Pest detector to identify a type of pest using machine learning
In some implementations, a detector may include one or more sensors, one or more external indicators, one or more processors, and one or more non-transitory computer readable media storing instructions that are executable by the one or more processors to perform various operations. The operations may include detecting, by a motion sensor, movement associated with the pest and capturing, by a sensor (e.g., an imaging sensor) of the detector, sensor data (e.g., a digital image) of the pest. The operations may include determining, by a machine learning algorithm, that the sensor data indicates a presence of a pest, and sending a notification message to a computing device. The notification message may include at least a portion of the sensor data. The operations may include visually indicating, using a pest indicator of the one or more external indicators, that the pest was detected.
1. A detector comprising:
one or more sensors;
one or more external indicators;
one or more processors; and
one or more non-transitory computer readable media storing instructions executable by the one or more processors to perform operations comprising:
receiving sensor data from one or more sensors;
determining, by a machine learning algorithm, that the sensor data indicates a presence of a pest;
sending, a notification message to a computing device, the notification message including at least a portion of the sensor data;
visually indicating, using a pest indicator of the one or more external indicators, that the pest was detected;
a sensor plugin comprising:
at least one sensor of the one or more sensors; and
a plug to mate with a receptacle in a housing of the detector, the plug to provide a mechanical linkage and electrical linkage to the detector, the electrical linkage carrying power from the detector to the at least one sensor and carrying additional sensor data from the at least one sensor to the one or more processors in the detector.
2. The detector of claim 1 , further comprising:
detecting, by a motion sensor of the detector, movement associated with the pest; and
capturing, by an imaging sensor of the detector, one or more digital images of the pest.
3. The detector of claim 1 , further comprising:
storing the sensor data in a memory of the detector to create stored data; and
sending the stored data to a server.
4. The detector of claim 1 , further comprising:
receiving ambient light data from an ambient light sensor of the one or more sensors;
determining that the ambient light data satisfies a predetermined threshold; and
transitioning the detector from an active mode to a low-power mode.
5. The detector of claim 1 , wherein indicating that the pest was detected comprises:
visually indicating, using an external indicator light of the detector, that the pest was detected; or
causing an indicator to be displayed on a software application executing on a computing device that is wirelessly connected to the detector.
6. The detector of claim 5 , further comprising:
determining, by the machine learning algorithm, a type of the pest, wherein the type of the pest comprises one of a cockroach, a mouse, or a rat.
7. The detector of claim 1 , wherein a particular sensor of the one or more sensors comprises an ultrasonic sensor, a capacitive sensor, or a micropower impulse radar sensor, the particular sensor capable of detecting movement within a wall.
8. A detector comprising:
one or more sensors;
a sensor plugin comprising:
a plugin housing that includes at least one sensor of the one or more sensors;
a plug to mate with a receptacle in a detector housing of the detector; and
a telescoping extender that can be extended to position the at least one sensor at a particular location to create a pest detection zone;
one or more external indicators;
one or more processors; and
one or more non-transitory computer readable media storing instructions executable by the one or more processors to perform operations comprising:
receiving sensor data from the one or more sensors;
determining, by a machine learning algorithm, that the sensor data indicates a presence of a pest;
sending a notification message to a computing device, the notification message including at least a portion of the sensor data; and
visually indicating, using a pest indicator of the one or more external indicators, that the pest was detected.
9. The detector of claim 8 , wherein the operations further comprise:
receiving ambient light data from an ambient light sensor of the one or more sensors;
determining that the ambient light data satisfies a predetermined threshold; and
transitioning the detector from an active mode to a low-power mode.
10. The detector of claim 8 , wherein the detector further comprises:
an electrified plugin comprising:
a shallow receptacle in which bait is placed; and
an electrified mesh to electrocute a particular pest that is attracted by the bait.
11. The detector of claim 8 , wherein the operations further comprise:
visually indicating, using an external indicator light of the detector, that the pest was detected; or
causing an indicator to be displayed on a software application executing on a computing device that is wirelessly connected to the detector.
12. The detector of claim 11 , wherein the at least one sensor is backward facing to detect pest movement within a wall.
13. The detector of claim 8 , wherein the one or more sensors comprise at least one of:
a motion sensor, an imaging sensor, a microphone, a structured light sensor, an ultrasound sensor, a temperature sensor, an ultrasonic sensor, a capacitive sensor, or a micropower impulse radar sensor.
14. One or more non-transitory computer readable media storing instructions executable by one or more processors to perform operations comprising:
receiving, by a detector, sensor data from one or more sensors;
determining, by a machine learning algorithm, that the sensor data indicates a presence of a pest;
sending a notification message to a computing device, the notification message including at least a portion of the sensor data;
visually indicating, using an external indicator light of the detector, that the pest was detected;
receiving second sensor data from the one or more sensors;
determining that the machine learning algorithm does not recognize a second pest in the second sensor data; and
sending a message to a server requesting assistance identifying the second pest, the message including the second sensor data.
15. The one or more non-transitory computer readable media of claim 14 , wherein the operations further comprise:
receiving an update to the machine learning algorithm from a server;
installing the update to create an updated machine learning algorithm; and
determining, by the updated machine learning algorithm, that the second sensor data indicates the presence of the second pest.
16. The one or more non-transitory computer readable media of claim 14 , wherein the sensor data comprises at least one of:
a set of one or more digital images; or
a digital audio recording.
17. The one or more non-transitory computer readable media of claim 14 , wherein the operations further comprise:
detecting, by a motion sensor of the detector, movement associated with the pest; and
in response to detecting the movement, capturing, by an imaging sensor of the detector, one or more digital images of the pest.
18. The one or more non-transitory computer readable media of claim 14 , wherein the operations further comprise:
receiving ambient light data from an ambient light sensor of the detector;
determining that the ambient light data does not satisfies a predetermined threshold; and
transitioning the detector from an active mode to a low-power mode.
19. The one or more non-transitory computer readable media of claim 14 , wherein the one or more sensors comprise at least one of:
a motion sensor, an imaging sensor, a microphone, a structured light sensor, an ultrasound sensor, a temperature sensor, an ultrasonic sensor, a capacitive sensor, or a micropower impulse radar sensor.
20. The one or more non-transitory computer readable media of claim 14 , wherein:
the pest comprises one of a cockroach, a mouse, or a rat.