小麦在生长期时植株高度预测数据

可以用于小麦植株高度预测，输入为土壤类型、肥料使用、灌溉方式、小麦茎粗(cm)、叶面积指数、根系长度(cm)、小麦产量（亩产量）、根系主要分布范围(cm)、小麦根系数量、根茎长(cm)、叶绿素含量（mg/g）、叶片数量。输出为植株预测高度。该模型帮助解决了小麦植株预测高度和小麦状况的关系建模的问题。预测小麦植株高度对小麦的生长有着重要的影响，通过预测数据保证其健康生长，保证小麦的生长和品质，提高其生产效益。通过调查采集小麦数据，并使用传统算法和多元线性回归算法预测小麦植株高度。该模型的输入为土壤类型、肥料使用、灌溉方式、小麦茎粗(cm)、叶面积指数、根系长度(cm)、小麦产量（亩产量）、根系主要分布范围(cm)、小麦根系数量、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小麦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，算法会利用小麦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小麦植株预测高度，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，准确预测小麦植株高度，有95%以上的概率预测植与实际值相差在1.9%以内。

This dataset is designed for wheat plant height prediction. Its input features cover soil type, fertilizer application, irrigation method, wheat stem diameter (cm), leaf area index (LAI), root length (cm), wheat yield (yield per mu), main distribution range of roots (cm), number of wheat roots, rhizome length (cm), chlorophyll content (mg/g), and number of leaves, with the output being the predicted wheat plant height.

This model solves the problem of modeling the relationship between predicted wheat plant height and wheat growth status. Predicting wheat plant height plays a critical role in wheat cultivation: by leveraging the predicted data, we can ensure healthy wheat growth, optimize grain yield and quality, and improve production efficiency.

Wheat data were collected via field surveys, and traditional algorithms and multiple linear regression (MLR) were employed to predict wheat plant height. The input features of the model are consistent with the aforementioned list. The multiple linear regression algorithm analyzes the linear correlation between these input variables and the predicted wheat plant height, and determines the weight coefficient for each individual variable. During model training, the algorithm utilizes the actual measured wheat plant height to optimize and adjust the weight coefficients, so as to minimize the prediction error. The model calculates the predicted wheat plant height based on the input data through techniques such as the least squares method to generate the final prediction result.

Through this process, the model comprehensively considers multiple input variables to achieve accurate wheat plant height prediction. The prediction results have a probability of over 95% that the deviation between the predicted and actual values is within 1.9%.

搜集汇总

数据集介绍

特点

该数据集包含4077条记录，每月更新，主要用于通过多元线性回归算法预测小麦植株高度，涉及多个生长参数如土壤类型、肥料使用等，旨在优化小麦的生长和产量。