US 11,852,618 B2
Detecting infection of plant diseases by classifying plant photos
Yichuan Gui, Pacifica, CA (US); and Wei Guan, Pleasanton, CA (US)
Assigned to CLIMATE LLC, Saint Louis, MO (US)
Filed by CLIMATE LLC, Saint Louis, MO (US)
Filed on Aug. 26, 2020, as Appl. No. 17/003,914.
Application 17/003,914 is a continuation of application No. 16/658,021, filed on Oct. 18, 2019, granted, now 10,761,075.
Claims priority of provisional application 62/748,288, filed on Oct. 19, 2018.
Prior Publication US 2020/0393435 A1, Dec. 17, 2020
This patent is subject to a terminal disclaimer.
Int. Cl. G06N 3/08 (2023.01); G06K 9/62 (2022.01); G01N 33/00 (2006.01); G06F 18/23 (2023.01); G06F 18/2431 (2023.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/10 (2022.01); G06V 20/00 (2022.01)
CPC G01N 33/0098 (2013.01) [G06F 18/23 (2023.01); G06F 18/2431 (2023.01); G06N 3/08 (2013.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 20/188 (2022.01); G06V 20/38 (2022.01)] 16 Claims
OG exemplary drawing
 
1. A system for configuring and utilizing deep learning for plant disease recognition, the system comprising:
a memory; and
a processor coupled to the memory and configured to perform:
receiving a set of photos of plants showing leaves with a plurality of marked regions having multiple aspect ratios, each marked region being associated with a label of a disease of a plurality of diseases and showing at least one lesion caused by the disease;
dividing, combining, or removing one or more of the plurality of marked regions to create a new set of marked regions in accordance with a restriction on a size of a marked region, on a size proportion of a cluster of lesions within a marked region to a leaf, or on a density of lesions within a marked region based on a predefined percentage;
normalizing one or more of the marked regions in the new set of marked regions based on a fixed distance between a camera and a plant and a fixed camera resolution;
determining, by the processor, a group of anchor boxes from the new set of marked regions for each of a series of convolutional layers of a single shot multibox detector (SSD), the SSD configured to receive an image and assign each of one or more areas of the image into at least one of a plurality of classes corresponding to the plurality of diseases, the group of anchor boxes having distinct aspect ratios and corresponding to various features of the plurality of classes;
mapping each of the plurality of marked regions to one of the groups of anchor boxes;
building the SSD from the group of anchor boxes, the set of photos having the plurality of marked regions, the associated plurality of labels, and the associated plurality of mappings;
receiving a new image from a client device; and
applying the SSD to the new image to identify symptoms of one or more diseases in one or more areas of the new image.