喇叭花在生长期时根系数量预测数据

可以用于喇叭花根系数量预测，输入为土壤类型、肥料使用、灌溉方式、植株高度(cm)、喇叭花茎粗(cm)、叶面积指数、根系长度(cm)、喇叭花产量（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。输出为喇叭花根系数量预测值。该模型帮助解决了喇叭花根系数量和喇叭花状况的关系建模的问题。喇叭花植株的根系数量对喇叭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，通过预测喇叭花根系的数量，可以初步判断作物的生长状况‌。通过调查采集喇叭花数据，并使用传统算法和多元线性回归算法预测喇叭花根系数量。模型输入为土壤类型、肥料使用、灌溉方式、植株高度(cm)，喇叭花茎粗(cm)，叶面积指数，根系长度(cm)，喇叭花产量（亩产量），根系主要分布范围(cm)，喇叭花根系数量，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喇叭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、喇叭花茎粗、叶面积指数、根系长度、喇叭花产量、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是喇叭花根系数量预测值。在模型训练过程中，算法会利用喇叭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喇叭花根系数量预测值，从而得出最终结果。

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

This model addresses the problem of modeling the relationship between morning glory root count and plant status. The root count of morning glory plants exerts a significant impact on root growth, and the health status of roots (including root count) also affects their nutrient absorption efficiency, thereby influencing the final crop yield. Therefore, predicting the morning glory root count allows preliminary judgment of crop growth status.

Data of morning glories were collected via field surveys, and traditional algorithms and multiple linear regression algorithms were employed to predict morning glory root count. The model's input features are: soil type, fertilizer application, irrigation method, plant height (cm), morning glory stem diameter (cm), leaf area index, root length (cm), morning glory yield (per mu yield), main root distribution range (cm), morning glory 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 morning glory root count to determine the weight coefficient of each variable. The model is constructed using a deep learning framework as follows:
F = ω₁ * U₁ + ω₂ * U₂ + … + ω₁₃ * U₁₃
where ω₁ to ω₁₃ are the weight coefficients corresponding to soil type, fertilizer application, irrigation method, plant height, morning glory stem diameter, leaf area index, root length, morning glory yield, main root distribution range, rhizome length, chlorophyll content, and number of leaves respectively; correspondingly, U₁ to U₁₃ represent the parameter values of the aforementioned 13 input quantities, and F is the predicted morning glory root count.

During model training, the algorithm utilizes the actual values of morning glory root count for optimization, adjusting the weight coefficients to minimize prediction errors, thus the weight coefficients (ω₁ to ω₁₃) are dynamically changeable. The model calculates the predicted morning glory root count based on input data using techniques such as the least squares method to generate the final result.