水稻在生长期时茎粗值预测数据

可以用于水稻茎粗预测，输入为土壤类型、肥料使用、灌溉方式、植株高度(cm)、叶面积指数、根系长度(cm)、水稻产量（亩产量）、根系主要分布范围(cm)、水稻根系数量、根茎长(cm)、叶绿素含量（mg/g）、叶片数量。输出为水稻茎粗预测。该模型帮助解决了水稻茎粗和水稻状况的关系建模的问题。水稻茎粗值对水稻根的生长有着重要的影响，通过预测水稻茎粗值，可有效、合理的种植水稻，保证水稻的生长和品质，提高其生产效益。通过调查采集水稻数据，并使用传统算法和多元线性回归算法预测水稻叶片数量。该模型的输入为土壤类型、肥料使用、灌溉方式、植株高度(cm)、叶面积指数、根系长度(cm)、水稻产量（亩产量）、根系主要分布范围(cm)、水稻根系数量、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水稻茎粗预测值之间的线性关系，确定每个变量的权重系数。在模型训练过程中，算法会利用水稻茎粗实际值进行优化，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水稻茎粗，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，准确预测水稻茎粗值，保证水稻的生长和品质，提高其生产效益。

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

This model addresses the problem of modeling the relationship between rice stem diameter and rice growth status. Rice stem diameter exerts a significant impact on rice root growth. Predicting rice stem diameter enables effective and rational rice cultivation, ensuring crop growth and quality, and improving production efficiency.

Rice data was collected through field surveys, and traditional algorithms and multiple linear regression (MLR) were used to predict the number of rice leaves. The input features of this model are consistent with the aforementioned ones: soil type, fertilizer application, irrigation method, plant height (cm), leaf area index (LAI), root length (cm), rice yield (per mu), main distribution range of roots (cm), number of rice roots, rhizome length (cm), chlorophyll content (mg/g), and number of leaves.

The multiple linear regression algorithm analyzes the linear relationship between these input variables and the predicted rice stem diameter values, and determines the weight coefficient for each variable. During model training, the algorithm utilizes the actual rice stem diameter values for optimization, adjusting the weight coefficients to minimize prediction error. The model calculates rice stem diameter based on the input data using techniques such as the least squares method, thereby obtaining the final prediction result. Through this process, the model comprehensively considers multiple input variables to accurately predict rice stem diameter values, ensuring rice growth and quality, and enhancing production efficiency.

搜集汇总

数据集介绍

特点

该数据集由杭州灵煜生物科技有限公司提供，包含4118条水稻生长数据，每月更新。数据涵盖土壤类型、肥料使用、灌溉方式等多种生长参数，用于通过多元线性回归算法预测水稻茎粗值，以提高水稻种植效益。