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Aggregate shape is classified based on the relative sharpness or roundness of the edges and corners. This classification includes categories like rounded, angular, elongated, and flaky, each with specific characteristics. Rounded aggregates, fully shaped by attrition, are typical of river or seashore gravel, while angular aggregates, such as crushed rock, have well-defined edges. Aggregates that are elongated and flaky are less desirable, as they can reduce the workability and strength of...
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相关实验视频

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Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
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根据正统的插值和机器学习技术对土壤质地分类的评估.

Lei Feng1, Umer Khalil2, Bilal Aslam3

  • 1Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing, China; College of Environment and Ecology, Chongqing University, Chongqing, China.

Environmental research
|December 30, 2023
PubMed
概括

人工神经网络 (ANN) 在预测巴基斯坦土壤结构方面表现优于其他方法. 虽然ANNs显示出更高的精度,但与土壤质感的相关性仍然低于50%.

关键词:
人工神经网络的人工神经网络插值方法 插值方法在 MATLAB 中,我们可以使用 MATLAB.精确度 精确度 精确度 精确度土壤质地 土壤质地质地

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科学领域:

  • 土壤科学 土壤科学
  • 地质统计学 在地质统计学
  • 机器学习 机器学习

背景情况:

  • 土壤质地对于农业规划和作物产量至关重要.
  • 准确地绘制土壤质感地图对于有效的土地管理至关重要.

研究的目的:

  • 评估人工神经网络 (ANN),战争,联合战争和反向距离权重 (IDW) 的有效性,以预测土壤质地.
  • 为了比较这些插值技术在巴基斯坦拉瓦尔宾迪地区的精度.

主要方法:

  • 采集了44个土壤样本 (深度10-15厘米),并使用水表法分析了质地.
  • 使用ArcGIS进行空间绘图和使用MATLAB进行土壤纹理评估.
  • 使用相关系数 (R),GMER和RMSE等统计指标来评估预测的准确性.

主要成果:

  • 人工神经网络 (ANN) 在评估粘土,泥土和沙子的颗粒大小和空间分布方面表现出卓越的有效性和精度,与战争,联合战争和IDW相比.
  • 反向距离权重 (IDW) 在测试方法中显示出较低的精度.
  • 年度数字实现了不到50%的相关性,这表明有改进的余地.

结论:

  • ANNs为土壤质地预测提供了一种有前途的方法,其性能优于传统的地理统计方法.
  • 尽管存在相关性的局限性,但测试方法为未来的农业研究和规划提供了可接受的结果.
  • 准确的土壤质地图对于优化作物产量和牧场安排至关重要.