水稻在生长期时根系数量预测数据

可以用于水稻根系数量预测，输入为土壤类型、肥料使用、灌溉方式、植株高度(cm)、水稻茎粗(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)、根茎长(cm)、叶绿素含量（mg/g）、叶片数量。输出为水稻根系数量预测值。该模型帮助解决了水稻根系数量和水稻状况的关系建模的问题。水稻植株的根系数量对水稻根的生长有着重要的影响，根系的健康状况（包括有根系数量）还能影响其对养分的吸收效率，从而影响作物的最终产量。因此，通过预测水稻根系的数量，可以初步判断作物的生长状况‌。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根系数量。该模型的输入为土壤类型、肥料使用、灌溉方式、植株高度(cm)，水稻茎粗(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）。多元线性回归算法通过分析这些输入变量与水稻根系预测数量之间的线性关系，确定每个变量的权重系数，使用深度学习框架构建模型F=ω1 * U1 + ω2* U2+…ω13 * U13，其中，ω1至ω13分别是土壤类型、肥料使用、灌溉方式、植株高度、水稻茎粗、叶面积指数、根系长度、水稻产量、根系主要分布范围、根茎长、叶绿素含量、叶片数量的权重系数，同样的 U1至 U13分别是上述13个输入量的参数值，F是水稻根系数量预测值。在模型训练过程中，算法会利用水稻根系数量实际值进行优化，调整权重系数以最小化预测误差，因此上述权重系数（ω1至ω13）是会动态变化。模型通过最小二乘法等技术，根据输入的数据计算水稻根系数量预测值，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，准确预测水稻根系数量。

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

This model addresses the problem of modeling the relationship between rice root count and rice growth status. Rice root count exerts a significant impact on rice root growth, and the health status of rice roots (including root count) also affects nutrient absorption efficiency, thereby influencing the final crop yield. Therefore, predicting rice root count allows for preliminary assessment of crop growth conditions.

Rice data was collected via field surveys, and traditional algorithms and multiple linear regression were used to predict rice root count. The model's inputs are: soil type, fertilizer application, irrigation method, plant height (cm), rice stem diameter (cm), leaf area index, root length (cm), rice yield per mu, main root distribution range (cm), actual rice root count, 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 root count, and determines the weight coefficient for each variable. The model is constructed using a deep learning framework as: $F = omega_1 imes U_1 + omega_2 imes U_2 + dots + omega_{13} imes U_{13}$, where $omega_1$ to $omega_{13}$ are the weight coefficients corresponding to soil type, fertilizer application, irrigation method, plant height, rice stem diameter, leaf area index, root length, rice yield per mu, main root distribution range, rhizome length, chlorophyll content, and number of leaves respectively; $U_1$ to $U_{13}$ are the parameter values of the aforementioned 13 input quantities, and $F$ is the predicted rice root count.

During model training, the algorithm uses the actual values of rice root count for optimization, adjusting the weight coefficients to minimize prediction error, meaning the weight coefficients ($omega_1$ to $omega_{13}$) are dynamically changeable. The model calculates the predicted rice root count based on input data using techniques such as the method of least squares to obtain the final result. Through this process, the model comprehensively considers multiple input variables to achieve accurate prediction of rice root count.

搜集汇总

数据集介绍

特点

该数据集包含4118条水稻生长期根系数量预测数据，每月更新，涵盖多个生长相关变量，通过多元线性回归算法预测根系数量，用于评估水稻生长状况和养分吸收效率。