plant growth medium
(106). More specifically, the EP energizes the switching circuit (110) which
enables the flow of electric current to the corresponding electrodes (104) of the
9
plant (102). For instance, the switching circuit (110) may be a 4-channel relay
module, and the control command is given to 3rd channel of the switching circuit
(110). In this scenario, the 3rd channel of the switching circuit (110) will be
energized due to the electric pulse from the control unit (108). Thus, the electric
5 current (or the electric pulse (EP)) will flow to the electrodes (104) that are
electrically connected to the 3rd channel of the switching circuit (110).
[0027] Upon sending the control command, the control unit (108) is
configured to send a command to the measuring unit (112) and the sensor (114).
In this scenario, the measuring unit (112), the switching circuit (110) and the
10 electrodes (104) form a closed circuit. Thus, the measuring unit (112) measures
the electrical parameters in the soil (106). More specifically, the electrical
parameters are computed based at least on the charge carrier concentration present
in the plant growth medium or soil (106). The charge carriers present in the soil
(106) correspond to nutrients (i.e. micronutrients and macronutrients), minerals,
15 and the like. Some examples of the charge carriers present in the soil (106) may
be phosphorous (P), potassium (K), nitrogen (N), magnesium (Mg) and the like.
The electrical parameters measured by the measuring unit (112) may be an
electrical resistance. Additionally, the measuring unit (112) may measure an
electrical conductance, an electrical impedance or any other electrical parameter
20 based at least on the charge carrier concentration in the soil (106). In an
embodiment, the control unit (108) may be configured to measure the electrical
parameters based on providing the EP to the electrodes (104) partially buried in
the soil (106). Further, the sensor (114) (e.g., DHT11 sensor) measures
environmental factors of the surrounding environment, where the plant (102) is
25 located.
[0028] The variation in the charge carrier concentration in the soil (106)
will result in variation in the electrical parameters measurement. It should be
understood that plant stress depends upon the charge carrier concentration in the
soil (106). To that effect, the variations in the electrical parameters across the
30 plant growth medium (106) are used to identify various growth characteristics of
10
the plant throughout the growth cycle.
[0029] More specifically, the plants (e.g., the plant (102)) tend to activate
defence mechanisms when the plant encounters stressors. The stress may trigger a
wide range of plant responses like altered gene expression, cellular metabolism,
5 changes in growth rates, crop yields, and the like. Thus, the defence mechanism
enables the plants (i.e. the plant (102)) to repair and resist to the external stressors,
such as bacteria, virus (or biotic stress), or stressful environment (or abiotic
stress). In this scenario, the plants tend to uptake and/or absorb the nutrients (i.e.
the charge carriers) from the soil (106) to create phenolic compounds for fighting
10 against the plant stress. Thus, the uptake of charge carriers from the soil (106)
results in deficiency of the charge carriers, thereby leading to variation in
measurement of the electrical parameters (i.e. the electrical resistance).
Particularly, the electrical resistance measured when the plant encounters the
stress (either biotic or abiotic stress) will exceed a predefined tolerance limit
15 (deviates from a standard value associated with a normal growth). In other words,
the soil (106) offers high resistance to the flow of the EP through the electrodes
(104), thus resulting in a high resistance value. As explained above, the sensor
(114) measures the environmental factors such as temperature and humidity of the
surrounding environment of the plant (102).
20 [0030] Thereafter, the control unit (108) receives the measured electrical
resistance values (or the electrical parameters) across the plant growth medium
(106) and the environmental factors of the surrounding environment of the plant
(102). The control unit (108) may store the aforementioned data (i.e. the electrical
parameters and the environmental factors) with a time stamp, in a database
25 associated with the control unit (108). The time stamp includes date, time of
reading of the aforementioned parameters. Further, the control unit (108) is
configured to transmit the electrical parameters and the environmental factors and
the time stamp associated with the aforementioned parameters to a computing
device (116) communicably coupled to the control unit (108).
