节节麦在生长期时根系数量预测数据

可以用于节节麦根系数量预测，输入为土壤类型、肥料使用、灌溉方式、植株高度(cm)、节节麦茎粗(cm)、叶面积指数、根系长度(cm)、节节麦产量（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。输出为节节麦根系数量预测值。该模型帮助解决了节节麦根系数量和节节麦状况的关系建模的问题。节节麦植株的根系数量对节节麦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，通过预测节节麦根系的数量，可以初步判断作物的生长状况‌。通过调查采集节节麦数据，并使用传统算法和多元线性回归算法预测节节麦根系数量。模型输入为土壤类型、肥料使用、灌溉方式、植株高度(cm)，节节麦茎粗(cm)，叶面积指数，根系长度(cm)，节节麦产量（亩产量），根系主要分布范围(cm)，节节麦根系数量，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节节麦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、节节麦茎粗、叶面积指数、根系长度、节节麦产量、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是节节麦根系数量预测值。在模型训练过程中，算法会利用节节麦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节节麦根系数量预测值，从而得出最终结果。

This dataset can be used for predicting the root quantity of Aegilops tauschii. The model inputs include soil type, fertilizer application, irrigation method, plant height (cm), stem diameter of Aegilops tauschii (cm), leaf area index, root length (cm), yield of Aegilops tauschii (mu yield), main root distribution range (cm), rhizome length (cm), chlorophyll content (mg/g), and leaf count. The output is the predicted root quantity of Aegilops tauschii.

This model addresses the problem of modeling the relationship between the root quantity and growth status of Aegilops tauschii. The root quantity of Aegilops tauschii plants exerts a significant impact on root growth, and the health status of the root system (including root quantity) also affects its nutrient absorption efficiency, thereby influencing the final yield of the crop. Therefore, predicting the root quantity of Aegilops tauschii can preliminarily evaluate the growth status of the crop.

Data of Aegilops tauschii were collected via field surveys, and traditional algorithms and multiple linear regression algorithms were adopted to predict the root quantity of Aegilops tauschii. The model inputs are as follows: soil type, fertilizer application, irrigation method, plant height (cm), stem diameter of Aegilops tauschii (cm), leaf area index, root length (cm), yield of Aegilops tauschii (mu yield), main root distribution range (cm), root quantity of Aegilops tauschii, rhizome length (cm), chlorophyll content (mg/g), and leaf count.

The multiple linear regression algorithm analyzes the linear relationship between these input variables and the predicted root quantity of Aegilops tauschii to determine the weight coefficient of each variable. The model is constructed using a deep learning framework as $F = omega_1 * U_1 + omega_2 * U_2 + dots + omega_{13} * U_{13}$, where $omega_1$ to $omega_{13}$ are the weight coefficients corresponding to soil type, fertilizer application, irrigation method, plant height, stem diameter of Aegilops tauschii, leaf area index, root length, yield of Aegilops tauschii, main root distribution range, rhizome length, chlorophyll content, and leaf count respectively, $U_1$ to $U_{13}$ are the parameter values of the aforementioned 13 input quantities, and $F$ is the predicted root quantity of Aegilops tauschii.

During the model training process, the algorithm utilizes the actual root quantity values of Aegilops tauschii for optimization, adjusting the weight coefficients to minimize the prediction error. Thus, the aforementioned weight coefficients ($omega_1$ to $omega_{13}$) are dynamically changeable. The model calculates the predicted root quantity of Aegilops tauschii based on the input data through techniques such as the least squares method, thereby obtaining the final result.