甘蔗在生长期时根系数量预测数据

可以用于甘蔗根系数量预测，输入为土壤类型、肥料使用、灌溉方式、植株高度(cm)、甘蔗茎粗(cm)、叶面积指数、根系长度(cm)、甘蔗产量（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。输出为甘蔗根系数量预测值。该模型帮助解决了甘蔗根系数量和甘蔗状况的关系建模的问题。甘蔗植株的根系数量对甘蔗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，通过预测甘蔗根系的数量，可以初步判断作物的生长状况‌。通过调查采集甘蔗数据，并使用传统算法和多元线性回归算法预测甘蔗根系数量。该模型的输入为土壤类型、肥料使用、灌溉方式、植株高度(cm)，甘蔗茎粗(cm)，叶面积指数，根系长度(cm)，甘蔗产量（亩产量），根系主要分布范围(cm)，甘蔗根系数量，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甘蔗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、甘蔗茎粗、叶面积指数、根系长度、甘蔗产量、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是甘蔗根系数量预测值。在模型训练过程中，算法会利用甘蔗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甘蔗根系数量预测值，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，准确预测甘蔗根系数量。This dataset can be used for sugarcane root count prediction. Its inference input features include soil type, fertilizer application, irrigation method, plant height (cm), sugarcane stem diameter (cm), Leaf Area Index (LAI), root length (cm), sugarcane yield (yield per mu), main distribution range of the root system (cm), rhizome length (cm), chlorophyll content (mg/g), and number of leaves, with the output being the predicted sugarcane root count. This model addresses the challenge of modeling the relationship between sugarcane root count and sugarcane growth status. The root count of sugarcane plants exerts a critical influence on root growth, while the health status of the root system (including root count) also affects nutrient uptake efficiency, thereby impacting the final crop yield. Accordingly, predicting sugarcane root count allows for preliminary assessment of crop growth conditions. Sugarcane data was collected through field surveys, and traditional algorithms and multiple linear regression (MLR) were employed to predict sugarcane root count. The model's training inputs include soil type, fertilizer application, irrigation method, plant height (cm), sugarcane stem diameter (cm), Leaf Area Index (LAI), root length (cm), sugarcane yield (yield per mu), main distribution range of the root system, actual sugarcane root count, 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 sugarcane root count. A deep learning framework is used to construct the model with the formula: $F = omega_1 U_1 + omega_2 U_2 + dots + omega_{13} U_{13}$, where $omega_1$ to $omega_{13}$ are the weight coefficients corresponding to soil type, fertilizer application, irrigation method, plant height, sugarcane stem diameter, Leaf Area Index (LAI), root length, sugarcane yield, main distribution range of the root system, actual sugarcane root count, rhizome length, chlorophyll content, and number of leaves respectively, while $U_1$ to $U_{13}$ are the parameter values of the aforementioned 13 input quantities, and $F$ is the predicted sugarcane root count. During model training, the algorithm uses the actual values of sugarcane root count for optimization, adjusting the weight coefficients to minimize prediction errors, so the aforementioned weight coefficients ($omega_1$ to $omega_{13}$) are dynamically updated. The model calculates the predicted sugarcane root count based on input data using techniques such as the least squares method to obtain the final result. Through this process, the model can comprehensively consider multiple input variables and accurately predict sugarcane root count.

搜集汇总

数据集介绍

特点

甘蔗在生长期时根系数量预测数据是一个农业数据集，包含4066条记录，每月更新，用于通过多元线性回归算法预测甘蔗根系数量，以评估作物生长状况。