木薯在成熟期时种植密度预测数据

木薯在成熟期的种植密度直接影响作物的生长条件、病虫害发生率以及最终产量。合理预测木薯在成熟期时种植密度，从而能够在分蘖期适时调整种植密度对于提高单位面积产量、优化资源使用及减少病虫害具有重要意义。该模型有效的解决了木薯生长状况与种植密度之间的预测关系。通过调查采集木薯在分蘖期的相关数据，并使用多元线性回归模型预测木薯种植密度，该模型的输入量依次为抗病评分、发病率(%)、叶片颜色指数(SPAD)、株高(cm)、病虫害类型、生育期(天)、分蘖数，多元线性回归算法通过分析这些输入量与木薯种植密度之间的线性关系，确定每个输入量相关的权重系数，使用深度学习框架构建模，模型通过最小二乘法等技术，根据输入的数据从而计算出木薯种植密度预测值。在模型训练过程中，算法会利用最终在成熟期测得的木薯种植密度实际值进行优化，调整上述的权重系数以最小化预测误差，因此上述每个权重系数在成熟期后，算法会根据实际值与预测值进行比较后再进行动态调整的。

Cassava planting density at the mature stage directly affects crop growth conditions, pest and disease incidence, and final yield. Reasonably predicting cassava planting density at the mature stage to timely adjust planting density at the tillering stage is of great significance for increasing yield per unit area, optimizing resource utilization, and reducing pest and disease occurrence. This model effectively addresses the task of predicting the correlation between cassava growth status and planting density. Relevant data of cassava at the tillering stage are collected through field surveys, and a multiple linear regression model is employed to predict cassava planting density at the mature stage. The input variables of the model are, in order: disease resistance score, incidence rate (%), SPAD value of leaf color index, plant height (cm), pest and disease type, growth duration (days), and tiller number. The multiple linear regression algorithm analyzes the linear relationship between these input variables and cassava planting density, determines the weight coefficients corresponding to each input variable, and constructs the model using a deep learning framework. The model calculates the predicted value of cassava planting density from the input data via techniques such as the least squares method. During the model training process, the algorithm uses the actual measured values of cassava planting density at the mature stage for optimization, adjusting the aforementioned weight coefficients to minimize prediction error. Therefore, after the crop reaches maturity, the algorithm will dynamically adjust each of the above weight coefficients by comparing the actual and predicted values.

搜集汇总

数据集介绍

特点

该数据集包含木薯在成熟期的种植密度预测相关数据，规模为4056条，每月更新。通过多元线性回归模型预测种植密度，旨在优化木薯生长条件和提高产量。