小麦在成熟期时种植密度预测数据

小麦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。合理预测小麦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小麦生长状况与种植密度之间的预测关系。通过调查采集小麦在分蘖期的相关数据，并使用多元线性回归模型预测小麦种植密度，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小麦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小麦种植密度预测值。在模型训练过程中，算法会利用最终在成熟期测得的小麦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会进行动态调整的。

The planting density of wheat at maturity directly impacts the crop’s growth conditions, pest and disease incidence, and final yield. Reasonably predicting the planting density of wheat at maturity to allow timely adjustment of planting density during the tillering stage holds great significance for improving yield per unit area, optimizing resource use efficiency, and reducing pest and disease occurrences. This model effectively models the predictive relationship between wheat growth status and planting density. Relevant data on wheat during the tillering stage is collected via field surveys, and a multiple linear regression model is employed to predict wheat planting density. The input variables of this model are listed in sequence: disease resistance score, incidence rate (%), leaf color index (SPAD), plant height (cm), pest and disease type, growth duration (days), and tiller number. The multiple linear regression algorithm determines the weight coefficients corresponding to each input variable by analyzing the linear correlation between these inputs and wheat planting density. The model is constructed using a deep learning framework, and calculates the predicted wheat planting density based on the input data through techniques such as the least squares method. During model training, the algorithm optimizes by leveraging the actual measured values of wheat planting density obtained at maturity, adjusting the aforementioned weight coefficients to minimize prediction errors. Therefore, each of these weight coefficients will be dynamically adjusted by the algorithm post-maturity based on a comparison between the actual and predicted values.

搜集汇总

数据集介绍

特点

该数据集包含4320条记录，用于预测小麦在成熟期的种植密度，涉及多个生长指标如抗病评分、发病率、株高等，通过多元线性回归模型进行预测，旨在优化种植密度以提高产量和减少病虫害。