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Applied Sciences
Volume 15
Issue 9
10.3390/app15094943
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Prediction of Selenium-Enriched Crop Zones in Xiaoyan Town Using Fuzzy Logic and Machine Learning Approaches

by
Jiacheng Li
1,
Shuyun Xie
1,*,
Wenbing Yang
2,
Weihang Zhou
1,
Emmanuel John M. Carranza
3,
Weiji Wen
1 and
Hongtao Shi
1
1
State Key Laboratory of Geological Processes and Mineral Resources (GPMR), Faculty of Earth Sciences, China University of Geosciences, Wuhan 430074, China
2
Eighth Geological Brigade of Hubei, Xiangyang 441002, China
3
Department of Geology, University of the Free State, Bloemfontein 9301, South Africa
*
Author to whom correspondence should be addressed.
Appl. Sci. 2025, 15(9), 4943; https://doi.org/10.3390/app15094943
Submission received: 29 March 2025 / Revised: 27 April 2025 / Accepted: 27 April 2025 / Published: 29 April 2025
(This article belongs to the Special Issue Recent Advances in Geochemistry)
Versions Notes

Abstract

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 (R2 = 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 R2 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.
Keywords: selenium-enriched crop zones; fuzzy weights-of-evidence; machine learning; geospatial prediction; cultivation recommendation selenium-enriched crop zones; fuzzy weights-of-evidence; machine learning; geospatial prediction; cultivation recommendation

Share and Cite

MDPI and ACS Style

Li, J.; Xie, S.; Yang, W.; Zhou, W.; Carranza, E.J.M.; Wen, W.; Shi, H. Prediction of Selenium-Enriched Crop Zones in Xiaoyan Town Using Fuzzy Logic and Machine Learning Approaches. Appl. Sci. 2025, 15, 4943. https://doi.org/10.3390/app15094943

AMA Style

Li J, Xie S, Yang W, Zhou W, Carranza EJM, Wen W, Shi H. Prediction of Selenium-Enriched Crop Zones in Xiaoyan Town Using Fuzzy Logic and Machine Learning Approaches. Applied Sciences. 2025; 15(9):4943. https://doi.org/10.3390/app15094943

Chicago/Turabian Style

Li, Jiacheng, Shuyun Xie, Wenbing Yang, Weihang Zhou, Emmanuel John M. Carranza, Weiji Wen, and Hongtao Shi. 2025. "Prediction of Selenium-Enriched Crop Zones in Xiaoyan Town Using Fuzzy Logic and Machine Learning Approaches" Applied Sciences 15, no. 9: 4943. https://doi.org/10.3390/app15094943

APA Style

Li, J., Xie, S., Yang, W., Zhou, W., Carranza, E. J. M., Wen, W., & Shi, H. (2025). Prediction of Selenium-Enriched Crop Zones in Xiaoyan Town Using Fuzzy Logic and Machine Learning Approaches. Applied Sciences, 15(9), 4943. https://doi.org/10.3390/app15094943

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