甘蔗在生长期时植株高度预测数据

可以用于甘蔗植株高度预测，输入为土壤类型、肥料使用、灌溉方式、甘蔗茎粗(cm)、叶面积指数、根系长度(cm)、甘蔗产量（亩产量）、根系主要分布范围(cm)、甘蔗根系数量、根茎长(cm)、叶绿素含量（mg/g）、叶片数量。输出为植株预测高度。该模型帮助解决了甘蔗植株预测高度和甘蔗状况的关系建模的问题。预测甘蔗植株高度对甘蔗的生长有着重要的影响，通过预测数据保证其健康生长，保证甘蔗的生长和品质，提高其生产效益。通过调查采集甘蔗数据，并使用传统算法和多元线性回归算法预测甘蔗植株高度。该模型的输入为土壤类型、肥料使用、灌溉方式、甘蔗茎粗(cm)、叶面积指数、根系长度(cm)、甘蔗产量（亩产量）、根系主要分布范围(cm)、甘蔗根系数量、根茎长(cm)、叶绿素含量（mg/g）、叶片数量。多元线性回归算法通过分析这些输入变量与甘蔗植株预测高度之间的线性关系，确定每个变量的权重系数。在模型训练过程中，算法会利用甘蔗植株实际高度进行优化，调整权重系数以最小化预测误差。模型通过最小二乘法等技术，根据输入的数据计算甘蔗植株预测高度，从而得出最终结果。通过这样的过程，模型能够将多个输入变量综合考虑，准确预测甘蔗植株高度，有95%以上的概率预测植与实际值相差在1.3%以内。

This dataset is applicable to sugarcane plant height prediction. Its input features include soil type, fertilizer application regime, irrigation method, sugarcane stem diameter (cm), leaf area index, root length (cm), sugarcane yield (per mu yield), main root distribution range (cm), number of sugarcane roots, rhizome length (cm), chlorophyll content (mg/g), and number of leaves. The output is the predicted sugarcane plant height.

This model addresses the challenge of modeling the correlation between predicted sugarcane plant height and sugarcane growth status. Predicting sugarcane plant height plays a critical role in sugarcane growth: it helps ensure healthy growth, improves crop yield and quality, and enhances production benefits.

Sugarcane-related data were collected via field surveys, and traditional algorithms and multiple linear regression were adopted to predict sugarcane plant height. The multiple linear regression algorithm analyzes the linear relationship between these input features and the target predicted plant height, and derives the weight coefficients for each input variable. During model training, the algorithm leverages the actual height of sugarcane plants for optimization, adjusting the weight coefficients to minimize prediction errors. The model calculates the predicted sugarcane plant height using techniques such as the least squares method based on the input data, generating the final prediction result.

Through this workflow, the model comprehensively incorporates multiple input variables to achieve accurate prediction of sugarcane plant height, with over 95% probability that the prediction error is less than 1.3% compared to the actual value.

搜集汇总

数据集介绍

特点

该数据集包含甘蔗生长期的多种生长参数，用于预测植株高度，应用多元线性回归算法，预测准确率高达95%以上，误差在1.3%以内。数据集规模为4066条，每月更新，适用于农业研究和生产优化。