US 12,080,051 B1
Camera apparatus and method of detecting crop plants irrespective of crop image data variations
Dhivakar Kanagaraj, Karur (IN); Pranav M P, Bengaluru (IN); Raghul Raghu, Madurai (IN); Parth Gupta, New Delhi (IN); and Vijay Sundaram, Tamil Nadu (IN)
Assigned to Tartan Aerial Sense Tech Private Limited, Bangalore (IN)
Filed by Tartan Aerial Sense Tech Private Limited, Bangalore (IN)
Filed on Feb. 20, 2024, as Appl. No. 18/582,148.
Claims priority of application No. 202341071593 (IN), filed on Oct. 19, 2023.
Int. Cl. G06V 10/774 (2022.01); G06V 10/26 (2022.01); G06V 10/30 (2022.01); G06V 10/764 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01); G06V 20/10 (2022.01)
CPC G06V 10/774 (2022.01) [G06V 10/26 (2022.01); G06V 10/30 (2022.01); G06V 10/764 (2022.01); G06V 10/776 (2022.01); G06V 10/82 (2022.01); G06V 20/188 (2022.01)] 20 Claims
OG exemplary drawing
 
1. A camera apparatus, comprising:
one or more processors configured to:
determine a plurality of crop image data variation classifications representative of real-world variations in a physical appearance of a crop plant as well as a surrounding area around the crop plant;
select a first set of input color images from a first training dataset comprising a plurality of different field-of-views (FOVs) of one or more agricultural fields, based on the determined plurality of crop image data variation classifications;
execute a plurality of different image level augmentation operations on the first set of input color images to obtain an augmented set of color images;
identify and filter noisy images from a second training dataset comprising the first set of input color images and the augmented set of color images based on a predefined set of image parameters;
train a neural network model in a first stage on a third training dataset comprising noise filtered images from the second 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; and
further train the neural network model in a second stage from the new color images representative of the new crop image data variation classifications to detect one or more crop plants.