棉花在生长期时根系数量预测数据

可以用于棉花根系数量预测，输入为土壤类型、肥料使用、灌溉方式、植株高度(cm)、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)、根茎长(cm)、叶绿素含量（mg/g）、叶片数量。输出为棉花根系数量预测值。该模型帮助解决了棉花根系数量和棉花状况的关系建模的问题。棉花植株的根系数量对棉花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，通过预测棉花根系的数量，可以初步判断作物的生长状况‌。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)，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）。多元线性回归算法通过分析这些输入变量与棉花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、棉花茎粗、叶面积指数、根系长度、棉花产量、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是棉花根系数量预测值。在模型训练过程中，算法会利用棉花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棉花根系数量预测值，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，准确预测棉花根系数量。

This dataset can be used for cotton root quantity prediction. The inputs include soil type, fertilizer application, irrigation method, plant height (cm), cotton stem diameter (cm), leaf area index, root length (cm), cotton yield (mu yield), main root distribution range (cm), rhizome length (cm), chlorophyll content (mg/g), and number of leaves, with the output being the predicted value of cotton root quantity.

This model addresses the problem of modeling the relationship between cotton root quantity and cotton growth status. The root quantity of cotton plants has a significant impact on root growth, and the health status of roots (including root quantity) also affects their nutrient absorption efficiency, thereby influencing the final crop yield. Therefore, predicting cotton root quantity enables preliminary assessment of crop growth status.

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

The multiple linear regression algorithm determines the weight coefficient of each variable by analyzing the linear relationship between these input variables and the predicted cotton root quantity. The model is constructed using a deep learning framework in the form of $F = omega_1 cdot U_1 + omega_2 cdot U_2 + dots + omega_{13} cdot U_{13}$, where $omega_1$ to $omega_{13}$ are the weight coefficients corresponding to soil type, fertilizer application, irrigation method, plant height, cotton stem diameter, leaf area index, root length, cotton yield, main root distribution range, cotton root quantity, rhizome length, chlorophyll content, and number of leaves respectively; similarly, $U_1$ to $U_{13}$ are the parameter values of the aforementioned 13 input quantities, and $F$ is the predicted value of cotton root quantity.

During model training, the algorithm utilizes the actual values of cotton root quantity for optimization, adjusting the weight coefficients to minimize prediction errors, so the weight coefficients ($omega_1$ to $omega_{13}$) are dynamically variable. The model calculates the predicted cotton root quantity based on input data using techniques such as the least squares method to derive the final result. Through this process, the model comprehensively considers multiple input variables to accurately predict cotton root quantity.