| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_af3c713a5715334b29d793137705ad10 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20081
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-30188
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10004
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20221 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-62
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-241
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-12
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-13
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-62
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-12
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-13
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-82
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-44
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-764 |
| filingDate |
2021-06-25-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_c77a379af3618e8ccdec1776f25b29f8
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_b59c4606cd71f792c55fb25703011b47
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_5e2b2ea98cc202c0ed030fb6543d89f5
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_a9f4a6a26c48adf99fa9f35a8aa92738
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_e90b98854857d7cdad200b9e91dcd4fb |
| publicationDate |
2022-01-14-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
CN-113936019-A |
| titleOfInvention |
A method for field crop yield estimation based on convolutional neural network technology |
| abstract |
The invention discloses a field crop yield estimation method based on convolutional neural network technology. The estimation method is as follows: Step 1: Separating the fruiting part of a single plant from the fruiting photos of the field crops, and using a computer vision algorithm to extract a single plant The outline of the solid part; Step 2: classify the separated solid parts of each individual plant with their outline photos; Step 3: Use various types and states of the outline photos of the solid parts of a single plant to carry out model training on the solid parts of the field crops; the present invention The beneficial effects are: the present invention adopts the deep convolutional neural network technology, compared with manual counting, the time is shorter, and the result is more accurate; Mark the solid parts with different occlusion degrees to improve the recognition accuracy of the model to the occluded solid parts; all stored pictures have been strictly identified and classified to remove duplication and ambiguity, and the quality of the database is guaranteed. |
| isCitedBy |
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-114267002-B
http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-114267002-A |
| priorityDate |
2021-06-25-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |