Soil Quality Evaluation for Cotton Fields in Arid Region Based on Graph Convolution Network

Xianglong Fan, Pan Gao, Li Zuo, Long Duan, Hao Cang, Mengli Zhang, Qiang Zhang, Ze Zhang, Xin Lv () and Lifu Zhang ()
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Xianglong Fan: Key Laboratory of Oasis Eco-Agriculture, Xinjiang Production and Construction Corps, Agricultural College, Shihezi University, Shihezi 832003, China
Pan Gao: College of Information Science and Technology, Shihezi University, Shihezi 832003, China
Li Zuo: Key Laboratory of Oasis Eco-Agriculture, Xinjiang Production and Construction Corps, Agricultural College, Shihezi University, Shihezi 832003, China
Long Duan: College of Information Science and Technology, Shihezi University, Shihezi 832003, China
Hao Cang: College of Information Science and Technology, Shihezi University, Shihezi 832003, China
Mengli Zhang: College of Information Science and Technology, Shihezi University, Shihezi 832003, China
Qiang Zhang: Key Laboratory of Oasis Eco-Agriculture, Xinjiang Production and Construction Corps, Agricultural College, Shihezi University, Shihezi 832003, China
Ze Zhang: Key Laboratory of Oasis Eco-Agriculture, Xinjiang Production and Construction Corps, Agricultural College, Shihezi University, Shihezi 832003, China
Xin Lv: Key Laboratory of Oasis Eco-Agriculture, Xinjiang Production and Construction Corps, Agricultural College, Shihezi University, Shihezi 832003, China
Lifu Zhang: Key Laboratory of Oasis Eco-Agriculture, Xinjiang Production and Construction Corps, Agricultural College, Shihezi University, Shihezi 832003, China

Land, 2023, vol. 12, issue 10, 1-18

Abstract: Accurate soil quality evaluation is an important prerequisite for improving soil management systems and remediating soil pollution. However, traditional soil quality evaluation methods are cumbersome to calculate, and suffer from low efficiency and low accuracy, which often lead to large deviations in the evaluation results. This study aims to provide a new and accurate soil quality evaluation method based on graph convolution network (GCN). In this study, soil organic matter (SOM), alkaline hydrolysable nitrogen (AN), available potassium (AK), salinity, and heavy metals (iron (Fe), copper (Cu), manganese (Mn), and zinc (Zn)) were determined and evaluated using the soil quality index (SQI). Then, the graph convolution network (GCN) was first introduced in the soil quality evaluation to construct an evaluation model, and its evaluation results were compared with those of the SQI. Finally, the spatial distribution of the evaluation results of the GCN model was displayed. The results showed that soil salinity had the largest coefficient of variation (86%), followed by soil heavy metals (67%) and nutrients (30.3%). The soil salinization and heavy metal pollution were at a low level in this area, and the soil nutrients and soil quality were at a high level. The evaluation accuracy of the GCN model for soil salinity/heavy metals, soil nutrients, and soil quality were 0.91, 0.84, and 0.90, respectively. Therefore, the GCN model has a high accuracy and is feasible to be applied in the soil quality evaluation. This study provides a new, simple, and highly accurate method for soil quality evaluation.

Keywords: soil quality assessment; machine learning; soil nutrients; heavy metals; soil salinity (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
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