Prediction of Selenium-Enriched Crop Zones in Xiaoyan Town Using Fuzzy Logic and Machine Learning Approaches
Selenium-rich foods play a crucial role in human health and hold significant economic value for agricultural products. However, many regions in China are experiencing selenium deficiency, which has led to an increased demand for Se-rich agricultural products. This study focused on Nanzhang County, a...
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2025-04-01
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| author | Jiacheng Li Shuyun Xie Wenbing Yang Weihang Zhou Emmanuel John M. Carranza Weiji Wen Hongtao Shi |
| author_facet | Jiacheng Li Shuyun Xie Wenbing Yang Weihang Zhou Emmanuel John M. Carranza Weiji Wen Hongtao Shi |
| author_sort | Jiacheng Li |
| collection | DOAJ |
| description | Selenium-rich foods play a crucial role in human health and hold significant economic value for agricultural products. However, many regions in China are experiencing selenium deficiency, which has led to an increased demand for Se-rich agricultural products. This study focused on Nanzhang County, a key area within the “Organic Valley” of Hubei Province, China. We employed fuzzy weights-of-evidence, backpropagation neural network, and support vector regression models to predict optimal planting zones for Selenium-rich crops. A comparative analysis indicated that the backpropagation neural network model provided the highest prediction accuracy (R<sup>2</sup> = 0.77), identifying Selenium-rich crop zones covering 68.87% of the aera, where Selenium-rich crops made up 86.67% of all samples. Notably, the backpropagation neural network yielded excellent performance for rice and rapeseed, with R<sup>2</sup> values of 0.95 and 0.99, respectively. The findings also indicate that the Selenium content in crops is closely linked to Selenium levels in the soil and is significantly influenced by synergistic and antagonistic interactions with other elements. This study provides scientific support for the cultivation of selenium-rich crops. It plays a positive role in promoting the development of the local selenium-rich industry and the sustainable utilization of soil selenium resources. |
| format | Article |
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| language | English |
| publishDate | 2025-04-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-430c71b7717146c19a234ae065dfb69b2025-08-20T01:49:14ZengMDPI AGApplied Sciences2076-34172025-04-01159494310.3390/app15094943Prediction of Selenium-Enriched Crop Zones in Xiaoyan Town Using Fuzzy Logic and Machine Learning ApproachesJiacheng Li0Shuyun Xie1Wenbing Yang2Weihang Zhou3Emmanuel John M. Carranza4Weiji Wen5Hongtao Shi6State Key Laboratory of Geological Processes and Mineral Resources (GPMR), Faculty of Earth Sciences, China University of Geosciences, Wuhan 430074, ChinaState Key Laboratory of Geological Processes and Mineral Resources (GPMR), Faculty of Earth Sciences, China University of Geosciences, Wuhan 430074, ChinaEighth Geological Brigade of Hubei, Xiangyang 441002, ChinaState Key Laboratory of Geological Processes and Mineral Resources (GPMR), Faculty of Earth Sciences, China University of Geosciences, Wuhan 430074, ChinaDepartment of Geology, University of the Free State, Bloemfontein 9301, South AfricaState Key Laboratory of Geological Processes and Mineral Resources (GPMR), Faculty of Earth Sciences, China University of Geosciences, Wuhan 430074, ChinaState Key Laboratory of Geological Processes and Mineral Resources (GPMR), Faculty of Earth Sciences, China University of Geosciences, Wuhan 430074, ChinaSelenium-rich foods play a crucial role in human health and hold significant economic value for agricultural products. However, many regions in China are experiencing selenium deficiency, which has led to an increased demand for Se-rich agricultural products. This study focused on Nanzhang County, a key area within the “Organic Valley” of Hubei Province, China. We employed fuzzy weights-of-evidence, backpropagation neural network, and support vector regression models to predict optimal planting zones for Selenium-rich crops. A comparative analysis indicated that the backpropagation neural network model provided the highest prediction accuracy (R<sup>2</sup> = 0.77), identifying Selenium-rich crop zones covering 68.87% of the aera, where Selenium-rich crops made up 86.67% of all samples. Notably, the backpropagation neural network yielded excellent performance for rice and rapeseed, with R<sup>2</sup> values of 0.95 and 0.99, respectively. The findings also indicate that the Selenium content in crops is closely linked to Selenium levels in the soil and is significantly influenced by synergistic and antagonistic interactions with other elements. This study provides scientific support for the cultivation of selenium-rich crops. It plays a positive role in promoting the development of the local selenium-rich industry and the sustainable utilization of soil selenium resources.https://www.mdpi.com/2076-3417/15/9/4943selenium-enriched crop zonesfuzzy weights-of-evidencemachine learninggeospatial predictioncultivation recommendation |
| spellingShingle | Jiacheng Li Shuyun Xie Wenbing Yang Weihang Zhou Emmanuel John M. Carranza Weiji Wen Hongtao Shi Prediction of Selenium-Enriched Crop Zones in Xiaoyan Town Using Fuzzy Logic and Machine Learning Approaches Applied Sciences selenium-enriched crop zones fuzzy weights-of-evidence machine learning geospatial prediction cultivation recommendation |
| title | Prediction of Selenium-Enriched Crop Zones in Xiaoyan Town Using Fuzzy Logic and Machine Learning Approaches |
| title_full | Prediction of Selenium-Enriched Crop Zones in Xiaoyan Town Using Fuzzy Logic and Machine Learning Approaches |
| title_fullStr | Prediction of Selenium-Enriched Crop Zones in Xiaoyan Town Using Fuzzy Logic and Machine Learning Approaches |
| title_full_unstemmed | Prediction of Selenium-Enriched Crop Zones in Xiaoyan Town Using Fuzzy Logic and Machine Learning Approaches |
| title_short | Prediction of Selenium-Enriched Crop Zones in Xiaoyan Town Using Fuzzy Logic and Machine Learning Approaches |
| title_sort | prediction of selenium enriched crop zones in xiaoyan town using fuzzy logic and machine learning approaches |
| topic | selenium-enriched crop zones fuzzy weights-of-evidence machine learning geospatial prediction cultivation recommendation |
| url | https://www.mdpi.com/2076-3417/15/9/4943 |
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