11
[0031] In a similar manner, the electrical parameters are computed in the
agricultural land/cultivation land for determining the stress in the plants. In this
case, the electrodes (104) may be buried partially in a prescribed volume of
growth medium in the agricultural land at regular intervals. Thus, the electrical
5 parameters are determined for the prescribed volume of growth medium in the
agricultural land as explained above.
[0032] The computing device (116) of the apparatus (100) is configured to
plot a graphical representation of the electrical parameters versus time to detect
anomaly in the measured electrical parameters. Further, the computing device
10 (116) generates a decision score for each of the measured electrical parameters for
determining the plant stress which will be explained with reference to FIGS. 2 and
3.
[0033] FIG. 2 illustrates a simplified block diagram representation of a
computing device (116), in accordance with an example embodiment of the
15 present disclosure. The computing device (116) includes at least one processor,
such as a processing module (202) and a memory (204). It is noted that although
the computing device (116) is depicted to include only one processor, the
computing device (116) may include more number of processors therein. In an
embodiment, the memory (204) is capable of storing machine executable
20 instructions, referred to herein as platform instructions (206). Further, the
processing module (202) is capable of executing the platform instructions (206).
In an embodiment, the processing module (202) may be embodied as a multi-core
processor, a single core processor, or a combination of one or more multi-core
processors and one or more single core processors. Examples of the processing
25 module (202) include, but are not limited to, an application-specific integrated
circuit (ASIC) processor, a reduced instruction set computing (RISC) processor, a
complex instruction set computing (CISC) processor, a field-programmable gate
array (FPGA), and the like. In an embodiment, the processing module (202) may
be configured to execute hard-coded functionality. In an embodiment, the
30 processing module (202) is embodied as an executor of software instructions,
12
wherein the instructions may specifically configure the processing module (202)
to perform the algorithms and/or operations described herein when the
instructions are executed.
[0034] The memory (204) includes suitable logic, circuitry, and/or
5 interfaces to store a set of computer readable instructions for performing
operations described herein. The memory (204) may be embodied as one or more
volatile memory devices, one or more non-volatile memory devices, and/or a
combination of one or more volatile memory devices and non-volatile memory
devices. Examples of the memory (204) include a random-access memory
10 (RAM), a read-only memory (ROM), a removable storage drive, a hard disk drive
(HDD), and the like. In at least some embodiments, the memory 204 stores
instructions for determining anomalies, computing the decision score, and the
like.
[0035] The computing device (116) also includes an input/output interface
15 (208) (hereinafter referred to as an ‘I/O interface (208)’). In an embodiment, the
I/O interface (208) may include mechanisms configured to receive inputs from the
control unit (108) and display the graphical representation of the measured
parameters, the decision scores, and the like. To that effect, the I/O interface (208)
may include at least one input interface and/or at least one output interface.
20 Examples of the input interface may include, but are not limited to, a keyboard, a
mouse, a joystick, a keypad, a touch screen, soft keys, and the like. Examples of
the output interface may include, but are not limited to, a display such as a light
emitting diode display, a thin-film transistor (TFT) display, a liquid crystal
display, an active-matrix organic light-emitting diode (AMOLED) display, a
25 microphone, a speaker, and the like.
[0036] The computing device (116) further includes a communication
interface (210). The communication interface (210) may include communication
circuitry such as for example, a transceiver circuitry including antenna and other
communication media interfaces to connect to a wired and/or wireless
13
communication network. The communication circuitry may, in at least some
example embodiments, enable transmission of data signals and/or reception of
signals from other entities, such as the control unit (108) (as shown in FIG. 1) or
other systems configured to maintain real-time information related to the electrical
5 parameters and the environmental factors for determining the plant-growth
characteristics. Additionally, the one or more components of the computing
device (116) communicate with each other via a centralized circuitry system
(212).
[0037] In one embodiment, the computing device (116) may include a
10 database (214) that is configured to store machine learning models.
[0038] It is noted that the computing device (116) as illustrated and
hereinafter described is merely illustrative of an apparatus that could benefit from
embodiments of the present disclosure and, therefore, should not be taken to limit
the scope of the present disclosure. It is noted that the computing device (116)
15 may include fewer or more components than those depicted in FIG. 2.
