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首页> 外文期刊>Journal of environmental biology >Growth estimation during hardening phase of tissue cultured banana plantlets using bootstrapped artificial neural network
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Growth estimation during hardening phase of tissue cultured banana plantlets using bootstrapped artificial neural network

机译:采用自动映射人工神经网络的组织培养的香蕉植株硬化阶段生长估计

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摘要

Aim : The study aims to develop an advanced non-destructive method to estimate the plant growth rate of tissue culture propagated banana plantlets during primary hardening phase inside the greenhouse using Bootstrapped Artificial Neural Network (BANN).Methodology : Both non-destructive growth parameters like plant height, girth, number of leaves, leaf length and leaf breadth, and destructive growth parameters like number of roots, longest root length, fresh and dry weight were measured periodically on selected plants of one week to nine week old which were kept in greenhouse at ICAR-National Research Centre for banana. In addition to plant growth parameters, greenhouse temperature, radiation and carbon dioxide concentration were also recorded daily. The experimental data obtained using destructive measurements were recorded on a small sample of size n, and hence re-sampling for bootstrap involves n repeated trials of simple random sampling with replacement. These sets of bootstrap samples were finally used as input to develop neural model using a novel methodology of bootstrap re-sampling based artificial neural network (ANN) for studying the progress of plant ontogeny.Results : The growth estimation analysis of plants in terms of its leaf area and biomass production was performed without physically handling the test plants using bootstrap ANN. The notion of prediction performance is validated through statistical indices namely Nash and Sutcliffe efficiency coefficient, root means square error and mean absolute error. The approximate estimates of mean relative growth and net assimilation rate of plants were 0.036 and 0.027, and the corresponding variance were 1.5 x 10(-6) and 2.12 x 10(-6), respectively.[GRAPHICS]Interpretation : Based on the non-destructive plant growth observations, the measures to increase the overall plant growth can be significantly predicted well in advance. This projected plant growth statistics at an early stage of hardening serves as an essential component in planning and evaluation of investments on protected structure to improve the productivity and profitability of banana tissue culture industry.
机译:目的:该研究旨在开发一种先进的非破坏性方法来估计组织培养的植物生长速率在使用自动映射的人工神经网络(禁区)内部初级硬化相位繁殖的香蕉植物。方法:既有非破坏性生长参数植物高度,周长,叶片数量,叶子长度和叶片宽度,以及像根部数量,最长的根长,新鲜和干重的破坏性生长参数定期测量,在一周至九周大的植物上定期测量,这些植物保持温室在icar-国家的香蕉研究中心。除植物生长参数外,每天还记录温室温度,辐射和二氧化碳浓度。使用破坏性测量获得的实验数据记录在一个大小的小样本上,因此重复对自动采样涉及使用更换简单随机采样的n重复试验。这些自举样品集目的是使用基于自动启动重新采样的新型人工神经网络(ANN)的新颖方法来开发神经模型,用于研究植物Ontogeny的进展。结果:植物的生长估算分析在不使用引导ANN的情况下进行叶面积和生物质生产,而不会物理处理测试厂。通过统计指标验证预测性能的概念即NASH和SUTCLIFFE效率系数,根部意味着方误差和平均绝对误差。植物平均相对生长和净同化率的近似估计为0.036和0.027,相应的方差分别为1.5×10(-6)和2.12×10(-6)。[图形]解释:基于非 - 破坏植物生长观测,提高整体植物增长的措施可以提前预测。这种预计的工厂增长统计数据在硬化的早期阶段是规划和评估保护结构投资的重要组成部分,以提高香蕉组织文化行业的生产率和盈利能力。

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