The root is an important organ for plants to obtain nutrients and water,and its phenotypic characteristics are closely related to its functions.Deep-learning-based high-throughput in situ root senescence feature extra...The root is an important organ for plants to obtain nutrients and water,and its phenotypic characteristics are closely related to its functions.Deep-learning-based high-throughput in situ root senescence feature extraction has not yet been published.In light of this,this paper suggests a technique based on the transformer neural network for retrieving cotton's in situ root senescence properties.High-resolution in situ root pictures with various levels of senescence are the main subject of the investigation.展开更多
The root is an important organ for crops to absorb water and nutrients.Complete and accurate acquisition of root phenotype information is important in root phenomics research.The in situ root research method can obtai...The root is an important organ for crops to absorb water and nutrients.Complete and accurate acquisition of root phenotype information is important in root phenomics research.The in situ root research method can obtain root images without destroying the roots.In the image,some of the roots are vulnerable to soil shading,which severely fractures the root system and diminishes its structural integrity.The methods of ensuring the integrity of in situ root identification and establishing in situ root image phenotypic restoration remain to be explored.Therefore,based on the in situ root image of cotton,this study proposes a root segmentation and reconstruction strategy,improves the UNet model,and achieves precise segmentation.It also adjusts the weight parameters of EnlightenGAN to achieve complete reconstruction and employs transfer learning to implement enhanced segmentation using the results of the former two.The research results show that the improved UNet model has an accuracy of 99.2%,mIOU of 87.03%,and F1 of 92.63%.The root reconstructed by EnlightenGAN after direct segmentation has an effective reconstruction ratio of 92.46%.This study enables a transition from supervised to unsupervised training of root system reconstruction by designing a combination strategy of segmentation and reconstruction network.It achieves the integrity restoration of in situ root system pictures and offers a fresh approach to studying the phenotypic of in situ root systems,also realizes the restoration of the integrity of the in situ root image,and provides a new method for in situ root phenotype study.展开更多
基金supported by grants from the National Natural Science Foundation of China(nos.32272220 and 32172120)the Top-notch Talent Plan Program of the Education Department of Hebei Province(BJ2021058)+2 种基金Central Guiding Local Science and Technology Development Fund Project(236Z7402G)S&T Program of Hebei(23567601H)State Key Laboratory of North China Crop Improvement and Regulation(NCCIR2024ZZ-18).
文摘The root is an important organ for plants to obtain nutrients and water,and its phenotypic characteristics are closely related to its functions.Deep-learning-based high-throughput in situ root senescence feature extraction has not yet been published.In light of this,this paper suggests a technique based on the transformer neural network for retrieving cotton's in situ root senescence properties.High-resolution in situ root pictures with various levels of senescence are the main subject of the investigation.
基金supported by grants from the National Natural Science Foundation of China(Nos.32272220 and 32172120)Top-notch Talent Plan Program of the Education Department of Hebei Province(BJ2021058)State Key Laboratory of North China Crop Improvement and Regulation(NCCIR2021ZZ-23).
文摘The root is an important organ for crops to absorb water and nutrients.Complete and accurate acquisition of root phenotype information is important in root phenomics research.The in situ root research method can obtain root images without destroying the roots.In the image,some of the roots are vulnerable to soil shading,which severely fractures the root system and diminishes its structural integrity.The methods of ensuring the integrity of in situ root identification and establishing in situ root image phenotypic restoration remain to be explored.Therefore,based on the in situ root image of cotton,this study proposes a root segmentation and reconstruction strategy,improves the UNet model,and achieves precise segmentation.It also adjusts the weight parameters of EnlightenGAN to achieve complete reconstruction and employs transfer learning to implement enhanced segmentation using the results of the former two.The research results show that the improved UNet model has an accuracy of 99.2%,mIOU of 87.03%,and F1 of 92.63%.The root reconstructed by EnlightenGAN after direct segmentation has an effective reconstruction ratio of 92.46%.This study enables a transition from supervised to unsupervised training of root system reconstruction by designing a combination strategy of segmentation and reconstruction network.It achieves the integrity restoration of in situ root system pictures and offers a fresh approach to studying the phenotypic of in situ root systems,also realizes the restoration of the integrity of the in situ root image,and provides a new method for in situ root phenotype study.
