Abstract: Field data is collected of a field. Each instance of field data contains information that can be used to determine a value corresponding to whether or not a plant is present or absent in a particular location and is referred to as a plant presence value. The plant presence values are aggregated using the position data associated with each instance of field data to generate aggregated plant presence values. Gaps between plots are identified based partly on variations in the plant presence values within the aggregated field data. Information known about a field can be used to heuristically identify gaps in a seed line or used to eliminate locations on a seed line that may look like a gap based on low plant presence values. The aggregated plant presence values can be presented as a heat map of plant presence values showing the relative plant density of the field.

Abstract: A system for plant parameter detection, including: a plant morphology sensor having a first field of view and configured to record a morphology measurement of a plant portion and an ambient environment adjacent the plant, a plant physiology sensor having a second field of view and configured to record a plant physiology parameter measurement of a plant portion and an ambient environment adjacent the plant, wherein the second field of view overlaps with the first field of view; a support statically coupling the plant morphology sensor to the physiology sensor, and a computing system configured to: identify a plant set of pixels within the physiology measurement based on the morphology measurement; determine physiology values for each pixel of the plant set of pixels; and extract a growth parameter based on the physiology values.

Abstract: A plant treatment platform uses a plant detection model to detect plants as the plant treatment platform travels through a field. The plant treatment platform receives image data from a camera that captures images of plants (e.g., crops or weeds) growing in the field. The plant treatment platform applies pre-processing functions to the image data to prepare the image data for processing by the plant detection model. For example, the plant treatment platform may reformat the image data, adjust the resolution or aspect ratio, or crop the image data. The plant treatment platform applies the plant detection model to the pre-processed image data to generate bounding boxes for the plants. The plant treatment platform then can apply treatment to the plants based on the output of the machine-learned model.

Abstract: A plant treatment system automatically adjusts camera operation parameters for a camera used by the plant treatment system to identify and treat plants in a field. The plant treatment system can generate image segments of images received from the camera and classify the image segments based on whether the image segments represent plants. The plant treatment system determines whether each of the image segments is over- or under-exposed and adjusts the camera operation parameters for the camera based on the exposure classification of the image segments. Alternatively, the plant treatment system may use a plant detection model to identify plant pixels within an image that represent plants. The plant treatment system can then determine whether the identified plant pixels are over- or under-exposed and adjust the camera operation parameters accordingly.

Abstract: Field data is collected of a field. Each instance of field data contains information that can be used to determine a value corresponding to whether or not a plant is present or absent in a particular location and is referred to as a plant presence value. The plant presence values are aggregated using the position data associated with each instance of field data to generate aggregated plant presence values. Gaps between plots are identified based partly on variations in the plant presence values within the aggregated field data. Information known about a field can be used to heuristically identify gaps in a seed line or used to eliminate locations on a seed line that may look like a gap based on low plant presence values. The aggregated plant presence values can be presented as a heat map of plant presence values showing the relative plant density of the field.

Abstract: A method including: recording a first image of a first field region; automatically treating a plant within the first region in-situ based on the first image; automatically verifying the plant treatment with a second image of the first region; and automatically treating a second region concurrently with treatment verification.

Abstract: A technique for generating virtual models of plants in a field is described. Generally, this includes recording images of plants in-situ; generating point clouds from the images; generating skeleton segments from the point cloud; classifying a subset of skeleton segments as unique plant features using the images; and growing plant skeletons from skeleton segments classified as unique plant feature. The technique may be used to generate a virtual model of a single, real plant, a portion of a real plant field, and/or the entirety of the real plant field. The virtual model can be analyzed to determine or estimate a variety of individual plant or plant population parameters, which in turn can be used to identify potential treatments or thinning practices, or predict future values for yield, plant uniformity, or any other parameter can be determined from the projected results based on the virtual model.

Abstract: A combine harvester (combine) includes any number of components to harvest plants as the combine travels through a plant field. The components take actions to harvest plants or facilitate harvesting plants. The combine includes any number of sensors to measure the state of the combine as the combine harvests plants. The combine includes a control system to generate actions for the components to harvest plants in the field. The control system includes an agent executing a model that functions to improve the performance of the combine harvesting plants. Performance improvement can be measured by the sensors of the combine. The model is an artificial neural network that receives measurements as inputs and generates actions that improve performance as outputs. The artificial neural network is trained using actor-critic reinforcement learning techniques.

Abstract: A technique for generating virtual models of plants in a field is described. Generally, this includes recording images of plants in-situ; generating point clouds from the images; generating skeleton segments from the point cloud; classifying a subset of skeleton segments as unique plant features using the images; and growing plant skeletons from skeleton segments classified as unique plant feature. The technique may be used to generate a virtual model of a single, real plant, a portion of a real plant field, and/or the entirety of the real plant field. The virtual model can be analyzed to determine or estimate a variety of individual plant or plant population parameters, which in turn can be used to identify potential treatments or thinning practices, or predict future values for yield, plant uniformity, or any other parameter can be determined from the projected results based on the virtual model.

