山核桃土壤pH预测数据

在农业生产中，土壤pH值是影响作物生长的关键因素之一，特别是对于山核桃这种对土壤要求较高的树种。预测土壤pH可以帮助农户调整土壤条件，优化施肥策略，从而提高山核桃的生长质量和产量。该模型解决了山核桃树的状况以及土壤pH之间的建模问题。通过理化实验以及调查获取山核桃的数据,首先进行数据预处理，包括数据清洗和特征选择，然后对数据进行标准化。通过输入树高,冠幅,胸径,光谱NDVI值到支持向量机模型中, 通过调整参数如正则化系数C和核函数参数来优化模型，在支持向量机中，正则化系数C控制模型的复杂度与训练误差的平衡，而核函数参数调节数据在高维空间中的映射，以优化回归边界, 使用交叉验证确保模型的泛化能力。最终，模型被用来预测新数据的土壤pH情况，帮助制定防治策略。

In agricultural production, soil pH is one of the critical factors affecting crop growth, particularly for Chinese hickory, a tree species with high soil requirements. Predicting soil pH can help farmers adjust soil conditions and optimize fertilization strategies, thereby enhancing the growth quality and yield of Chinese hickory. This model addresses the modeling problem between the status of Chinese hickory trees and soil pH. Data of Chinese hickory were collected via physical and chemical experiments and field surveys. First, data preprocessing was performed, including data cleaning and feature selection, followed by data standardization. The model was optimized by feeding tree height, crown width, diameter at breast height (DBH), and spectral NDVI values into the Support Vector Machine (SVM) model, with adjustments to key parameters including the regularization coefficient C and kernel function parameters. In SVM, the regularization coefficient C governs the trade-off between model complexity and training error, while the kernel function parameters adjust the data mapping in the high-dimensional space to optimize the regression boundary. Cross-validation was employed to validate the model’s generalization capability. Finally, the model is used to predict soil pH for new datasets, assisting in formulating prevention and control strategies.

搜集汇总

数据集介绍

特点

山核桃土壤pH预测数据集包含1030条记录，每年更新，通过支持向量机模型预测土壤pH值，帮助优化山核桃的种植条件。