| Study on monitoring model for total nitrogen content in plow layer of cotton field based on field in\|situ spectroscopy |
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| DOI：10.7606/j.issn.1000-7601.2023.06.29 |
| Key Words: soil total nitrogen in\|situ spectroscopy soil total nitrogen monitoring soil texture plow layer |
| Author Name | Affiliation | | LI Gang | Agricultural College of Shihezi University
Shihezi, Xinjiang, 832000, China
Key Laboratory of Oasis Ecology Agriculture of Xinjiang Production and Construction Corps, Shihezi, Xinjiang 832000, China | | KONG Yacong | Agricultural College of Shihezi University
Shihezi, Xinjiang, 832000, China
Key Laboratory of Physiology and Germplasm Utilization of Characteristic Fruits and Vegetables of Xinjiang Production and Construction Corps, Shihezi, Xinjiang 832000, China | | DAI Yuanshuai | Agricultural College of Shihezi University
Shihezi, Xinjiang, 832000, China
Key Laboratory of Oasis Ecology Agriculture of Xinjiang Production and Construction Corps, Shihezi, Xinjiang 832000, China | | LV Xin | Agricultural College of Shihezi University
Shihezi, Xinjiang, 832000, China
Key Laboratory of Oasis Ecology Agriculture of Xinjiang Production and Construction Corps, Shihezi, Xinjiang 832000, China |
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| Abstract: |
| In response to the challenge of weak penetration of visible and near\|infrared light, which hinders the detection of total nitrogen in the plow layer soil, this study focuses on pre\|planting cotton soil in the Shihezi Karez area. It aims to obtain in\|situ spectral data of three different soil texture types and the total nitrogen content of different plow layers. The soil in\|situ spectra are preprocessed using Savitzky-Golay smoothing and maximum normalization. Four modeling methods, namely Generalized Regression Neural Network (GRNN), Random Forest Regression (RFR), Support Vector Machine Regression (SVR), and Least Squares Regression, are employed to establish and select the optimal monitoring models for cotton field soil content based on different soil texture types. The results showed that: (1) Different modeling methods exhibit varying monitoring accuracies across the plow layers, with GRNN models consistently delivering the best performance in the shallow, medium, and deep layers, achieving accuracies R2 of 0.72, 0.68, and 0.63, respectively. (2) The optimized NGRO-GRANN model outperforms the GRNN model, with R2 values increasing by 16.2%~30.2% in the shallow, medium, and deep layers. (3) The monitoring models for soil total nitrogen in different plow layers, established based on in\|situ spectral data, demonstrate R2 values greater than 0.6, indicating excellent monitoring performance and significant savings in the cumbersome steps of indoor spectral processing. This study provides a theoretical basis and technical support for the rapid acquisition of nutrient information in different plow layers of pre\|planting cotton soil using in\|situ spectroscopy, demonstrating feasibility and robustness. |
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