Image-based root phenotyping technologies,including the minirhizotron(MR),have expanded our understanding of the in situ root responses to changing environmental conditions.The conventional manual methods used to anal...Image-based root phenotyping technologies,including the minirhizotron(MR),have expanded our understanding of the in situ root responses to changing environmental conditions.The conventional manual methods used to analyze MR images are time-consuming,limiting their implementation.This study presents an adaptation of our previously developed convolutional neural network-based models to estimate the total(cumulative)root length(TRL)per MR image without requiring segmentation.Training data were derived from manual annotations in Rootfly,commonly used software for MR image analysis.We compared TRL estimation with 2 models,a regression-based model and a detection-based model that detects the annotated points along the roots.Notably,the detection-based model can assist in examining human annotations by providing a visual inspection of roots in MR images.The models were trained and tested with 4,015 images acquired using 2 MR system types(manual and automated)and from 4 crop species(corn,pepper,melon,and tomato)grown under various abiotic stresses.These datasets are made publicly available as part of this publication.The coefficients of determination(R2),between the measurements made using Rootfly and the suggested TRL estimation models were 0.929 to 0.986 for the main datasets,demonstrating that this tool is accurate and robust.Additional analyses were conducted to examine the effects of(a)the data acquisition system and thus the image quality on the models'performance,(b)automated differentiation between images with and without roots,and(c)the use of the transfer learning technique.These approaches can support precision agriculture by providing real-time root growth information.展开更多
基金supported by the Israeli Ministry of Agriculture and Rural Development(Eugene Kandel Knowledge Centers)as part of the“Root of the Matter-The Root Zone Knowledge Center for Leveraging Modern Agriculture",grant number 16-38-0044 of the Israeli Ministry of Agriculture and Rural Development,and the European Union's Horizon 2020 Research and Innovation Program(grant agreement no.777222)(ATTRACT project“NextMR-IAA”)Partial support was provided by Ben-Gurion University of the Negev and the W.Gunther Plaut Chair in Manufacturing Engineering.
文摘Image-based root phenotyping technologies,including the minirhizotron(MR),have expanded our understanding of the in situ root responses to changing environmental conditions.The conventional manual methods used to analyze MR images are time-consuming,limiting their implementation.This study presents an adaptation of our previously developed convolutional neural network-based models to estimate the total(cumulative)root length(TRL)per MR image without requiring segmentation.Training data were derived from manual annotations in Rootfly,commonly used software for MR image analysis.We compared TRL estimation with 2 models,a regression-based model and a detection-based model that detects the annotated points along the roots.Notably,the detection-based model can assist in examining human annotations by providing a visual inspection of roots in MR images.The models were trained and tested with 4,015 images acquired using 2 MR system types(manual and automated)and from 4 crop species(corn,pepper,melon,and tomato)grown under various abiotic stresses.These datasets are made publicly available as part of this publication.The coefficients of determination(R2),between the measurements made using Rootfly and the suggested TRL estimation models were 0.929 to 0.986 for the main datasets,demonstrating that this tool is accurate and robust.Additional analyses were conducted to examine the effects of(a)the data acquisition system and thus the image quality on the models'performance,(b)automated differentiation between images with and without roots,and(c)the use of the transfer learning technique.These approaches can support precision agriculture by providing real-time root growth information.
