The rapid and accurate measurement of soil salinity is essential for assessing the level of soil salinization. In natural conditions, the surface of cohesive soda saline-alkali soil experiences shrinkage and cracking phenomena during water evaporation. The propagation and development of these desiccation cracks can be effectively described using texture features due to their statistical randomness. This study aims to establish a relationship between electrical conductivity (EC) values and texture features derived from both gray-level co-occurrence matrix (GLCM) and wavelet decomposition, and to develop a prediction model for EC accordingly. To achieve these objectives, crack images on the surface of 200 soil samples with varying levels of salinity were obtained in the Songnen Plain field. GLCM was computed and wavelet decomposition was performed to extract texture features in different directions and scales. The results demonstrate a significant correlation between texture features and soil EC values. Among the various texture features, 12 GLCM texture features with a grayscale level of 2 and a step size of 1 pixel, and wavelet texture features derived from a 4th level orthogonal decomposition based on the coiflet-1 function were selected as the optimal parameters for establishing and comparing the predictive effects of two machine learning models on soil EC values. In comparison to the testing results of the BP neural network model (R=0.83, RPD=1.64, RMSE=0.32 dS/m, MAE=0.18 dS/m), the random forest model exhibited higher accuracy and stability (R=0.95, RPD=3.48, RMSE=0.21 dS/m, MAE=0.07 dS/m), indicating that although both machine learning models demonstrated rapid and nondestructive capabilities, the random forest method was better suited for EC prediction in soda saline-alkali soil due to its superior accuracy.

中文翻译：

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基于地面观测高分辨率图像纹理分析和机器学习方法的裂化苏打盐碱土EC预测研究


快速、准确地测量土壤盐分对于评估土壤盐渍化水平至关重要。在自然条件下，粘性苏打盐碱土表面因水分蒸发而出现收缩、开裂现象。由于其统计随机性，这些干燥裂纹的传播和发展可以使用纹理特征有效地描述。本研究旨在建立电导率（EC）值与灰度共生矩阵（GLCM）和小波分解得出的纹理特征之间的关系，并相应地开发 EC 预测模型。为了实现这些目标，在松嫩平原田间获取了 200 个不同盐度的土壤样品表面的裂纹图像。计算GLCM并进行小波分解以提取不同方向和尺度的纹理特征。结果表明质地特征与土壤 EC 值之间存在显着相关性。在各种纹理特征中，选取灰度级为2、步长为1像素的12个GLCM纹理特征以及基于coiflet-1函数的四级正交分解得到的小波纹理特征作为建立的最佳参数。并比较两种机器学习模型对土壤EC值的预测效果。与BP神经网络模型的测试结果（R=0.83，RPD=1.64，RMSE=0.32 dS/m，MAE=0.18 dS/m）相比，随机森林模型表现出更高的准确性和稳定性（R=0.95， RPD=3.48，RMSE=0.21dS/m，MAE=0。07 dS/m），这表明虽然两种机器学习模型都表现出快速和无损的能力，但随机森林方法由于其优越的精度更适合苏打盐碱土的EC预测。