[0039] In one embodiment, the processing module (202) includes an
anomaly detection module (216) and a decision score computing module (218).
[0040] The anomaly detection module (216) includes suitable logic and/or
interfaces for determining anomalies in the electrical parameters measured for the
20 plant growth medium (106). More particularly, the anomaly detection module
(216) implements one or more machine learning models over the electrical
parameters measured for a predefined time (i.e. the growth cycle) for determining
the anomalies in the measured electrical parameters. The machine learning model
is trained with a training data. The training data includes standard electrical
25 parameter values (i.e. standard electrical resistance values) associated with the
plant in a normal growth cycle. Additionally, the training data includes standard
environmental factors for the normal growth cycle of the plant.
[0041] The anomaly detection module (216) may include an analysis
14
software which uses anomaly detection algorithms trained with machine learning
technique for detecting any abnormal resistance measurements. In an
embodiment, the anomaly detection module (216) may plot/generate the graphical
representation of the electrical parameters measured for the plant growth medium
5 at different intervals for identifying anomalies of the electrical parameters based,
at least in part, on the training data. The graphical representation may be
displayed using the I/O interface (208) of the computing device (116). In one nonlimiting example, the machine learning model may be, but not limited to,
Supervised Machine Learning Technique for Anomaly Detection or a Logic
10 regression model.
[0042] The decision score computing module (218) includes a suitable
logic and/or interfaces for computing the decision score for each of the measured
electrical parameters based at least on the identified anomaly. The decision score
is indicative of the plant stress. In one scenario, if the decision score is determined
15 to be higher than a predefined tolerance limit, the plant is identified to be in stress
(or abnormal behaviour). In another scenario, if the decision score is determined
to be lower than the predefined tolerance limit, the plant is identified to be in the
normal growth cycle. It should be understood that the predefined tolerance limit
corresponds to a range of values of the electrical parameters and the
20 environmental factors during the normal growth cycle and onset of the abnormal
behaviour of the plants which is explained with reference to FIG. 3B.
[0043] In an embodiment, the computing device (116) may transmit, via
the communication interface (210), a notification to a user device (118) associated
with a user (120) (i.e. farmer) who is maintaining the agricultural land, if the
25 decision score is determined to be higher than the predefined tolerance limit (or
when the plant is stressed). This enables the user (120) to take corrective actions
(e.g., pesticides) to prevent crop loss. It should be noted that the information (e.g.,
mobile phone number) related to the user (120) may be stored in the database
(214) of the computing device (116). The computing device (116) may transmit
30 the notification via wireless communication protocols such as short messaging
15
services (SMS).
[0044] Additionally, the computing device (116) may be configured to
determine a type of plant stress based at least on the decision score. The type of
plant stress may be an abiotic stress and a biotic stress. This enables the user (120)
5 to opt corrective measures for neutralizing the plant stress based at least on the
type of plant stress (abiotic or biotic stress).
[0045] In an embodiment, the computing device (116) may also determine
soil humidity based at least on the electrical parameters measured for the plant
growth medium (106).
10 [0046] FIG. 3A illustrates a graph (302) depicting variation of the
electrical parameters during the growth cycle of the plant, in accordance with an
example embodiment of the present disclosure. The graph (302) depicts variation
in the electrical resistance values (represented in Y-axis) and the time or the
growth cycle (represented in X-axis). The electrical resistance values on the Y15 axis are exemplary represented in ohm, and the time on the X-axis is exemplary
represented in minutes. As shown in FIG. 3A, the graph (302) is depicted to
include three regions such as, a region A, a region B and a region C. Each of the
regions A, B, and C collectively, represents the plant behaviour during the growth
cycle based at least on the electrical parameters as explained above.
