| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_138d134e402bcd1c2152370ccb09cb62 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-006
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-126 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-23
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06K9-628
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V20-188
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-764
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-82
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-2431
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06K9-6218
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V20-38
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N33-0098
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01N33-00
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-764
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08 |
| filingDate |
2019-10-18^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate |
2020-09-01^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_a1f431d085c4bed43308ba71a18f1ef5
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_4b123e4f6400cc2adca903a8c8da5975 |
| publicationDate |
2020-09-01^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-10761075-B2 |
| titleOfInvention |
Detecting infection of plant diseases by classifying plant photos |
| abstract |
A system and processing methods for configuring and utilizing a convolutional neural network (CNN) for plant disease recognition are disclosed. In some embodiments, the system is programmed to collect photos of infected plants or leaves where regions showing symptoms of infecting diseases are marked. Each photo may have multiple marked regions. Depending on how the symptoms are sized or clustered, one marked region may include only one lesion caused by one disease, while another may include multiple, closely-spaced lesions caused by one disease. The system is programmed to determine anchor boxes having distinct aspect ratios from these marked regions for each convolutional layer of a single shot multibox detector (SSD). For certain types of plants, common diseases lead to relatively many aspect ratios, some having relatively extreme values. The system is programmed to then train the SSD using the marked regions and the anchor boxes and apply the SSD to new photos to identify diseased plants. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11763400-B2
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11688210-B2
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-11682085-B2
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/WO-2022106302-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-2022108543-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-2022012957-A1
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/US-2019057462-A1 |
| priorityDate |
2018-10-19^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |