Identification of cotton in-situ root phenotypes based on deep learning
In situ root system research is an important method for exploring root system morphology and dynamic changes,and it has been widely applied.However,traditional methods for image segmentation of root systems suffer from low efficiency and poor accuracy,which are key obstacles to in situ root system research.To achieve efficient and accurate segmentation of in situ root system images,this paper designed and improved a U-Net network based on semantic segmentation.SE modules were incorporated in the skip-connection,and the optimizer was replaced with Lion,enabling precise identification of in situ root system phenotype.Furthermore,a 1D-CNN network was employed to extract features of phenotypic information from the in situ root system.The validation results showed that the improved U-Net achieved a 1.57%increase in accuracy and a 3.41%improvement in intersection over union(IoU)compared with that of the original model.The identification accuracy of phenotype parameter using 1D-CNN was 90.9%.This study realized efficient and accurate identification and segmentation of in situ root systems through deep learning method,providing important support for in situ root system research in cotton.
cotton in situ rootphenotypic identificationimprove U-Net1D-CNN
NETL
NSTL
万方数据