20 [0047] In the region A, the electrical resistance value is at a maximum
level between the time-period 30-60 minutes. In this scenario, the electrical
resistance value is determined to be higher than the predefined tolerance limit. In
one example, the predefined tolerance limit may be 15000 ohms. As shown in
FIG. 3A, the electrical resistance value is depicted to be 18000 ohms, which
25 exceeds the predefined tolerance limit, due to abnormal behaviour of the plants (or
when the plant is stressed). As explained above, the abnormal behaviour of the
plants is due to the chemical changes in the plant growth medium (106), when the
plant is stressed. In other words, the charge carrier concentration in the soil (106)
decreases when the plant is stressed, thereby resulting in increase in the electrical
16
resistance offered by the soil (106).
[0048] In the region B, the electrical resistance value dips to low
resistance value (e.g., less than 13000 ohms) in a short time-period. It should be
noted that the plant stress is neutralized in this region (i.e. the region B). In other
5 words, the electrical resistance value is determined to be lower than the predefined
tolerance limit. Particularly, the dip in the electrical resistance value in the region
B is due to release of by-products into the soil (106), upon activating the defence
mechanism to fight against the plant stress. The by-products are allelochemicals.
The release of allelochemicals leads to increase in the charge carrier concentration
10 (or the nutrients) in the soil (106) which results in high electrical conductivity and
low electrical resistance.
[0049] In the region C, the electrical resistance value gradually increases
and attains a value (e.g., 14000 ohms) over the time. In this scenario, the plant
tends to be in the normal growth cycle. The gradual increase in the electrical
15 resistance value may be due to uptake of the charge carriers (or the nutrients) from
the soil (106) for performing the functionalities such as, preparation of food,
repair of cells, and the like.
[0050] FIG. 3B illustrates a graph (304) depicting decision score
generated for the plant growth characteristics throughout the growth cycle of the
20 plant, in accordance with an example embodiment of the present disclosure. The
decision score is represented on Y-axis, and the time is represented in minutes on
X-axis. As shown in FIG. 3B, the graph (304) is depicted to include three regions
such as, a region X, a region Y and a region Z. The decision score is indicative of
the plant stress. Further, the decision score indicative of the plant stress is
25 generated based on the electrical parameters measured in the soil (106) which is
explained with reference to FIGS. 1 and 2.
[0051] In the region X, the decision score is of a low value between the
time-period 30-60 minutes. More specifically, the decision score is determined to
be lower than the predefined tolerance limit. In one example, the predefined
17
tolerance limit may be 100. As shown in FIG. 3B, the decision score is depicted to
be less than 100, which is lower than the predefined threshold limit. In this
scenario, the plant is determined to be not stressed (or in the normal growth
cycle), as the decision score is less than the predefined threshold limit due to low
5 electrical resistance value.
[0052] In the region Y, the decision score instantaneously shoots to a
higher value (e.g., 600) in the short time period (i.e. after 60 minutes). In this
scenario, the decision score exceeds the predefined tolerance limit, thereby
indicating the plant is stressed. The electrical resistance value increases when the
10 plant is stressed, thus leading to increase in the value of decision score. In the
region Z, the decision score gradually decreases and attains a value less (e.g., 90)
than the predefined threshold limit. This indicates the plant is attaining the normal
growth cycle upon neutralizing the stress. Particularly, the attainment of the
normal growth cycle in the plants is due to activation of the defence mechanism in
15 the plants as explained above. In this scenario, the electrical resistance also
gradually decreases based on activation of the defence mechanism in the plants.
Further, the variation in the electrical parameters and computing the decision
score based on the electrical parameters are already explained with reference to
FIGS. 1 and 2, therefore they are not reiterated herein for the sake of brevity.
20 [0053] FIG. 4 illustrates a flow diagram of a method (400) for determining
the plant stress, in accordance with an embodiment of the present disclosure. The
method (400) starts at step (402).
[0054] At step (402), the method (400) includes providing a control
command to a plurality of electrodes (104) partially inserted into a plant growth
25 medium (106), for determining electrical parameters based, at least in part, on
charge carrier concentration in the plant growth medium (106). The control
command corresponds to an electric pulse (EP). The control command is provided
by the control unit (108) that is electrically coupled to the electrodes (104) via the
switching circuit (110). The flow of the EP in the electrodes (104) is the measure
18
of the electrical parameter (i.e. the electrical resistance). The measurement of the
electrical parameters is explained in detail with reference to FIG. 1.
