http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-113408701-A
Outgoing Links
| Predicate | Object |
|---|---|
| assignee | http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_ae036cad0ec2f57bc8cb384c61be292a |
| classificationCPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-045 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16C20-90 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06N3-084 http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G16C20-70 |
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-08 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16C20-90 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G16C20-70 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06N3-04 |
| filingDate | 2021-06-22-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor | http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_bf7fb9dfcd4bcfa0511d65bd5deb7ea8 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_123e81d414e6831b2ca68f533eb48778 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_f6338a2355f7db6b773ded8f37fa9d14 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_4508efcd7a96fed7a42666f00602559c http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_1152eb360967617c2e183b3e23c1990f http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_9927752a7930aa945683afd4398822c6 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_562c45254557729952241a8d172fa4f7 http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_036dd24e502ef97abea5eeb207f03c8b |
| publicationDate | 2021-09-17-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-113408701-A |
| titleOfInvention | A convolutional neural network soil organic matter analysis model building system and method |
| abstract | The invention relates to the fields of remote sensing technology and variable fertilization technology, and more particularly relates to a system and method for constructing a convolutional neural network soil organic matter analysis model, including a raw data sorting module, a band transformation module, and a sensitivity analysis module that operate in a process-based manner , Transform Band Analysis Module, Parameter Input Module, Convolutional Neural Network Building Module, Model Training Module, Accuracy Evaluation Module, Removal of Gross Errors Module, Model Validation Module, Model Storage Module, Model Retraining Module and Results Map Output Module, Sensitivity The analysis module is used to analyze the sensitivity of the original band and the transformed band of the soil sample image to soil organic matter, and to calibrate the input band parameters. It can guide accurate and comprehensive fertilization through the soil organic matter analysis model of remote sensing and convolutional neural network, so as to avoid excessive fertilization. It causes too much cost input, pollutes the environment, and too little fertilization leads to soil compaction, problems affecting crop growth, and damage to the land. |
| priorityDate | 2021-06-22-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
Total number of triples: 40.