公开(公告)号：US12088773B1

公开(公告)日：2024-09-10

申请号：US18444378

申请日：2024-02-16

Applicant: Tartan Aerial Sense Tech Private Limited

Inventor： Dhivakar Kanagaraj ,  Pranav M P ,  Raghul Raghu ,  Parth Gupta ,  Ananya Mahapatra

IPC: G06K9/36 ,  G06T11/60 ,  G06V10/25 ,  G06V10/56 ,  G06V20/10 ,  H04N1/60

CPC classification number: H04N1/6002 ,  G06T11/60 ,  G06V10/25 ,  G06V10/56 ,  G06V20/188

Abstract: A camera apparatus includes control circuitry configured to acquire an input color image of an agricultural field, detect one or more foliage regions, and generate output binary mask images of foliage mask indicating one or more foliage regions and a soil region. The control circuitry is configured to convert the input color image to a Hue, Saturation, Lightness (HSV) color space to obtain an HSV image. Thereafter, the control circuitry is configured to selectively adjust a hue component and convert back to the RGB color space to obtain a soil region-adjusted RGB image. Furthermore, generate an augmented color image by combining pixels of the soil region, with pixels of the one or more foliage regions and utilize the generated augmented color image in training of a crop detection (CD) neural network model to learn a plurality of different types of soil and a range of color variation of soil.

公开(公告)号：US12080051B1

公开(公告)日：2024-09-03

申请号：US18582148

申请日：2024-02-20

Applicant: Tartan Aerial Sense Tech Private Limited

Inventor： Dhivakar Kanagaraj ,  Pranav M P ,  Raghul Raghu ,  Parth Gupta ,  Vijay Sundaram

IPC: G06V10/774 ,  G06V10/26 ,  G06V10/30 ,  G06V10/764 ,  G06V10/776 ,  G06V10/82 ,  G06V20/10

CPC classification number: G06V10/774 ,  G06V10/26 ,  G06V10/30 ,  G06V10/764 ,  G06V10/776 ,  G06V10/82 ,  G06V20/188

Abstract: A camera apparatus includes one or more processors configured to determine a plurality of crop image data variation classifications representative of real-world variations in physical appearance of a crop plant as well as a surrounding area around the crop plant. Furthermore, select a first set of input color images from first training dataset comprising a plurality of different field-of-views (FOVs). Thereafter, execute plurality of different image level augmentation operations to obtain an augmented set of color images, identify and filter noisy images from a second training dataset based on a predefined set of image parameters. After that, train neural network model in a first stage on a third training dataset, re-determine new crop image data variation classifications and re-select new color images representative of the new crop image data variation classifications to further train the neural network model in a second stage to detect one or more crop plants.

公开(公告)号：US20250133177A1

公开(公告)日：2025-04-24

申请号：US18791123

申请日：2024-07-31

Applicant: Tartan Aerial Sense Tech Private Limited

Inventor： Dhivakar Kanagaraj ,  Pranav M P ,  Raghul Raghu ,  Parth Gupta ,  Ananya Mahapatra

IPC: H04N1/60 ,  G06T11/60 ,  G06V10/25 ,  G06V10/56 ,  G06V20/10

Abstract: A training server acquires an input color image of an agricultural field, detects one or more foliage regions in the input color image, and generates output binary mask images of foliage mask indicating one or more foliage regions and a soil region. The training server further generates an augmented color image by combining pixels of the soil region adjusted for soil hue, with pixels of the one or more foliage regions unaltered from the acquired input color image in the RGB color space. The training server then utilizes the generated augmented color image in training of a crop detection (CD) neural network model.

公开(公告)号：US20250131695A1

公开(公告)日：2025-04-24

申请号：US18787986

申请日：2024-07-29

Applicant: Tartan Aerial Sense Tech Private Limited

Inventor： Dhivakar Kanagaraj ,  Pranav M P ,  Raghul Raghu ,  Parth Gupta ,  Vijay Sundaram

IPC: G06V10/774 ,  G06V10/26 ,  G06V10/30 ,  G06V10/764 ,  G06V10/776 ,  G06V10/82 ,  G06V20/10

Abstract: A training server includes one or more processors configured to determine a plurality of crop image data variation classifications representative of real-world variations in physical appearance of a crop plant as well as a surrounding area around the crop plant. A first set of input color images is selected from first training dataset and a plurality of different image level augmentation operations are executed to obtain an augmented set of color images. Noisy images are identified and filtered from a second training dataset and a third training dataset comprising noise filtered images from the second training dataset is obtained. The third training dataset is split into a plurality of different classes for data balancing across the plurality of different classes and a neural network model in a first stage is trained on the third training dataset.