山核桃树高预测数据

在现代精准农业管理中，对山核桃树高的预测不仅有助于评估树木健康状况，还可以指导修剪、管理和收获作业。利用数据分析技术预测树高可以提升农业生产的效率和经济效益。该模型解决了山核桃树的状况以及树高之间的建模问题。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，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树高情况，帮助制定防治策略。

In modern precision agricultural management, predicting the height of pecan trees not only aids in assessing tree health but also guides pruning, management and harvesting operations. Using data analysis technologies to predict tree height can improve the efficiency and economic benefits of agricultural production. This model addresses the modeling problem between the status of pecan trees and their height. Data of pecan trees were collected via physical and chemical experiments and field surveys. First, data preprocessing was conducted, including data cleaning and feature selection, followed by data standardization. The support vector machine (SVM) model was fed with crown width, breast-height diameter and spectral NDVI values, then optimized by adjusting parameters such as the regularization coefficient C and kernel function parameters. Cross-validation was employed to ensure the generalization ability of the model. Finally, the model was used to predict the tree height of new data to assist in formulating prevention and control strategies.

搜集汇总

数据集介绍

特点

该数据集名为‘山核桃树高预测数据’，属于农、林、牧、渔业，由杭州帅程科技有限公司提供，包含1030条数据，每年更新一次。数据集通过支持向量机模型预测山核桃树高，应用于精准农业管理，帮助评估树木健康状况和提升生产效率。