Ecoation Innovative Solutions Inc.

The University of British Columbia

Simon Fraser University

Murray Bruce Isman

Saber Miresmailli

Maryam Antikchi

Erika Plettner

A method includes receiving sensor data pertaining to each of multiple plants in a growing area from a mobile sensory platform that includes (i) a propulsion system configured to move the mobile sensory platform along a ground of the growing area and (ii) multiple sensors configured to capture the sensor data associated with the multiple plants. The method also includes processing the sensor data using a predictive model to identify one or more issues affecting at least one of the plants. The method further includes generating a graphical user interface that includes a graphical representation of the growing area. In addition, the method includes identifying, using one or more markers in the graphical representation of the growing area, one or more locations of the at least one of the plants having the one or more issues. The sensor data includes information about a three-dimensional structure of each of the plants.

Multi-sensor platform for crop health monitoring

A mobile sensory platform includes a propulsion system configured to move the mobile sensory platform along a surface of a growing area. The mobile sensory platform also includes multiple sensors configured to capture measurement data associated with multiple plants in the growing area. The mobile sensory platform further includes a communication interface configured to support two-way communication over a wireless network. In addition, the mobile sensory platform also includes a power supply and a control system configured to control movement of the mobile sensory platform and operation of the sensors. The multiple sensors include one or more microclimate sensors configured to generate microclimate sensor data based on changes in a microclimate around individual ones of the multiple plants.

A method includes receiving first sensor data pertaining to plant-related parameters of each of one or more first plants that performed well over time. The method also includes analyzing at least some of the first sensor data to generate a predictive model associated with the one or more first plants. The method further includes receiving second sensor data pertaining to plant-related parameters of each of multiple second plants. In addition, the method includes identifying at least one of the second plants to receive one or more interventions by applying the predictive model to the second sensor data. Identifying the at least one of the second plants includes identifying the at least one of the second plants as having at least one of the plant-related parameters that deviates from at least one of the plant-related parameters of the one or more first plants.

A multi-sensor device includes a housing having multiple cavities and one or more sensor modules. Each sensor module is configured to occupy one of the cavities, and each sensor module is configured to sense at least one plant-related parameter when the multi-sensor device is positioned proximate to a plant. The multi-sensor device also includes a control unit configured to control operation of the one or more sensor modules and a location tracking system configured to track a location of the multi-sensor device. The multi-sensor device further includes a communications interface configured to transmit information including data from the one or more sensor modules. In addition, the multi-sensor device includes an electrical power connector configured to connect the multi-sensor device to a power source.

A mobile sensory platform includes a propulsion system configured to move the platform within a growing area, a vertically-extending support, and sensors in or on the support. Different sensors are positioned at different heights along the support. The sensors are configured to capture data associated with plants in the growing area. The platform also includes a communication interface configured to support two-way wireless communication, a power supply, and a control system configured to control movement of the platform and operation of the sensors. The sensors include microclimate sensors configured to sense microclimates around individual ones of the plants and stereo imaging sensors configured to capture images of the plants. The sensors are configured to non-invasively capture multi-dimensional data points for the individual ones of the plants. At least some of the multi-dimensional data points are associated with a 3D structure of a canopy of the individual ones of the plants.

A method includes receiving, using at least one processor, first sensor data pertaining to plant-related parameters of each of multiple first plants over time. The method also includes storing the first sensor data in at least one memory. The method further includes identifying, using the at least one processor, an issue affecting at least one of the first plants. The method also includes analyzing, using the at least one processor, at least some of the stored first sensor data to generate a predictive model associated with the issue. The method further includes receiving, using the at least one processor, second sensor data pertaining to plant-related parameters of each of multiple second plants. In addition, the method includes identifying, using the at least one processor, at least one of the second plants to receive one or more interventions by applying the predictive model to the second sensor data.

Crop health monitoring using predictive modeling

A multi-sensor device comprises a housing containing multiple sensor modules for capturing and transmitting sensor data for plants in a crop. A control unit within the housing is operable to control the sensor modules, and a communications interface is connected to the control unit for transmitting data from said plurality of sensor modules. The sensor modules can include a physiological sensor, a surface analysis sensor, and chemical sensor. The multi-sensor device can be used as a hand-held device or mounted to a mobile platform for use in an automated crop monitoring system.

The invention provides in part dialkoxybenzene compounds for controlling infestation by a, and methods thereof. The compounds include a compound of Formula I: where R1 may be methyl, ethyl, propyl, n-butyl, isopentyl (3-methylbutyl) or allyl; R2 may be at positions 2, 3 or 4 and may be H, methyl, ethyl, propyl, n-butyl, isopentyl (3-methylbutyl) or allyl; and R3 may be optionally present at positions 2, 3 and 4, and is allyl; except that when R2 is at position 2, R3 if present is at position 3, and when R2 is at position 3, R3 if present is at positions 2 or 4, and when R2 is at position 4, R3 if present is at position 2, and when R2 is at position 4 and R3, if present, has reacted with an OH group at position 1 in a Markovnikov sense, then R3 becomes R4, a dihydrofuran.

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Vancouver, Canada

Murray Bruce Isman