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...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Ecotoxicology and Environmental Safety |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0147651324016245 |
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| author | Xiaoyun Huang Shengxi Chen Tianling Fu Chengwu Fan Hongxing Chen Song Zhang Hui Chen Song Qin Zhenran Gao |
| author_facet | Xiaoyun Huang Shengxi Chen Tianling Fu Chengwu Fan Hongxing Chen Song Zhang Hui Chen Song Qin Zhenran Gao |
| author_sort | Xiaoyun Huang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f7a41b2ebc904639b72e49697a3480a3 |
| institution | Kabale University |
| issn | 0147-6513 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ecotoxicology and Environmental Safety |
| spelling | doaj-art-f7a41b2ebc904639b72e49697a3480a32025-02-12T05:29:39ZengElsevierEcotoxicology and Environmental Safety0147-65132025-01-01290117548Enhancing the estimation of cadmium content in rice leaves by integrating vegetation indices and color indices using machine learningXiaoyun Huang0Shengxi Chen1Tianling Fu2Chengwu Fan3Hongxing Chen4Song Zhang5Hui Chen6Song Qin7Zhenran Gao8National Key Laboratory of Green Pesticide, State Key Laboratory Breeding Base of Green Pesticide and Agricultural Bioengineering, Key Laboratory of Green Pesticide and Agricultural Bioengineering, Ministry of Education, Center for Research and Development of Fine Chemicals, Guizhou University, Guiyang 550025, China; Institute of New Rural Revitalization, Guizhou University, Guiyang 550025, ChinaInstitute of New Rural Revitalization, Guizhou University, Guiyang 550025, ChinaInstitute of New Rural Revitalization, Guizhou University, Guiyang 550025, ChinaGuizhou Institute of Soil and Fertilizer, Guizhou Academy of Agricutural Science, Guiyang 550025, ChinaInstitute of New Rural Revitalization, Guizhou University, Guiyang 550025, ChinaInstitute of New Rural Revitalization, Guizhou University, Guiyang 550025, ChinaInstitute of New Rural Revitalization, Guizhou University, Guiyang 550025, ChinaGuizhou Institute of Soil and Fertilizer, Guizhou Academy of Agricutural Science, Guiyang 550025, ChinaInstitute of New Rural Revitalization, Guizhou University, Guiyang 550025, China; Correspondence to: Institute of New Rural Revitalization, Guizhou University, Guiyang, Guizhou province 550025, China.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.http://www.sciencedirect.com/science/article/pii/S0147651324016245RiceCadmium contentMachine learningVegetation index |
| spellingShingle | Xiaoyun Huang Shengxi Chen Tianling Fu Chengwu Fan Hongxing Chen Song Zhang Hui Chen Song Qin Zhenran Gao Enhancing the estimation of cadmium content in rice leaves by integrating vegetation indices and color indices using machine learning Ecotoxicology and Environmental Safety Rice Cadmium content Machine learning Vegetation index |
| title | Enhancing the estimation of cadmium content in rice leaves by integrating vegetation indices and color indices using machine learning |
| title_full | Enhancing the estimation of cadmium content in rice leaves by integrating vegetation indices and color indices using machine learning |
| title_fullStr | Enhancing the estimation of cadmium content in rice leaves by integrating vegetation indices and color indices using machine learning |
| title_full_unstemmed | Enhancing the estimation of cadmium content in rice leaves by integrating vegetation indices and color indices using machine learning |
| title_short | Enhancing the estimation of cadmium content in rice leaves by integrating vegetation indices and color indices using machine learning |
| title_sort | enhancing the estimation of cadmium content in rice leaves by integrating vegetation indices and color indices using machine learning |
| topic | Rice Cadmium content Machine learning Vegetation index |
| url | http://www.sciencedirect.com/science/article/pii/S0147651324016245 |
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