[0055] At step (404), the method (400) includes receiving environmental
factors of the surrounding environment of a plant (102) from at least one sensor
5 (114). The at least one sensor (114) is coupled to the control unit (108) which is
configured to measure the environmental factors (i.e. temperature and humidity)
of the surrounding environment of the plant based on receipt of the command
from the control unit (108).
[0056] At step (406), the method (400) includes detecting anomaly in the
10 electrical parameters measured for the plant growth medium (106) based at least
on a training data. The training data includes standard electrical parameter values
associated with the plant (102) in a normal growth cycle. More specifically, the
control unit (108) transmits the electrical parameters and the environmental
factors measured over the time to the computing device (116). The computing
15 device (116) is configured to anomalies in the measured parameters based on the
training data. At step (408), the method (400) includes computing a decision score
indicative of the plant stress based on the identified anomaly in the electrical
parameters. Further, detecting anomalies and computing the decision score
indicative of the plant stress are already described in detail with reference to
20 FIGS. 2 and 3A-3B, and are not reiterated herein for the sake of brevity.
[0057] FIG. 5 illustrates a simplified block diagram of a control unit
(500), in accordance with an example embodiment of the present disclosure. The
control unit (500) is an example of the control unit (108) of FIG. 1. The control
unit (500) includes a processor (505) configured to extract instructions from a
25 memory (510) to provide various features of the present disclosure. The
components of the control unit (500) provided herein may not be exhaustive and
the control unit (500) may include more or fewer components than those depicted
in FIG. 5.
[0058] The control unit (500) includes a communication interface (515).
19
The communication interface (515) may include communication circuitry such as
for example, a wired communication network (the electrical connection). Via the
communication interface (515), the control unit (500) may transmit the control
command to the electrodes (104) for determining the electrical parameters in the
5 plant growth medium. Further the control unit (500) may send the command to the
sensors (114) for determining the environmental factors at the vicinity of the
plant. In an embodiment, the control unit including a database (520) may store the
data related to the electrical parameters and the environmental factors. The control
unit (500) may also perform similar operations as performed by the control unit
10 (108). For the sake of brevity, the detailed explanation of the control unit (500) is
omitted herein with reference to the FIG. 1.
[0059] Although the invention has been described with reference to
specific exemplary embodiments, it is noted that various modifications and
changes may be made to these embodiments without departing from the broad
15 spirit and scope of the invention.
[0060] The foregoing descriptions of specific embodiments of the present
disclosure have been presented for purposes of illustration and description. They
are not intended to be exhaustive or to limit the present disclosure to the precise
forms disclosed, and obviously many modifications and variations are possible in
20 light of the above teaching. The embodiments were chosen and described in order
to best explain the principles of the present disclosure and its practical application,
and to thereby enable others skilled in the art to best utilize the present disclosure
and various embodiments with various modifications as are suited to the particular
use contemplated. It is understood that various omissions and substitutions of
25 equivalents are contemplated as circumstances may suggest or render expedient,
but such are intended to cover the application or implementation without
departing from the spirit or scope of the invention.

CLAIMS
We claim:
5 1. An apparatus (100) for plant stress detection, the apparatus (100)
comprising:
a plurality of electrodes (104) partially inserted into a plant growth
medium (106), wherein each electrode (104) of the plurality of
electrodes (104) inserted into the plant growth medium (106) is spaced
10 apart from each other;
at least one sensor (114) configured to determine environmental factors of
a surrounding environment of a plant (102);
a control unit (108) configured to monitor plant health characteristics
throughout a growth cycle of the plant (102), the control unit (108)
15 configured to:
provide a control command to the plurality of electrodes (104) through
a switching circuit (110) electrically coupled to the control unit
(108), for determining electrical parameters based, at least in part, on
charge carrier concentration in the plant growth medium (106), the
20 control command corresponding to an electric pulse (EP), and
receive the environmental factors of the surrounding environment of the
plant (102) from the at least one sensor (114); and
a computing device (116) communicably coupled to the control unit (108),
the computing device (116) configured to compute a decision score
25 indicative of the plant stress based, at least in part, on the electrical
parameters measured for the plant growth medium (106) and the
environmental parameters.
