利用机器学习整合植被指数和颜色指数,加强对水稻叶片中镉含量的估算。

IF 6.2 2区 环境科学与生态学 Q1 ENVIRONMENTAL SCIENCES
Ecotoxicology and Environmental Safety Pub Date : 2025-01-15 Epub Date: 2024-12-16 DOI:10.1016/j.ecoenv.2024.117548
Xiaoyun Huang, Shengxi Chen, Tianling Fu, Chengwu Fan, Hongxing Chen, Song Zhang, Hui Chen, Song Qin, Zhenran Gao
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引用次数: 0

摘要

镉(Cd)是一种具有显著生物毒性的重金属。过量的镉含量会对作物生长、发育和产量产生有害影响。叶片中镉含量的实时、快速、无损监测对粮食安全至关重要。以往的研究主要是利用传统的统计方法和与重金属相关的植被指数(VIs)来建立估算LCd的模型,往往缺乏通用性。在水培和土壤栽培条件下,采集了6种镉浓度梯度下不同镉含量的叶片样品252组。通过集成VIs、颜色指数(ci)和机器学习(ML)算法,开发了LCd估计模型。结果表明,VIs和CIs与LCd呈强相关,相关系数(r)分别为0.73和0.57。综合这两个指标的机器学习估计模型比传统统计方法建立的单参数模型更有效。值得注意的是,使用随机森林方法建立的LCd估计模型具有最高的准确性,其决定系数(R2)为0.81,均方根误差为0.120。这些结果表明,基于ML算法的多源索引数据可以有效地估计LCd。本研究提出了一种准确、可靠、通用的估算LCd的方法,为利用无人机遥感技术评估水稻大尺度重金属污染状况提供了有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing the estimation of cadmium content in rice leaves by integrating vegetation indices and color indices using machine learning.

Cadmium (Cd) is a heavy metal recognized for its notable biotoxicity. Excessive Cd levels can have detrimental effects on crop growth, development, and yield. Real-time, rapid, and nondestructive monitoring of Cd content in leaves (LCd) is essential for food security. Previous research has primarily utilized traditional statistical methods and heavy metal-related vegetation indices (VIs) to develop models for estimating LCd, often resulting in a lack of generalizability. Herein, 252 sets of leaf samples with varying Cd contents were collected under six Cd concentration gradients in hydroponic and soil cultivation conditions. An LCd estimation model was developed by integrating VIs, color indices (CIs), and machine learning (ML) algorithms. Results indicate that VIs and CIs were strongly correlated with LCd, exhibiting correlation coefficients (r) of 0.73 and 0.57, respectively. The ML estimation model, which integrated both indices, was more effective than the single-parameter model developed using traditional statistical methods. Notably, the LCd estimation model developed using the random forest method exhibited the highest accuracy, with a coefficient of determination (R2) of 0.81 and a root-mean-square error of 0.120. These results indicate that multisource index data based on ML algorithms can effectively estimate LCd. This study presents an accurate, reliable, and generalized method to estimate LCd, providing valuable insights for assessing the large-scale heavy metal pollution status of rice using unmanned aerial vehicle remote sensing technology.

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来源期刊
CiteScore
12.10
自引率
5.90%
发文量
1234
审稿时长
88 days
期刊介绍: Ecotoxicology and Environmental Safety is a multi-disciplinary journal that focuses on understanding the exposure and effects of environmental contamination on organisms including human health. The scope of the journal covers three main themes. The topics within these themes, indicated below, include (but are not limited to) the following: Ecotoxicology、Environmental Chemistry、Environmental Safety etc.
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