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87 results about "Helianthus annuus seed" patented technology

Helianthus Annuus (Sunflower) Seed Oil is the oil expressed from the seeds of the sunflower, Helianthus annuus, consisting primarily of triglycerides of linoleic and oleic acids. It is commonly used in food as a frying oil and supplies more vitamin E than any other vegetable oil.

Double-layer essence with repair and anti-aging functions, method for preparing double-layer essence and application thereof

The invention belongs to the technical field of cosmetics, and particularly discloses double-layer essence with repair and anti-aging functions. The double-layer essence comprises, by weight, 1.0%-2.0% of PEG-40 (polyethylene glycol-40) hydrogenated castor oil, 5%-19% of methyl trimethicone, 4%-6% of dicaprylyl carbonate, 0.5%-1.5% of cetyl dimethicone, 0.5%-1.5% of helianthus annuus seed oil, 3%-9% of glycerin, 0.1%-0.15% of sodium hyaluronate, 0.5%-1.5% of myrothamnus flabellifolia leaf/stem extract, 1%-3% of polypeptides with palmitoyl tripeptide-1 and palmitoyl tetrapeptide-7, 0.5%-1.5% of lactobacillus ferment lysate, 1%-3% of glycosphingolipid, 0.8%-1% of mixtures of glycerol caprylate, p-hydroxy phenyl ethyl ketone and glyceryl laurate and the balance water. The double-layer essence has the advantages that the double-layer essence which is a novel ultralow-viscosity double-layer system is unique in structure and convenient to use; formulas and structures of the double-layer essence are optimized, and accordingly the double-layer essence is free of potential irritation and allergy sources such as perfume and preservatives, is in mild and non-irritant formulas and has the excellent repair and anti-aging functions.
Owner:GUANGDONG MARUBI BIOLOGICAL TECH CO LTD

Seed sorting method for sunflower crop on basis of deep convolutional neural network

The invention provides a seed sorting method for a sunflower crop on the basis of a deep convolutional neural network. The seed sorting method comprises the steps of: firstly, collecting original RGBsunflower seed image data, carrying out marking, randomly dividing the original RGB sunflower seed image data into a training sample set and a verification sample set, and carrying out amplification and standardization on the training sample set and a verification sample set; secondly, constructing a deep convolutional neural network by utilizing standard convolution, a residual module containingan attention mechanism module, a pooling layer and a classifier, training the training sample set, and optimizing network parameters in the deep convolutional neural network by combining a random gradient descent algorithm so as to obtain a deep convolutional neural network model; and finally, verifying the deep convolutional neural network model by using the verification sample set, and testing the recognition capability of the model. According to the invention, the memory required for training the model is reduced; features with high robustness can be automatically learned and extracted froma color sample image of sunflower seeds; meanwhile, the method has high recognition rate.
Owner:ZHONGYUAN ENGINEERING COLLEGE

Organic tea cultivation method

InactiveCN105830687AMeet the requirements of cultivationGet clean effectExcrement fertilisersFertilising methodsFermentationLivestock
The patent discloses an organic tea cultivation method in the technical field of similar tree cultivation. The organic tea cultivation method comprises: preparing an organic fertilizer: collecting old branches and leaves fallen from tea trees, and mixing the old branches and leaves with livestock dung to prepare the organic fertilizer through fermentation; eliminating chemical fertilizers in soil: uniformly sowing rape seeds, sunflower seeds or wheat seeds in a selected soil, and performing deep tillage on the soil after the harvest of rape, sunflower or wheat; applying a base fertilizer, and performing plowing; uniformly spreading the organic fertilizer on the soil, and then performing plowing to ensure that the organic fertilizer is mixed with the soil; performing ridging and digging holes; selecting seedlings, and performing transplantation; performing field management: performing top application; placing a plurality of yellow plates in a tea garden; and decocting a traditional Chinese medicine decoction, and uniformly spraying the decoction to trunks of tea trees and the front sides and the back sides of tea leaves. The organic tea cultivation method disclosed by the invention achieves the following beneficial technical effects: the purpose of eliminating residual chemical fertilizers and pesticides from the source is realized, a pure natural nutrition fertilizer is adopted to perform cultivation, traditional Chinese medicines and a physical method is adopted to prevent and expel insects, and pure natural high-quality organic tea leaves are obtained.
Owner:GUIZHOU PROVINCE ZHENGAN COUNTY YIREN TEA IND CO LTD

Sunflower seed sorting method based on double-branch convolutional neural network

The invention provides a sunflower seed sorting method based on a double-branch convolutional neural network, which comprises the following steps: firstly, labeling class labels on acquired original images of sunflower seeds, randomly dividing the original images into a training set and a test set, and then carrying out data amplification on the training set and the test set to form an amplification training set and an amplification test set; secondly, constructing a double-branch convolutional neural network of which the network structure is an input layer-feature extraction layer-output layer; inputting the amplification training set into a double-branch convolutional neural network for training to obtain a sunflower seed sorting model based on the double-branch convolutional neural network; and finally, verifying the sunflower seed sorting model by utilizing the amplification test set, and testing the recognition capability of the sunflower seed sorting model. According to the method, the utilization rate of the model for front-layer lower-level features is increased, the storage space of the network model on hardware equipment is reduced, and meanwhile the method has the advantages of being high in robustness and high in recognition precision.
Owner:ZHONGYUAN ENGINEERING COLLEGE
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