Verdant Robotics, Inc.

X Development LLC

Google Inc.

Patrick Christopher Leger

Curtis Dale Garner

Gabriel Thurston Sibley

Lorenzo Ibarria

Andre Robert Daniel Michelin

Benjamin Rewis

Shi Yan

Wisit Jirattigalochote

Nareshkumar Rajkumar

Hasan Tafish

Dustin James Webb

John Phillip Hurliman, Ii

Abhinav Gupta

Rainer Hessmer

Nicolas Hudson

Krishna Shankar

Alexa Greenberg

Ignacio Pablo Mellado Bataller

Wisit Jirattigalachote

Andrew Charles Burns

Gearoid Murphy

Liang-shian Chen

Jacob R Stelzriede

Various embodiments of apparatuses, methods, systems, and computer program products described herein are directed to a treatment system. An example apparatus includes a treatment apparatus having a first fluid source, one or more channels for delivering a first fluid of the first fluid source, and a treatment unit, the treatment unit having one or more fluid regulators, including a first fluid regulator configured to regulate a flow of the first fluid received by the treatment unit, a movable treatment head unit having one or more spraying tips, a first channel fluidly coupling the one or more spraying tips with the first fluid regulator, wherein the first fluid regulator controls a volume of fluid emitted from the one or more spraying tips, an one or more controllers disposed in the treatment unit configured to control the one or more fluid regulators.

Multiaction agricultural sprayer

An agricultural treatment system can include one or more motor assemblies, linkage assemblies coupled to the motor assemblies, and a treatment head assembly coupled to the linkage assemblies. Each motor assembly can rotate about a motor assembly rotational axis and each linkage assembly can move in a direction in response to motor assembly rotation about a motor assembly rotational axis. Portions of the treatment head assembly can rotate about one or more treatment head assembly rotational axes in response to linkage assembly movement. The treatment head assembly can include a treatment head, a fluid port that delivers a fluid into the treatment head, spraying tip(s) that spray the fluid onto an agricultural target object, and a fluid regulator that delivers the fluid from the fluid port to the spraying tip(s). The fluid regulator can be coupled to and can rotate with the treatment head, which can be movable.

Fluid spraying system for targeting small objects

Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to an agricultural observation and treatment system and method of operation. Fluid may be provided through a first pressurized fluid line from a fluid source within an agricultural treatment system to a fluid port of a treatment head assembly of the agricultural treatment system. The fluid may be delivered from the fluid port to one or more spraying tips when the fluid regulator is actuated when in a desired position.

Method of spraying small objects

A method includes traversing, by the treatment system, along a path in an agricultural environment, receiving, by the treatment system, one or more sensor readings comprising one or more agricultural objects, identifying one or more objects of interest from the one or more agricultural objects by analyzing the one or more sensor readings, determining a first target object of the one or more objects of interest for treatment, selecting a treatment policy to treat the first target object, and activating the treatment mechanism to treat the first target object with the treatment policy.

Multiaction treatment of plants in an agricultural environment

Various embodiments of methods, systems, and computer program products described herein are directed to a treatment system. An example method includes identifying a type of a first target agricultural object, based on the identified type of the first target agricultural object, determining a first spray profile for treating the first target agricultural object, sending instructions of a first treatment parameter to a treatment unit, and based on the first spray profile, activating the treatment unit to emit a first fluid at the first target agricultural object, wherein the first fluid regulator is moved to an opened position for an amount of time such that the first fluid is emitted, via the first spraying tip, as a first fluid projectile at the first target agricultural object for a first spray duration.

Performing multiple actions on plant objects on a moving platform

Various embodiments of apparatuses, methods, systems, and computer program products described herein are directed to a treatment system having a first group of in line emitters, a second group of in line emitters, and one or more payload sources, configured to identify a first group of one or more agricultural objects, select a first emitter from the first group of in line emitters to apply a first liquid-based treatment to the identified first group of one or more agricultural objects, identify a second group of one or more agricultural objects, select a second emitter from the second group of in line emitters to apply a second liquid-based treatment to the identified second group of one or more agricultural objects, simultaneously apply the first liquid-based treatment towards the first group of one or more agricultural objects and apply the second liquid-based treatment towards the second group of one or more agricultural objects.

Systems and methods for autonomously applying precision treatements to a group of plant objects

Various embodiments relate generally to computer vision and automation to autonomously identify and deliver for application a treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, and robotics and mobility technologies to navigate a delivery system, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. In some examples, a method may include, at least, detecting an optical sight to align with an associated agricultural object, tracking the agricultural objects relative to the optical sight, predicting a parameter to track in association with agricultural object, and activating an emitter to apply an action based on the parameter.

