Xiaoyun Huang, Shengxi Chen, Tianling Fu, Chengwu Fan, Hongxing Chen, Song Zhang, Hui Chen, Song Qin, Zhenran Gao
{"title":"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。","authors":"Xiaoyun Huang, Shengxi Chen, Tianling Fu, Chengwu Fan, Hongxing Chen, Song Zhang, Hui Chen, Song Qin, Zhenran Gao","doi":"10.1016/j.ecoenv.2024.117548","DOIUrl":null,"url":null,"abstract":"<p><p>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 (R<sup>2</sup>) 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.</p>","PeriodicalId":303,"journal":{"name":"Ecotoxicology and Environmental Safety","volume":"290 ","pages":"117548"},"PeriodicalIF":6.2000,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancing the estimation of cadmium content in rice leaves by integrating vegetation indices and color indices using machine learning.\",\"authors\":\"Xiaoyun Huang, Shengxi Chen, Tianling Fu, Chengwu Fan, Hongxing Chen, Song Zhang, Hui Chen, Song Qin, Zhenran Gao\",\"doi\":\"10.1016/j.ecoenv.2024.117548\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>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 (R<sup>2</sup>) 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.</p>\",\"PeriodicalId\":303,\"journal\":{\"name\":\"Ecotoxicology and Environmental Safety\",\"volume\":\"290 \",\"pages\":\"117548\"},\"PeriodicalIF\":6.2000,\"publicationDate\":\"2024-12-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ecotoxicology and Environmental Safety\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://doi.org/10.1016/j.ecoenv.2024.117548\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENVIRONMENTAL SCIENCES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ecotoxicology and Environmental Safety","FirstCategoryId":"93","ListUrlMain":"https://doi.org/10.1016/j.ecoenv.2024.117548","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
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.
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.