A remote notification electronic rodent trapping system and method is provided having a plurality of rearming electronic rodent trapping devices configured to wirelessly communicate trap information to a base station that is in communication with a cloud server. When a trap is activated by a rodent trigger it changes from a set state to a kill alert state and initiates a killing cycle. The trap waits a first time period and then sends a notification signal to the base station of trap activation which the base station forwards to the cloud server. The cloud server waits a second predetermined time period before sending a pushed notification to a remote user of the trap's kill alert state. If the trap rearms prior to expiration of the second time period, it alerts the base station which notifies the cloud server. The cloud server updates trap status to the set state and does not send a notification to the user.

The herein described invention is a camera mounting system where the user can safely install a camera high in a tree without having to leave the ground. The camera mounting system is comprised of a tree attachment, ball mount, and camera mount. The system is installed by using an extension pole with releasable capture means for installation and removal of the system. This system is also comprised of a multi-mount adapter, which allows a user to install multiple wire connected mounted components on one tree attachment, or in different elevated positions. Once installed, a camera can be accurately aimed by using a laser attachment and standard laser pen light. This system is further comprised of a saw attachment, which can be used to clear branches from the installation area of the tree being utilized.

A path finding system using a series of networked receiver beacons is disclosed. The system includes receiver beacons placed on a path. Each of the receiver beacons include a transceiver receiving and sending signals and a location indicator such as a LED, that when activated indicates the location of the receiver beacon. Each of the receiver beacons include a controller coupled to the indicator and the transceiver. The controller is operable to receive an activation signal to activate the indicator. A transmitter is paired with each of the receiver beacons. The transmitter includes a transceiver to send an activation signal to at least one of the receiver beacons. The receiver beacon receives the activation signal and activates the indicator. The receiver beacon also relays the activation signal to at least another receiver beacon.

In some implementations, a trap to capture and kill a pest may include a circuit. The circuit may receive sensor data from a sensor and determine that a pest has entered the trap. The circuit may cause a shutter inside the trap to move from an approximately horizontal position to an approximately vertical position, thereby causing a portion of a door of a container located inside the trap to come in contact with adhesive on an outer surface of the container to prevent the pest from leaving the container. Substantially simultaneously with the shutter moving, the circuit may provide electricity to two conductive plates located on in a inner bottom surface of the container, thereby causing the electricity to pass through the pest. The circuit may send a wireless notification message, indicating that the pest was captured in the container, over a wireless network to a computing device.

A rodent trap having a remote arming capability and a method of deploying a rodent trap in an unarmed state and then arming the trap after a wait period without the user having to again physically access the trap is provided. The trap, preferably embodied as an electronic rodent trap, may be configured with wireless remote arming capability, timed arming functionality, activity based arming and the like. When in the unarmed state, the trap operates in a monitoring mode in which the trap can monitor and evaluate activity and/or interactions with the trap and can send and receive wireless communication signals.

The present disclosure describes a device which is able to detect and deter wildlife. The present solution provides for a modular system which can have various modules received in a module housing and those modules can be replaced without the need for the device to be serviced by the manufacturer or a certified repair technician.

A pest monitoring system and associated method includes pest monitoring devices for placement at a site and generating a signal upon detection of a pest, and a pesticide dispensing unit including a reservoir housing a diluent and a pesticide module housing a pesticide. A computer device remotely disposed to the pest monitoring devices receives generated signals therefrom, analyzes environmental and historical factor data for the site to determine an amount and placement location of the pest monitoring devices for monitoring the site, and directs deployment of the pest control monitoring devices according to the analysis. The computer device also receives data from the pesticide dispensing unit including an amount or rate of the pesticide substance dispensed from the pesticide module, to form treatment having a concentration of the pesticide substance in the diluent, the treatment being dispensed from the pesticide dispensing unit to treat a detected pest at the site.

A camera mount may include a base member having a rear face to face a support structure and a front face, the front face a curved guide surface, a camera support having a rear face facing the front face of the base member, the rear face of the camera support comprising a guide abutting surface and a retainer resiliently biasing the guide abutting surface against the curved guide surface to releasably retain the camera support at one of a plurality of different positions along the curved guide surface and at one of a plurality of different available orientations relative to the support structure.

A machine learning aerial wildlife survey and wounded game animal tracking process for completing accurate aerial wildlife surveys and tracking wounded game animals by machine learning and AI-supported filtering of non-relevant information is disclosed. The machine learning AI aerial wildlife survey and wounded game animal tracking process utilizes aerial vehicles, such as drones, to conduct wildlife surveys. Drones are quieter than helicopters and, therefore, have a diminished stress impact on the surrounding wildlife. In some embodiments, the machine learning AI aerial wildlife survey and wounded game animal tracking process utilizes machine learning AI filters to filter out trees and underbrush to accurately visualize wildlife and accurately count wild game.

A method, system and computer program product includes identifying a presence of a problem with one or more items in transport using cognitive analytics and information received from at least one sensing device. In response to the identifying, storing an identification of the presence of the problem in a block chain ledger by a secure and validated feed having an indexed encryption identifying the sensing device, and recommending a remediation action to fix the problem with the one or more items.