Abstract: 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.

Abstract: A mobile platform includes at least one first sensor mounted at a first position on the mobile platform and at least one second sensor mounted at a second position on the mobile platform, where the first position is offset from the second position. The at least one first sensor is configured to capture first data measurements of plants in the growing area. The at least one second sensor is configured to capture second data measurements of the plants in the growing area. Each of the first and second data measurements is associated with a three-dimensional position within the growing area and a time. The first and second sensors are configured to generate at least one common type of data measurement such that at least some of the first and second data measurements represent stereo-spatio-temporal data measurements of the plants in the growing area.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method includes obtaining data measurements associated with plants in at least one growing area. The plants have a common genotype and are grown under different growing or environmental conditions in the at least one growing area. The data measurements are associated with one or more characteristics of the plants and multiple characteristics of the growing or environmental conditions. The method also includes processing at least some of the data measurements to identify one or more of the growing or environmental conditions associated with at least one desired characteristic being expressed in the plants being grown. The method further includes outputting an identification of the one or more growing or environmental conditions identified as achieving a specific genotype or phenotype trait for the plants. The specific genotype or phenotype trait is associated with the at least one desired characteristic.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: A method includes receiving manual input from a human operator, where the manual input includes human observation data measurements associated with plants in a growing area. The method also includes, for each human observation data measurement, identifying a height of a movable portion of a mobile platform on which the human operator rides, identifying a location of the mobile platform in the growing area, identifying a time at which the human observation data measurement is received, and associating the human observation data measurement with a three-dimensional position within the growing area and the time in order to generate a spatio-temporal data measurement. The three-dimensional position includes the height and the location. The method further includes storing, processing, or transmitting at least some of the spatio-temporal data measurements or one or more results based on at least some of the spatio-temporal data measurements.

Abstract: 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.

Abstract: A mobile platform includes at least one first sensor mounted at a first position on the mobile platform and at least one second sensor mounted at a second position on the mobile platform, where the first position is offset from the second position. The at least one first sensor is configured to capture first data measurements of plants in the growing area. The at least one second sensor is configured to capture second data measurements of the plants in the growing area. Each of the first and second data measurements is associated with a three-dimensional position within the growing area and a time. The first and second sensors are configured to generate at least one common type of data measurement such that at least some of the first and second data measurements represent stereo-spatio-temporal data measurements of the plants in the growing area.

Abstract: A method includes receiving manual input from a human operator, where the manual input includes human observation data measurements associated with plants in a growing area. The method also includes, for each human observation data measurement, identifying a height of a movable portion of a mobile platform on which the human operator rides, identifying a location of the mobile platform in the growing area, identifying a time at which the human observation data measurement is received, and associating the human observation data measurement with a three-dimensional position within the growing area and the time in order to generate a spatio-temporal data measurement. The three-dimensional position includes the height and the location. The method further includes storing, processing, or transmitting at least some of the spatio-temporal data measurements or one or more results based on at least some of the spatio-temporal data measurements.

Abstract: An apparatus includes at least one processor configured to obtain stereo-spatio-temporal data measurements of plants in a growing area. The stereo-spatio-temporal data measurements include (i) first spatio-temporal data measurements of the plants in the growing area and (ii) second spatio-temporal data measurements of the plants in the growing area. The at least one processor is also configured to analyze the stereo-spatio-temporal data measurements to identify one or more actual or potential problems associated with one or more of the plants. The at least one processor is further configured to generate a graphical user interface identifying at least one of the one or more actual or potential problems with the one or more plants. The first and second spatio-temporal data measurements of each stereo-spatio-temporal data measurement are associated with at least one common plant characteristic and different three-dimensional positions within the growing area taken at one or more known times.

Abstract: Systems and methods for monitoring and assessing crop health and performance can provide rapid screening of individual plants. The systems and methods have an automated component, and rely primarily on the detection and interpretation of plant-based signals to provide information about crop health. In some cases knowledge from human experts is captured and integrated into the automated crop monitoring systems and methods. Predictive models can also be developed and used to predict future health of plants in a crop.

Abstract: A method includes obtaining data measurements associated with plants in at least one growing area. The data measurements are associated with one or more characteristics of the plants and one or more characteristics of the at least one growing area. The method also includes processing at least some of the data measurements to identify one or more anomalous plant production issues associated with the plants. At least one anomalous plant production issue is associated with uneven production by the plants in at least part of the at least one growing area. The method further includes generating at least one visualization presenting at least some of the data measurements or processed versions of at least some of the data measurements in a spatial manner that corresponds to the at least one growing area. The at least one visualization identifies information associated with the one or more anomalous plant production issues.

Abstract: A method includes obtaining data measurements associated with plants in at least one growing area. The plants have a common genotype and are grown under different growing or environmental conditions in the at least one growing area. The data measurements are associated with one or more characteristics of the plants and multiple characteristics of the growing or environmental conditions. The method also includes processing at least some of the data measurements to identify one or more of the growing or environmental conditions associated with at least one desired characteristic being expressed in the plants being grown. The method further includes outputting an identification of the one or more growing or environmental conditions identified as achieving a specific genotype or phenotype trait for the plants. The specific genotype or phenotype trait is associated with the at least one desired characteristic.

Abstract: Systems and methods for monitoring and assessing crop health and performance can provide rapid screening of individual plants. The systems and methods have an automated component, and rely primarily on the detection and interpretation of plant-based signals to provide information about crop health. In some cases knowledge from human experts is captured and integrated into the automated crop monitoring systems and methods. Predictive models can also be developed and used to predict future health of plants in a crop.

Abstract: 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.