A system for lightweight multi-branch and multi-scale (LMBMS) re-identification is described herein. The system includes a convolutional neural network trained for person identification, wherein the convolutional neural network comprises a series of residual blocks that obtain input from a head network of the convolutional neural network. The system also includes a plurality of refine blocks, wherein one or more refine blocks take as input features from a residual block of the series of residual blocks, wherein the features are at input at different scales and different resolutions and an output of the plurality of refine blocks is a plurality of features in a same feature space. A channel-wise attention mechanism may merge the plurality of features and generate final dynamic features.

This disclosure relates generally to assessing soil carbon sequestration. One of the driving factors for climate change and global warming is emission of greenhouse gas emissions. Of the possible ways to reduce climate change and global warming adoption, sustainable agricultural practices will enable efficient soil carbon sequestration thereby reducing the greenhouse gases as well as increasing the crop yield. The disclosure is a method and a system for real time assessing soil carbon sequestration of farm based on remote sensing. The soil carbon sequestration of farm is assessed by continuously monitoring the farm at real time based on remote sensing using a plurality of satellite data and a plurality of farming data using several techniques. The several techniques utilized for assessing soil carbon sequestration includes machine learning, a carbon sequestration estimation technique, estimating a crop health index and an adoption index and computing a set of carbon sequestration parameters.

A method is provided for operating a machine for harvesting root crops and/or for separating root crops from further additionally conveyed material that includes at least soil in the form of loose earth and/or soil aggregates, and also, if applicable, leaves and/or stones. By means of at least one electromagnetic, in particular optical, or acoustic image capturing unit, at least one inspection image is captured of at least one portion of the material, moved relative to a machine frame of the machine by at least one transport element, in particular a screen belt. On the basis of at least one inspection data set generated using the inspection image and/or formed by this image, an evaluation device generates an adjustment signal for adjusting at least one operating parameter of the transport element and/or a further transport element of the machine. At least one feature for describing the ability to be screened of the additionally conveyed soil is determined by the evaluation device and is used for adjusting the operating parameter. The invention also relates to a machine for harvesting root crops and a computer program product.

Described are methods for identifying the in-field positions of plant features on a plant by plant basis. These positions are determined based on images captured as a vehicle (e.g., tractor, sprayer, etc.) including one or more cameras travels through the field along a row of crops. The in-field positions of the plant features are useful for a variety of purposes including, for example, generating three-dimensional data models of plants growing in the field, assessing plant growth and phenotypic features, determining what kinds of treatments to apply including both where to apply the treatments and how much, determining whether to remove weeds or other undesirable plants, and so on.

A system for monitoring and recording and processing an activity includes one or more cameras for automatically recording video of the activity. A remote media system is located at the location of the activity. A network media processor and services is communicatively coupled with the remote media system. The remote media system includes one or more AI enabled cameras. The AI enabled camera is configured to record the activity. The network media processor is configured to receive an activation request of the AI enabled camera and the validate the record request. The system may automatically administer a skill-based competition.

Autonomous ground vehicles capture images during operation, and process the images to recognize ground surfaces or features within their vicinity, such as by providing the images to a segmentation network trained to recognize the ground surfaces or features. Semantic maps of the ground surfaces or features are generated from the processed images. A point on a semantic map is selected, and the autonomous ground vehicle is instructed to travel to a location corresponding to the selected point. The point is selected in accordance with one or more goals, such as to maintain the autonomous ground vehicle at a selected distance from a roadway or other hazardous surface, or along a centerline of a sidewalk.

A system for monitoring and recording and processing an activity includes one or more cameras for automatically recording video of the activity. A remote media system is located at the location of the activity. A network media processor and services is communicatively coupled with the remote media system. The remote media system includes one or more AI enabled cameras. The AI enabled camera is configured to record the activity. The network media processor is configured to receive an activation request of the AI enabled camera and the validate the record request.

In a robot (3) in a remote location, an action scene of the robot (3) is determined based on a feature amount derived from its position and motion detection data and video data, and a video parameter or an imaging mode corresponding to the determined action scene is selected. Then, a process of adjusting the selected video parameter for the video data or a process of setting the selected imaging mode to the camera is performed, and the processed video data is transmitted to the information processing apparatus (2) on the user side via the network (4) and displayed on the HMD (1).

The orientation of imagery relative to a compass bearing may be determined based on the position of the sun or other information relating to celestial bodies captured in the image.

An image processing apparatus for controlling white balance of an image is provided. The apparatus detects a white region from image data and calculates a first white balance correction value based on data of the white region. The apparatus also detects, with use of machine learning, a region of a subject that has a preset specific color, from the image data, and calculates a second white balance correction value based on a color of the region of the subject. The apparatus calculates a white balance correction value to be applied to the image data, based on the first white balance correction value and the second white balance correction value.