Abstract: Systems, techniques, and devices for detecting plant biometrics, for example, plants in a crop field. An imaging device of an unmanned vehicle may be used to generate a plurality of images of the plants, and the plurality of images may be used to generate a 3D model of the plants. The 3D model may define locations and orientations of leaves and stems of plants. The 3D model may be used to determine at least one biometric parameter of at least one plant in the crop. Such detection of plant biometrics may facilitate the automation of crop monitoring and treatment.

Abstract: Systems, techniques, and devices for detecting plant biometrics, for example, plants in a crop field. An imaging device of an unmanned vehicle may be used to generate a plurality of images of the plants, and the plurality of images may be used to generate a 3D model of the plants. The 3D model may define locations and orientations of leaves and stems of plants. The 3D model may be used to determine at least one biometric parameter of at least one plant in the crop. Such detection of plant biometrics may facilitate the automation of crop monitoring and treatment.

Abstract: Systems, techniques, and devices for detecting plant biometrics, for example, plants in a crop field. An imaging device of an unmanned vehicle may be used to generate a plurality of images of the plants, and the plurality of images may be used to generate a 3D model of the plants. The 3D model may define locations and orientations of leaves and stems of plants. The 3D model may be used to determine at least one biometric parameter of at least one plant in the crop. Such detection of plant biometrics may facilitate the automation of crop monitoring and treatment.

Abstract: Systems, techniques, and devices for detecting plant biometrics, for example, plants in a crop field. An imaging device of an unmanned vehicle may be used to generate a plurality of images of the plants, and the plurality of images may be used to generate a 3D model of the plants. The 3D model may define locations and orientations of leaves and stems of plants. The 3D model may be used to determine at least one biometric parameter of at least one plant in the crop. Such detection of plant biometrics may facilitate the automation of crop monitoring and treatment.

Abstract: A sensor network provides the ability to detect, classify and identify a diverse range of agents over a large area, such as a geographical region or building. The network possesses speed of detection, sensitivity, and specificity for the diverse range of agents. Different functional level types of sensors are employed in the network to perform early warning, broadband detection and highly specific and sensitive detection. A high probability of detection with low probability of false alarm is provided by the processing of information provided from multiple sensors. A Bayesian net is utilized to combine probabilities from the multiple sensors in the network to reach a decision regarding the presence or absence of a threat. The network is field portable and capable of autonomous operation. It also is capable of providing automated output decisions.

Abstract: A sensor network provides the ability to detect, classify and identify a diverse range of agents over a large area, such as a geographical region or building. The network possesses speed of detection, sensitivity, and specificity for the diverse range of agents. Different functional level types of sensors are employed in the network to perform early warning, broadband detection and highly specific and sensitive detection. A high probability of detection with low probability of false alarm is provided by the processing of information provided from multiple sensors. A Bayesian net is utilized to combine probabilities from the multiple sensors in the network to reach a decision regarding the presence or absence of a threat. The network is field portable and capable of autonomous operation. It also is capable of providing automated output decisions.

Abstract: A sensor network provides the ability to detect, classify and identify a diverse range of agents over a large area, such as a geographical region or building. The network possesses speed of detection, sensitivity, and specificity for the diverse range of agents. Different functional level types of sensors are employed in the network to perform early warning, broadband detection and highly specific and sensitive detection. A high probability of detection with low probability of false alarm is provided by the processing of information provided from multiple sensors. A Bayesian net is utilized to combine probabilities from the multiple sensors in the network to reach a decision regarding the presence or absence of a threat. The network is field portable and capable of autonomous operation. It also is capable of providing automated output decisions.

Abstract: A sensor network provides the ability to detect, classify and identify a diverse range of agents over a large area, such as a geographical region or building. The network possesses speed of detection, sensitivity, and specificity for the diverse range of agents. Different functional level types of sensors are employed in the network to perform early warning, broadband detection and highly specific and sensitive detection. A high probability of detection with low probability of false alarm is provided by the processing of information provided from multiple sensors. A Bayesian net is utilized to combine probabilities from the multiple sensors in the network to reach a decision regarding the presence or absence of a threat. The network is field portable and capable of autonomous operation. It also is capable of providing automated output decisions.