棉花在生长期时植株高度预测数据

可以用于棉花植株高度预测，输入为土壤类型、肥料使用、灌溉方式、棉花茎粗(cm)、叶面积指数、根系长度(cm)、棉花产量（亩产量）、根系主要分布范围(cm)、棉花根系数量、根茎长(cm)、叶绿素含量（mg/g）、叶片数量。输出为植株预测高度。该模型帮助解决了棉花植株预测高度和棉花状况的关系建模的问题。预测棉花植株高度对棉花的生长有着重要的影响，通过预测数据保证其健康生长，保证棉花的生长和品质，提高其生产效益。通过调查采集棉花数据，并使用传统算法和多元线性回归算法预测棉花植株高度。该模型的输入为土壤类型、肥料使用、灌溉方式、棉花茎粗(cm)、叶面积指数、根系长度(cm)、棉花产量（亩产量）、根系主要分布范围(cm)、棉花根系数量、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棉花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，算法会利用棉花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棉花植株预测高度，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，准确预测棉花植株高度，有95%以上的概率预测植与实际值相差在1.2%以内。

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

This model addresses the critical challenge of modeling the relationship between predicted cotton plant height and cotton growth conditions. Accurate prediction of cotton plant height plays a vital role in cotton cultivation: by leveraging predictive data, growers can ensure healthy crop development, improve cotton yield and quality, and boost overall production efficiency.

Cotton-related phenotypic and environmental data were collected through field surveys, and both traditional algorithms and multiple linear regression were employed to predict cotton plant height.

The model's input features are consistent with the aforementioned list: soil type, fertilizer application, irrigation method, cotton stem diameter (cm), leaf area index (LAI), root length (cm), cotton yield (yield per mu), main root distribution range (cm), number of cotton roots, rhizome length (cm), chlorophyll content (mg/g), and number of leaves.

The multiple linear regression algorithm analyzes the linear correlation between these input variables and the target predicted cotton plant height, and calculates the weight coefficient for each input feature. During model training, the algorithm optimizes by utilizing the actual measured heights of cotton plants, adjusting the weight coefficients to minimize prediction errors. The model computes the predicted cotton plant height based on the input data using techniques such as the ordinary least squares (OLS) method to generate the final prediction result.

Through this process, the model comprehensively integrates multiple input variables to achieve accurate cotton plant height prediction, with over 95% probability that the deviation between the predicted value and the actual measured value is within 1.2%.

搜集汇总

数据集介绍

特点

该数据集包含4089条记录，用于预测棉花植株高度，通过多元线性回归算法实现，预测准确率高，误差小。数据集每月更新，适用于棉花生长状况建模和高度预测。