2. The apparatus (100) as claimed in claim 1, wherein the computing device
30 (116) is trained with a training data to at least:
detect anomaly in the electrical parameters measured for the plant growth
medium (106) based at least on the training data, the training data
21
including standard electrical parameter values associated with the plant in
a normal growth cycle; and
compute the decision score indicative of the plant stress based on the detected
anomaly in the electrical parameters.
5
3. The apparatus (100) as claimed in claim 2, wherein the plant (102) is
identified to be stressed, if the decision score is determined to be higher than a
predefined tolerance limit, and wherein the plant is identified to be in the
normal growth cycle, if the decision score is determined to be lower than the
10 predefined tolerance limit.
4. The apparatus (100) as claimed in claim 2, wherein the computing device
(116) is further configured to at least:
generate a graph (302) of the electrical parameters measured for the plant
15 growth medium (106) at different intervals for identifying the
anomalies in the electrical parameters based, at least in part, on the
training data.
5. The apparatus (100) as claimed in claim 2, wherein the computing device
20 (116) is further configured to determine a type of plant stress based at least on
the decision score, wherein the type of plant stress is one of an abiotic stress
and a biotic stress.
6. The apparatus (100) as claimed in claim 3, wherein the computing device
25 (116) is further configured to transmit a notification to a user device (118)
associated with a user (120), if the decision score is determined to be higher
than the predefined tolerance limit.
7. The apparatus (100) as claimed in claim 1, wherein the control unit (108)
30 is further configured to determine humidity of the plant growth medium (106)
based, at least in part, on the electrical parameters measured for the plant
growth medium (106).
22
8. The apparatus (100) as claimed in claim 1, further comprising:
a measuring unit (112) electrically coupled to the control unit (108) and
the plurality of electrodes (104), the measuring unit (112) configured to
5 measure the electrical parameters indicative of the charge carrier
concentration based at least on the flow of the electric pulse (EP) in the
plurality of electrodes (104).
9. The apparatus (100) as claimed in claim 1, wherein the electrical
10 parameters measured for the plant growth medium (106) comprise one of an
electrical resistance, an electrical conductance and an electrical impedance,
and wherein the environmental factors comprise temperature and humidity.
10. The apparatus (100) as claimed in claim 1, wherein the plurality of
15 electrodes (104) is made of graphite and copper.
11. A method (400) for plant stress detection, the method (400) comprising:
providing a control command to a plurality of electrodes (104) partially
inserted into a plant growth medium (106), for determining electrical
20 parameters based, at least in part, on charge carrier concentration in the
plant growth medium (106), the control command corresponding to an
electric pulse (EP);
receiving environmental factors of a surrounding environment of a plant
(102) from at least one sensor (114);
25 detecting anomaly in the electrical parameters measured for the plant
growth medium (106) based at least on a training data, the training data
including standard electrical parameter values associated with the plant
(102) in a normal growth cycle; and
computing a decision score indicative of the plant stress based at least on
30 the detected anomaly in the electrical parameters.
23
12. The method (400) as claimed in claim 11, wherein the plant (102) is
determined to be stressed, if the decision score is determined to be higher than
a predefined tolerance limit, and wherein the plant is determined to be in the
normal growth cycle, if the decision score is determined to be lower than the
5 predefined tolerance limit.
13. The method (400) as claimed in claim 11, further comprising:
generating a graphical representation of the electrical parameters measured
for the plant growth medium (106) at different intervals for detecting
10 the anomalies in the electrical parameters based, at least in part, on the
training data.
14. The method (400) as claimed in claim 11, further comprising:
determining a type of plant stress based at least on the decision score,
15 wherein the type of plant stress is one of an abiotic stress and a biotic
stress.
15. The method (400) as claimed in claim 11, wherein the electrical
parameters measured for the plant growth medium (106) comprise one of an
20 electrical resistance, an electrical conductance and an electrical impedance,
and wherein the environmental factors comprise temperature and humidity.