水稻生育期预测数据

水稻的生育期长度直接影响到整个生产周期和作物的产量。准确预测水稻的生育期可以帮助农户合理规划种植时间、管理措施及收获时机，从而优化资源使用，提高作物产量和质量。该模型解决了水稻生长状况与生育期之间的关系。通过理化实验以及调查获取水稻的数据,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。通过输入种植密度(株/亩),发病率(%),叶片颜色指数(SPAD),株高(cm),穗长(cm),病虫害类型,抗病评分,分蘖数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生育期，帮助制定防治策略。

The growth duration of rice directly affects the entire production cycle and crop yield. Accurate prediction of rice growth duration can help farmers rationally plan planting schedules, management measures and harvest timing, thereby optimizing resource utilization and improving crop yield and quality. This model addresses the relationship between rice growth status and growth duration. Rice data was collected through physical and chemical experiments and surveys. Data preprocessing was first performed, including data cleaning and feature selection, followed by data standardization. Various features including planting density (plants per mu), disease incidence rate (%), SPAD value (leaf color index), plant height (cm), panicle length (cm), pest and disease types, disease resistance score, and tiller number were input into the Support Vector Machine (SVM) model. The model was optimized by adjusting parameters such as the regularization coefficient C and kernel function parameters, and cross-validation was used to ensure the model's generalization capability. Ultimately, the model was used to predict the growth duration of new data, assisting in formulating prevention and control strategies.

搜集汇总

数据集介绍

特点

水稻生育期预测数据是一个由杭州帅程科技有限公司提供的企业数据集，包含702条记录，每年更新。数据集涵盖了水稻生长的多个关键指标，如种植密度、病虫害类型、发病率等，用于支持向量机模型预测水稻生育期，帮助农户优化种植管理。