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Eff-3DPSeg:3D Organ-Level Plant Shoot Segmentation Using Annotation-Efficient Deep Learning

Plant Phenomics 2023年第3期5卷 626-641页

作者： Liyi Luo Xintong Jiang Yu Yang Eugene Roy Antony Samy Mark Lefsrud valerio hoyos-villegas Shangpeng Sun Bioresource Engineering Department McGill UniversityMontrealQCCanada Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education) Jiangnan UniversityWuxiJiangsuChina Plant Science Department McGill UniversityMontrealQCCanada

Reliable and automated 3-dimensional(3D)plant shoot segmentation is a core prerequisite for the extraction of plant phenotypic traits at the organ *** deep learning and point clouds can provide effective ways to address the ***,fully supervised deep learning methods require datasets to be point-wise annotated,which is extremely expensive and *** our work,we proposed a novel weakly supervised framework,Eff-3DPSeg,for 3D plant shoot ***,high-resolution point clouds of soybean were reconstructed using a low-cost photogrammetry system,and the Meshlab-based Plant Annotator was developed for plant point cloud ***,a weakly supervised deep learning method was proposed for plant organ *** method contained(a)pretraining a self-supervised network using Viewpoint Bottleneck loss to learn meaningful intrinsic structure representation from the raw point clouds and(b)fine-tuning the pretrained model with about only 0.5%points being annotated to implement plant organ ***,3 phenotypic traits(stem diameter,leaf width,and leaf length)were *** test the generality of the proposed method,the public dataset Pheno4D was included in this *** results showed that the weakly supervised network obtained similar segmentation performance compared with the fully supervised *** method achieved 95.1%,96.6%,95.8%,and 92.2%in the precision,recall,F1 score,and mIoU for stem–leaf segmentation for the soybean dataset and 53%,62.8%,and 70.3%in the AP,AP@25,and AP@50 for leaf instance segmentation for the Pheno4D *** study provides an effective way for characterizing 3D plant architecture,which will become useful for plant breeders to enhance selection *** trained networks are available at https://***/jieyi-one/EFF-3DPSEG.

关键词： Deep Plant weakly

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Camalote Grass (Paspalum fasciculatum Willd) as a Sustainable Raw Material for the Production of Lignocellulosic Ethanol

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Journal of Sustainable Bioenergy Systems 2024年第2期14卷 23-33页

作者： María Luz May-Reyes Cintya valerio-Cárdenas Gloria Ivette Bolio-López Manuel Mateo Hernández-villegas Miguel Ángel Velázquez-Carmona Patricia De la Cruz-Burelo Research and Postgraduate Coordination Universidad Popular de la Chontalpa Crdenas Tabasco Mexico

The current trend of replacing a percentage of gasoline with ethanol has promoted the development of new processes for its production from lignocellulosic biomass. This work reports the production of ethanol from the Camalote grass (Paspalum fasciculatum Willd). The lignocellulosic biomass was subjected to acid hydrolysis at 125C and 15 psi with H2SO4 concentrations at 5%, 10%, and 20%, obtaining an average of reducing sugars (pentoses and hexoses) from the hydrolyzed juice with 12.3%, 10%, and 17% Brix, respectively. The sugars were fermented using yeast of the Saccharomyces cerevisiae at 30C for 48 hours. Finally, the ethanol was distilled at 78C, and the average yields were obtained through analysis of variance with a 95% confidence level. The values indicate that there is a significant difference (p > 0.05), the Tukey study shows that all the % v/v averages are different from each other. For H2SO4 concentration at 5% (10.33 ± 2), H2SO4 at 10% (9.33 ± 1.8), and H2SO4 at 20% (6.33 ± 2). The acidity analysis for the ethanol obtained from each treatment gave a value of 1.8 mg/L of acetic acid in all cases.

关键词： Lignocellulosic Ethanol Lignocellulosic Biomass Camalote Grass Acid Hydrolysis Energy Crops

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Camalote Grass (Paspalum fasciculatum Willd) as a Sustainable Raw Material for the Production of Lignocellulosic Ethanol

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Journal of Sustainable Bioenergy Systems（可持续生物质能源系统） 2024年第2期 23页

作者： María Luz May-Reyes Cintya valerio-Cárdenas Gloria Ivette Bolio-López Manuel Mateo Hernández-villegas Miguel Ángel Velázquez-Carmona Patricia De la Cruz-Burelo

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