Plant overground part and underground part phenotype combined identification method based on root box
The invention relates to a plant overground part and underground part phenotype joint identification method based on a root box, the method is based on a crop phenotype information monitoring device, and the method comprises the following steps: filling nutrient soil into the root box, and placing t...
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| creator | HAN RUIXI WANG SONGHAN FU XIUQING LIU SHOUYANG DING YANFENG ZHANG RULEI |
| description | The invention relates to a plant overground part and underground part phenotype joint identification method based on a root box, the method is based on a crop phenotype information monitoring device, and the method comprises the following steps: filling nutrient soil into the root box, and placing the root box on a root box bracket; crop seeds are placed in a root box, and phenotypic data of crops are obtained when the seeds grow to a certain degree; the camera is connected to a computer end, and real-time images of crops are displayed on a screen; marking the acquired images of the overground part and the underground part; a Segform neural network training model is used to train the underground part; segmenting the overground part by using a U-Net method; predicting the image after model training to obtain a segmentation map of the underground part of the crop; predicting the image segmented by the U-Net method to obtain a segmentation map of the crop overground part; performing character extraction on the s |
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crop seeds are placed in a root box, and phenotypic data of crops are obtained when the seeds grow to a certain degree; the camera is connected to a computer end, and real-time images of crops are displayed on a screen; marking the acquired images of the overground part and the underground part; a Segform neural network training model is used to train the underground part; segmenting the overground part by using a U-Net method; predicting the image after model training to obtain a segmentation map of the underground part of the crop; predicting the image segmented by the U-Net method to obtain a segmentation map of the crop overground part; performing character extraction on the s</description><language>chi ; eng</language><subject>AGRICULTURE ; ANIMAL HUSBANDRY ; CALCULATING ; COMPUTING ; COUNTING ; CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPSOR SEAWEED ; FISHING ; FORESTRY ; HORTICULTURE ; HUMAN NECESSITIES ; HUNTING ; MEASURING ; MEASURING ANGLES ; MEASURING AREAS ; 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crop seeds are placed in a root box, and phenotypic data of crops are obtained when the seeds grow to a certain degree; the camera is connected to a computer end, and real-time images of crops are displayed on a screen; marking the acquired images of the overground part and the underground part; a Segform neural network training model is used to train the underground part; segmenting the overground part by using a U-Net method; predicting the image after model training to obtain a segmentation map of the underground part of the crop; predicting the image segmented by the U-Net method to obtain a segmentation map of the crop overground part; performing character extraction on the s</abstract><oa>free_for_read</oa></addata></record> |
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| subjects | AGRICULTURE ANIMAL HUSBANDRY CALCULATING COMPUTING COUNTING CULTIVATION OF VEGETABLES, FLOWERS, RICE, FRUIT, VINES, HOPSOR SEAWEED FISHING FORESTRY HORTICULTURE HUMAN NECESSITIES HUNTING MEASURING MEASURING ANGLES MEASURING AREAS MEASURING IRREGULARITIES OF SURFACES OR CONTOURS MEASURING LENGTH, THICKNESS OR SIMILAR LINEARDIMENSIONS PHYSICS TESTING TRAPPING WATERING |
| title | Plant overground part and underground part phenotype combined identification method based on root box |
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