http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-111784650-A
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_e141294a8e84888bfa52450f0659691c |
| classificationCPCAdditional | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-10081 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20084 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T2207-20081 |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N21-39 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-048 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06T7-0002 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01N21-39 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06T7-00 |
| filingDate | 2020-06-24-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_636bd8f339fe8aa5402a851a0f832924 |
| publicationDate | 2020-10-16-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-111784650-A |
| titleOfInvention | Detection method of fruit moisture content based on graphic image processing technology |
| abstract | The invention belongs to the technical field of image processing and artificial intelligence, and in particular relates to a method for detecting moisture content of fruits based on graphic image processing technology, including a model building method and a detection and identification method; the model building method includes the following steps: S11: Obtaining laser speckles of fruit slices Image and corresponding true moisture content; S12: Convert laser speckle image to grayscale image; S13: Randomly divide speckle grayscale image into training set and test set; S14: Construct convolutional neural network; S15: Training volume integrated neural network; S16: real moisture content detection and verification; the detection and identification method includes the following steps: S21: obtain the laser speckle image of the sample to be detected; S22: convert the laser speckle image into a grayscale image; S23: convert the speckle gray The water content image corresponds to the real water content detection model; S24: Use the real water content detection model to identify the current speckle grayscale image, and obtain the actual real water content of the current sample; S25: Compare the actual real water content with the preset value . |
| isCitedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113776982-A http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-112288719-A |
| priorityDate | 2020-06-24-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
| Predicate | Subject |
|---|---|
| isDiscussedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID419512635 http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID962 |
Total number of triples: 25.