公开(公告)号：US12190501B2

公开(公告)日：2025-01-07

申请号：US18371838

申请日：2023-09-22

Applicant: Deere & Company

Inventor： Jie Yang ,  Zhiqiang Yuan ,  Hongxu Ma ,  Cheng-en Guo ,  Elliott Grant ,  Yueqi Li

IPC: G06V10/74 ,  G05D1/00 ,  G06F18/20 ,  G06F18/214 ,  G06N3/04 ,  G06N3/08 ,  G06T7/00 ,  G06V10/20 ,  G06V10/764 ,  G06V10/776 ,  G06V10/82 ,  G06V20/10 ,  G06V20/20

Abstract: Implementations are described herein for training and applying machine learning models to digital images capturing plants, and to other data indicative of attributes of individual plants captured in the digital images, to recognize individual plants in distinction from other individual plants. In various implementations, a digital image that captures a first plant of a plurality of plants may be applied, along with additional data indicative of an additional attribute of the first plant observed when the digital image was taken, as input across a machine learning model to generate output. Based on the output, an association may be stored in memory, e.g., of a database, between the digital image that captures the first plant and one or more previously-captured digital images of the first plant.

公开(公告)号：US20240411837A1

公开(公告)日：2024-12-12

申请号：US18813324

申请日：2024-08-23

Applicant: Deere & Company

Inventor： Zhiqiang Yuan

IPC: G06F18/24 ,  A01C21/00 ,  A01D46/30 ,  A01M21/00 ,  B64U101/30 ,  B64U101/40 ,  G06T7/33 ,  G06V10/75 ,  G06V20/10

Abstract: Systems, apparatus, articles of manufacture, and methods are disclosed. An example agricultural robot comprises interface circuitry; machine readable instructions; and at least one processor to execute the machine readable instructions to: spatially align, by execution of a trained machine learning model, an invariant anchor point within high-elevation images and a plant whose wind-triggered deformation is perceptible between the high-elevation images; localize, by execution of the trained machine learning model, the plant based on the spatial alignment; and cause the agricultural robot to perform, in response to the localization, one or more agricultural tasks to the plant.

公开(公告)号：US12112501B2

公开(公告)日：2024-10-08

申请号：US17354147

申请日：2021-06-22

Applicant: Deere & Company

Inventor： Zhiqiang Yuan ,  Jie Yang

IPC: G06F18/24 ,  B25J11/00 ,  G06T3/40 ,  G06T7/33 ,  G06T7/73 ,  G06V20/10

CPC classification number: G06T7/74 ,  G06F18/24 ,  G06T3/40 ,  G06T7/33 ,  G06V20/182 ,  G06V20/188 ,  B25J11/00 ,  G06T2207/10032 ,  G06T2207/20081 ,  G06T2207/30188

Abstract: Implementations are described herein for localizing individual plants using high-elevation images at multiple different resolutions. A first set of high-elevation images that capture the plurality of plants at a first resolution may be analyzed to classify a set of pixels as invariant anchor points. High-elevation images of the first set may be aligned with each other based on the invariant anchor points that are common among at least some of the first set of high-elevation images. A mapping may be generated between pixels of the aligned high-elevation images of the first set and spatially-corresponding pixels of a second set of higher-resolution high-elevation images. Based at least in part on the mapping, individual plant(s) of the plurality of plants may be localized within one or more of the second set of high-elevation images for performance of one or more agricultural tasks.

公开(公告)号：US12111888B2

公开(公告)日：2024-10-08

申请号：US17344328

申请日：2021-06-10

Applicant: DEERE & COMPANY

Inventor： Zhiqiang Yuan

IPC: G06F18/24 ,  A01D46/30 ,  A01M21/00 ,  B64C39/02 ,  G06T7/33 ,  G06V10/75 ,  G06V20/10 ,  A01C21/00 ,  B64U101/30

CPC classification number: G06F18/24 ,  B64C39/024 ,  G06T7/33 ,  G06V10/751 ,  G06V20/182 ,  G06V20/188 ,  A01C21/007 ,  A01D46/30 ,  A01M21/00 ,  B64U2101/30 ,  G06T2207/10032 ,  G06T2207/30184 ,  G06T2207/30188

