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| Prediction and safe utilization zoning of zinc content of wheat kernels based on deep learning |
| Received:February 02, 2024 |
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| KeyWord:deep learning;multi-layer perception neural networks（MLPNN）;random forest （RF）;wheat;zinc;safe utilization |
| Author Name | Affiliation | E-mail | | LI Qingcai | Lunan Geo-engineering Exploration Institute of Shandong Province(Shandong Provincial Bureau of Geology and Mineral Resources No. 2 Geology Group), Jining 272100, China
Technology Innovation Center of Integrated Management and Ecological Restoration for Mining Subsidence Area, Ministry of Natural Resources, Jining 272100, China
College of Resources and Environment, Shandong Agricultural University, Tai' an 271018, China | | | CHEN Juan | Lunan Geo-engineering Exploration Institute of Shandong Province(Shandong Provincial Bureau of Geology and Mineral Resources No. 2 Geology Group), Jining 272100, China
Technology Innovation Center of Integrated Management and Ecological Restoration for Mining Subsidence Area, Ministry of Natural Resources, Jining 272100, China | | | ZHAO Qingling | Lunan Geo-engineering Exploration Institute of Shandong Province(Shandong Provincial Bureau of Geology and Mineral Resources No. 2 Geology Group), Jining 272100, China
Technology Innovation Center of Integrated Management and Ecological Restoration for Mining Subsidence Area, Ministry of Natural Resources, Jining 272100, China
College of Resources and Environment, Shandong Agricultural University, Tai' an 271018, China | zqlzb@126.com | | CAI Tu | Jining Bureau of Natural Resources and Planning, Jining 272000, China | | | HAN Wencheng | Lunan Geo-engineering Exploration Institute of Shandong Province(Shandong Provincial Bureau of Geology and Mineral Resources No. 2 Geology Group), Jining 272100, China
Technology Innovation Center of Integrated Management and Ecological Restoration for Mining Subsidence Area, Ministry of Natural Resources, Jining 272100, China | | | CHU Linlin | Shandong Weihai Eco-Environment Monitoring Center, Weihai 264200, China | |
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| Abstract: |
| In order to achieve accurate prediction of Zn content in wheat grains and safe utilization zoning. Zn content of wheat grains and 12 physical and chemical indexes of rhizosphere soil samples including SiO2, Fe2O3, MgO, CaO, Na2O, K2O, OrgC, P, N, S, Zn, and pH were determined in the wheat planting area of southern Jining. The characteristics of Zn content in wheat grains and the physical and chemical indexes of rhizosphere soils were systematically studied. The multi-layer perception neural networks（MLPNN）and random forest（RF） models were used to predict the variation characteristics of wheat grain Zn content. The optimal model parameters were selected to predict the Zn content of wheat grains. Zn-poor, Zn-deficient, Zn-sufficient, and Zn-rich farmlands were divided by GIS technology. The results showed that the average Zn content（39.7 mg·kg-1）of wheat grains in the southern region of Jining was similar to the recommended value of Zn-rich wheat grains, which was 1.32 times higher than the average Zn content of wheat grains in the Huang-Huai wheat region. Through correlation analysis and cluster analysis, there was a complex nonlinear relationship between the interaction and coupling between Zn in wheat grain and the physical and chemical indexes of rhizosphere soil. R2（0.999）, RMSE（0.194）, and MAE（0.146）of the MLPNN prediction model were better than those of the RF model. P, pH, OrgC, and N in rhizosphere soil were relatively important characteristic variables affecting MLPNN prediction. The study area is dominated by Zn-sufficient and Zn-deficient farmlands, accounting for 57.47% and 33.97%, respectively. The proportions of Zn-poor farmland and safe use of Zn-rich area were 6.05% and 2.51%, respectively. |
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