水稻分蘖数预测数据

水稻的分蘖数是影响其产量的重要因素之一，因为分蘖数与最终成熟稻穗的数量直接相关。预测水稻分蘖数可以帮助农户更精确地管理种植策略，优化田间管理，从而提高产量和作物质量。该模型解决了水稻生长状况与分蘖数之间的关系。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,生育期(天),抗病评分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分蘖数，帮助制定防治策略。

The tiller number of rice is one of the critical factors affecting grain yield, as it is directly correlated with the number of mature panicles at harvest. Predicting rice tiller number can help farmers precisely adjust planting strategies and optimize field management, thereby improving both crop yield and quality. This model is developed to elucidate the relationship between rice growth status and tiller number. Rice data is collected through physicochemical experiments and field surveys. First, data preprocessing is performed, including data cleaning and feature selection, followed by data standardization. The Support Vector Machine (SVM) model takes input features such as planting density (plants/mu), disease incidence (%), leaf color index (SPAD), plant height (cm), panicle length (cm), pest and disease type, growth period (days), and disease resistance score. The model is optimized by tuning parameters including the regularization coefficient C and kernel function parameters, with cross-validation employed to ensure its generalization ability. Finally, the trained model is used to predict the tiller number of new data samples, assisting in formulating targeted prevention and control strategies.

搜集汇总

数据集介绍

特点

该数据集由杭州帅程科技有限公司提供，包含702条水稻种植相关数据，用于预测水稻分蘖数。数据涵盖种植密度、病虫害类型、发病率、叶片颜色指数、株高、穗长、生育期和抗病评分等多个特征，通过支持向量机模型进行预测，旨在帮助农户优化田间管理，提高产量和作物质量。