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紫花苜蓿幼苗生物量无损估算 被引量:1

Nondestructive estimation for biomass of alfalfa seedlings
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摘要 植物表型组学是一门新兴的学科,可实现高效、无损、连续获取植物表型特征.以6个国内外常见紫花苜蓿品种为供试材料,使用Lab Scananlyzer表型分析仪拍摄供试材料出苗后第30、40、50天图像,测定其相应生长阶段的株高、叶鲜重、叶干重、茎鲜重、茎干重等表型指标,并建立总鲜重和总干重的估算模型.结果表明,相对最小外接圆直径与紫花苜蓿株高呈现显著的正相关关系,相对绿色面积与叶鲜重、茎鲜重、总鲜重、叶干重、茎干重和总干重之间均呈现极显著的正相关关系;相对绿色面积估算总鲜重的方程为y=4.4826x+0.6128(R^(2)=0.78),估算总干重的方程为y=1.2184x+0.069(R^(2)=0.75). Plant phenomics is a new branch of science developed to efficiently,nondestructively and continuously acquire plant phenotype characteristics without damage.Six alfalfa cultivars were used as the experimental materials in this study.The images of plant were taken using the Lab Scananlyzeron 30,40 and 50 days after sprout.In the meantime,some phenotypic parameters were measured,including the plant height,fresh weight of stem and leaves,dry weight of stem and leaves.Models were built for estimating total fresh weight and total dry weight using the indice extracted from the images.Results showed that significant correlational relationships were found between the relative minimum enclosing circle diameter and the plant height of the experimental plant on 30,40 and 50 days after sprout,respectively.The relative green area was significantly correlated to the fresh leaves weight,the fresh stem weight,the total fresh weight,the dry leaves weight,the dry stem weight and the total dry weight,respectively.The model for estimating the total fresh weight and the total dry weight was,y=4.4826 x+0.6128(R^(2)=0.78)and y=1.2184 x+0.069(R^(2)=0.75),respectively.
作者 汪辉 田浩琦 向上 雷俊锋 周青平 WANG Hui;TIAN Hao-qi;XIANG Shang;LEI Jun-feng;ZHOU Qing-ping(Institute of Qinghai Tibetan Plateau,Southwest Minzu University,Chengdu 610041,China)
出处 《西南民族大学学报(自然科学版)》 CAS 2021年第2期117-123,共7页 Journal of Southwest Minzu University(Natural Science Edition)
基金 四川省科技计划项目资助(2020ZHFP0055) 国家牧草产业技术体系青藏高原牧草育种岗位(CARS34)。
关键词 紫花苜蓿 无损估算 生物量 表型 相对绿色面积 alfalfa nondestructive estimation biomass phenotype relative green area
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  • 1陈维君,周启发,黄敬峰.用高光谱植被指数估算水稻乳熟后叶片和穗的色素含量[J].中国水稻科学,2006,20(4):434-439. 被引量:16
  • 2Li J, Rao X,Ying Y. Detection of common defects on orangesusing hyperspectral reflectance imaging [J]. Compute Electronics inAgriculture,2011, 78: 38-48.
  • 3Mahlein A K,Steiner U, Hillnhutter C, et al. Hyperspectral imaging for small-scale analysis of symptoms caused by different sugarbeet diseases [J]. Plant Methods,2012,8: 3-15.
  • 4Huang W, Lamb D W, Niu Z- et al. Identification of yellow rust in wheat using in-situ spectral reflectance measurements and air-borne hyperspectral imaging [J]. Precision Agriculture, 2007,8: 187-197.
  • 5Bock C H, Poole G H, Parker P E, et al. Plant disease severity estimated visually, by digital photography and image analysis, andby hyperspectral imaging [J]. Critical Reviews in Plant Sciences, 2010, 29: 59-107.
  • 6Gowen A A, 0;Donnell C P,Cullen P J, ei al. Hyperspectral imaging-an emerging process analytical tool for food quality andsafety control [J]. Trends in Food Science Technology, 2007,18: 590-598.
  • 7Nansen C- Zhao G,Dakin N, et al. Using hyperspectral imaging to determine germination of native Australian plant seeds [J].Journal of Photochemistry and Photobiology B: Biology, 2015, 1451 19-24.
  • 8ElMasry G, Wang N* Vigneault C. Detecting chilling injury in Red Delicious apple using hyperspectral imaging and neural networks[J]. Postharvest Biology and Technology, 2009, 52: 1 -8.
  • 9Li L, Zhang Q,Huang D. A Review of imaging techniques for pant phenotyping [J] Sensors, 2014, 14: 20078-20111.
  • 10A wad Y M, Abdullah A A, Bayoumi T Y- et al. Early Detection of Powdery Mildew Disease in Wheat (.Triticum aestivum L.) U-sing Thermal Imaging Technique [C]. The Institute of Electrical and Electronics Engineer, 2014: 755-765.

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