Abstract: Field data is collected of a field. Each instance of field data contains information that can be used to determine a value corresponding to whether or not a plant is present or absent in a particular location and is referred to as a plant presence value. The plant presence values are aggregated using the position data associated with each instance of field data to generate aggregated plant presence values. Gaps between plots are identified based partly on variations in the plant presence values within the aggregated field data. Information known about a field can be used to heuristically identify gaps in a seed line or used to eliminate locations on a seed line that may look like a gap based on low plant presence values. The aggregated plant presence values can be presented as a heat map of plant presence values showing the relative plant density of the field.

Abstract: Field data is collected of a field. Each instance of field data contains information that can be used to determine a value corresponding to whether or not a plant is present or absent in a particular location and is referred to as a plant presence value. The plant presence values are aggregated using the position data associated with each instance of field data to generate aggregated plant presence values. Gaps between plots are identified based partly on variations in the plant presence values within the aggregated field data. Information known about a field can be used to heuristically identify gaps in a seed line or used to eliminate locations on a seed line that may look like a gap based on low plant presence values. The aggregated plant presence values can be presented as a heat map of plant presence values showing the relative plant density of the field.

Abstract: A method including: recording a first image of a first field region; automatically treating a plant within the first region in-situ based on the first image; automatically verifying the plant treatment with a second image of the first region; and automatically treating a second region concurrently with treatment verification.

Abstract: A method of real-time plant selection and removal from a plant field including capturing a first image of a first section of the plant field, segmenting the first image into regions indicative of individual plants within the first section, selecting the optimal plants for retention from the first image based on the first image and the previously thinned plant field sections, sending instructions to the plant removal mechanism for removal of the plants corresponding to the unselected regions of the first image from the second section before the machine passes the unselected regions, and repeating the aforementioned steps for a second section of the plant field adjacent the first section in the direction of machine travel.

Abstract: A system for plant parameter detection, including: a plant morphology sensor having a first field of view and configured to record a morphology measurement of a plant portion and an ambient environment adjacent the plant, a plant physiology sensor having a second field of view and configured to record a plant physiology parameter measurement of a plant portion and an ambient environment adjacent the plant, wherein the second field of view overlaps with the first field of view; a support statically coupling the plant morphology sensor to the physiology sensor, and a computing system configured to: identify a plant set of pixels within the physiology measurement based on the morphology measurement; determine physiology values for each pixel of the plant set of pixels; and extract a growth parameter based on the physiology values.

Abstract: A method including: recording a first image of a first field region; automatically treating a plant within the first region in-situ based on the first image; automatically verifying the plant treatment with a second image of the first region; and automatically treating a second region concurrently with treatment verification.

Abstract: 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.

Abstract: A system for plant parameter detection, including: a plant morphology sensor having a first field of view and configured to record a morphology measurement of a plant portion and an ambient environment adjacent the plant, a plant physiology sensor having a second field of view and configured to record a plant physiology parameter measurement of a plant portion and an ambient environment adjacent the plant, wherein the second field of view overlaps with the first field of view; a support statically coupling the plant morphology sensor to the physiology sensor, and a computing system configured to: identify a plant set of pixels within the physiology measurement based on the morphology measurement; determine physiology values for each pixel of the plant set of pixels; and extract a growth parameter based on the physiology values.

Abstract: A modular system includes a hub and a set of modules removably coupled to the hub. The modules are physically coupled to the frame relative to each other so that each module can operate with respect to a different row of a field. An individual module includes a sensor for capturing field measurement data of individual plants along a row as the modular system moves through the geographic region. An individual module further includes a treatment mechanism for applying a treatment to the individual plants of the row based on the field measurement data before the modular system passes by the individual plants. An individual module further includes a computing device that determines the treatment based on the field measurement data and communicates data to the hub. The hub is communicatively coupled to the modules, so that it may exchange data between the modules and with a remote computing system.

Abstract: A method of real-time plant selection and removal from a plant field including capturing a first image of a first section of the plant field, segmenting the first image into regions indicative of individual plants within the first section, selecting the optimal plants for retention from the first image based on the first image and the previously thinned plant field sections, sending instructions to the plant removal mechanism for removal of the plants corresponding to the unselected regions of the first image from the second section before the machine passes the unselected regions, and repeating the aforementioned steps for a second section of the plant field adjacent the first section in the direction of machine travel.

Abstract: A method including: recording a first image of a first field region; automatically treating a plant within the first region in-situ based on the first image; automatically verifying the plant treatment with a second image of the first region; and automatically treating a second region concurrently with treatment verification.