山核桃病虫害预测数据

山核桃作为一种经济价值较高的坚果作物，在中国南方地区尤为普遍。随着种植面积的不断扩大，病虫害的防控变得尤为重要。利用现代信息技术和人工智能对山核桃病虫害进行预测和管理，不仅可以提高预防效率，还能降低化学防治的使用，从而保护生态环境和提高山核桃的品质。该模型解决了山核桃树的状况以及病虫害之间的建模问题。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,将病虫害情况进行处理,若发生病虫害则为1,没有则为0,通过输入树高,冠幅,胸径,土壤pH值,土壤含水量,氮含量,磷含量,钾含量,有机质含量,光谱NDVI值,产量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病虫害情况，帮助制定防治策略。

Pecan, a nut crop with high economic value, is particularly prevalent in southern China. With the continuous expansion of planting area, pest and disease control has become particularly critical. Utilizing modern information technology and artificial intelligence for the prediction and management of pecan pests and diseases can not only improve prevention efficiency but also reduce the application of chemical control, thereby protecting the ecological environment and enhancing pecan quality. This model solves the problem of modeling the correlation between the growth status of pecan trees and the occurrence of pests and diseases. Data of pecans are collected through physical and chemical experiments and field surveys. Firstly, data preprocessing is performed, including data cleaning and feature selection, followed by data standardization. The pest and disease status is labeled as 1 if an infestation occurs, and 0 otherwise. The input features include tree height, crown width, breast-height diameter, soil pH value, soil moisture content, nitrogen content, phosphorus content, potassium content, organic matter content, spectral NDVI value, and yield, which are fed into the Support Vector Machine (SVM) model. The model is optimized by adjusting parameters such as the regularization coefficient C and kernel function parameters, and cross-validation is adopted to ensure the generalization ability of the model. Finally, the model is used to predict the pest and disease status of new data, assisting in formulating targeted control strategies.