Targeting agricultural objects via image pixel tracking

Various embodiments relate generally to computer vision and automation to autonomously identify and deliver for application a treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, and robotics and mobility technologies to navigate a delivery system, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. In some examples, a method may include identifying an agricultural object being associated with a subset of agricultural objects, identifying an action corresponding to a subset of agricultural objects responsive, selecting an emitter with which to perform an action associated with the agricultural object, configuring an agricultural projectile delivery system to activate an emitter to propel an agricultural projectile to intercept an agricultural object.

Managing stages of growth of a crop with micro-precision via an agricultural treatment delivery system

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for sharing learned information among robots. In some implementations, a robot obtains sensor data indicating characteristics of an object. The robot determines a classification for the object and generates an embedding for the object using a machine learning model stored by the robot. The robot stores the generated embedding and data indicating the classification for the object. The robot sends the generated embedding and the data indicating the classification to a server system. The robot receives, from the server system, an embedding generated by a second robot and a corresponding classification. The robot stores the received embedding and the corresponding classification in the local cache of the robot. The robot may then use the information in the cache to identify objects.

Sharing learned information among robots

A method and system having instructions to perform actions include obtaining images of an agricultural environment including a first image comprising at least one background portion and one or more regions of interest, implementing a machine learning (ML) algorithm on a portion of the first image including a portion of the background portion and the one or more regions of interest, detecting a plurality of objects associated with a plurality of real-world objects in the agricultural environment in at least one region of interest in the one or more regions of interest of the first image including detecting a first object and detecting a second object, implementing a second algorithm on the portion of the first image comprising the first object to detect one or more divided features of the first object, tracking a feature of the one or more divided features across subsequent images as the moving platform traverses the agricultural environment, tracking the second object, and selecting a target action configured to target the second object and applying the target action via the target mechanism to the second object.

Detecting and tracking features of plants for treatment in an agricultural environment

Various embodiments of an apparatus, methods, systems and computer program products described herein are directed to an agricultural observation and treatment system and method of operation. The agricultural treatment system may determine a first real-world geo-spatial location of the treatment system. The system can receive captured images depicting real-world agricultural objects of a geographic scene. The system can associate captured images with the determined geo-spatial location of the treatment system. The treatment system can identify, from a group of mapped and indexed images, images having a second real-word geo-spatial location that is proximate with the first real-world geo-spatial location. The treatment system can compare at least a portion of the identified images with at least a portion of the captured images. The treatment system can determine a target object and emit a fluid projectile at the target object using a treatment device.

Classifying plant objects in an agricultural environment

Methods, systems, and apparatus, including computer programs encoded on computer storage media for evaluating robot learning. In some implementations, a system receives classification examples from a plurality of remote devices over a communication network. The classification examples can include (i) a data representation generated by a remote device based on sensor data captured by the remote device and (ii) a classification corresponding to the data representation. The system assigns quality scores to the classification examples based on a level of similarity of the data representations with other data representations. The system selects a subset of the classification examples based on the quality scores assigned to the classification examples. The system trains a machine learning model using the selected subset of the classification examples.

Evaluating robot learning

A method performed by a treatment system disposed on a moving platform, the treatment system having one or more processors, a storage and a treatment mechanism, comprising: receiving one or more images of an environment in which the moving platform is operating; identifying, in real-time, a pose of the moving platform using sensor inputs; identifying one or more target objects by processing the one or more images using a machine learning (ML) algorithm; and controlling the treatment mechanism to treat the one or more target objects by orienting the treatment mechanism towards the one or more target objects at least partially based on the pose.

Detection and precision application of treatment to target objects

Computer vision and autonomous identification of objects are used for the application of a treatment to an object. Robotics and mobility technologies navigate a delivery system which is configured to identify and apply an agricultural treatment to an identified agricultural object. The delivery system identifies a subset of payloads to provide one or more actions based on data representing a policy for one or more subsets of agricultural objects. The delivery system causes one or more cartridges to be charged based on the subset of payloads.

Payload selection to treat multiple plant objects having different attributes

Various embodiments relate generally to computer vision and automation to autonomously identify and deliver for application a treatment to an object among other objects, data science and data analysis, including machine learning, deep learning, and other disciplines of computer-based artificial intelligence to facilitate identification and treatment of objects, and robotics and mobility technologies to navigate a delivery system, more specifically, to an agricultural delivery system configured to identify and apply, for example, an agricultural treatment to an identified agricultural object. In some examples, a method may include, receiving data representing a policy specifying a type of action for an agricultural object, selecting an emitter with which to perform a type of action for the agricultural object as one of one or more classified subsets, and configuring the agricultural projectile delivery system to activate an emitter to propel an agricultural projectile to intercept the agricultural object.

Growing community of inventors

Belmont, CA, United States of America

Patrick Christopher Leger