Abstract: Implementations are described herein for localizing individual plants by aligning high-elevation images using invariant anchor points while disregarding variant feature points, such as deformable plants. High-elevation images that capture the plurality of plants at a resolution at which wind-triggered deformation of individual plants is perceptible between the high-elevation images may be obtained. First regions of the high-elevation images that depict the plurality of plants may be classified as variant features that are unusable as invariant anchor points. Second regions of the high-elevation images that are disjoint from the first set of regions may be classified as invariant anchor points. The high-elevation images may be aligned based on invariant anchor point(s) that are common among at least some of the high-elevation images. Based on the aligned high-elevation images, individual plant(s) may be localized within one of the high-elevation images for performance of one or more agricultural tasks.

公开(公告)号：US12159457B2

公开(公告)日：2024-12-03

申请号：US18341434

申请日：2023-06-26

Applicant: Deere & Company

Inventor： Bodi Yuan ,  Zhiqiang Yuan ,  Ming Zheng

IPC: G06V20/10 ,  A01C1/02 ,  G06N3/045 ,  G06N3/08 ,  G06T7/00 ,  G06T7/10 ,  G06T7/70 ,  G06T7/90 ,  A01D75/00 ,  A01G25/16 ,  A01M21/00

Abstract: Techniques are described herein for using artificial intelligence to predict crop yields based on observational crop data. A method includes: obtaining a first digital image of at least one plant; segmenting the first digital image of the at least one plant to identify at least one seedpod in the first digital image; for each of the at least one seedpod in the first digital image: determining a color of the seedpod; determining a number of seeds in the seedpod; inferring, using one or more machine learning models, a moisture content of the seedpod based on the color of the seedpod; and estimating, based on the moisture content of the seedpod and the number of seeds in the seedpod, a weight of the seedpod; and predicting a crop yield based on the moisture content and the weight of each of the at least one seedpod.

公开(公告)号：US20250001610A1

公开(公告)日：2025-01-02

申请号：US18755228

申请日：2024-06-26

Applicant: Deere & Company

Inventor： Zhiqiang Yuan ,  Elliott Grant

IPC: B25J9/16 ,  A01B79/00 ,  B25J11/00 ,  G05D1/81

Abstract: Implementations are described herein for coordinating semi-autonomous robots to perform agricultural tasks on a plurality of plants with minimal human intervention. In various implementations, a plurality of robots may be deployed to perform a respective plurality of agricultural tasks. Each agricultural task may be associated with a respective plant of a plurality of plants, and each plant may have been previously designated as a target for one of the agricultural tasks. It may be determined that a given robot has reached an individual plant associated with the respective agricultural task that was assigned to the given robot. Based at least in part on that determination, a manual control interface may be provided at output component(s) of a computing device in network communication with the given robot. The manual control interface may be operable to manually control the given robot to perform the respective agricultural task.

公开(公告)号：US12175303B2

公开(公告)日：2024-12-24

申请号：US17485903

申请日：2021-09-27

Applicant: Deere & Company

Inventor： Zhiqiang Yuan ,  Rhishikesh Pethe ,  Francis Ebong

IPC: G06F9/50 ,  G01N33/24 ,  G06F11/30 ,  G06F17/18

Abstract: Implementations are disclosed for adaptively reallocating computing resources of resource-constrained devices between tasks performed in situ by those resource-constrained devices. In various implementations, while the resource-constrained device is transported through an agricultural area, computing resource usage of the resource-constrained device ma may be monitored. Additionally, phenotypic output generated by one or more phenotypic tasks performed onboard the resource-constrained device may be monitored. Based on the monitored computing resource usage and the monitored phenotypic output, a state may be generated and processed based on a policy model to generate a probability distribution over a plurality of candidate reallocation actions. Based on the probability distribution, candidate reallocation action(s) may be selected and performed to reallocate at least some computing resources between a first phenotypic task of the one or more phenotypic tasks and a different task while the resource-constrained device is transported through the agricultural area.