水稻在开花期时种植密度预测数据

水稻在开花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。合理预测水稻在开花期时种植密度，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水稻生长状况与种植密度之间的预测关系。通过调查采集水稻在分蘖期的相关数据，并使用多元线性回归模型预测水稻种植密度，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)、穗长(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水稻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水稻种植密度预测值。在模型训练过程中，算法会利用最终在开花期测得的水稻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开花期后，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。

Planting density of rice during the flowering stage directly affects crop growth conditions, pest and disease incidence, and final yield. Reasonably predicting the planting density at the flowering stage and making timely adjustments to the planting density during the tillering stage is of great significance for improving yield per unit area, optimizing resource utilization, and reducing pests and diseases. This model effectively addresses the predictive relationship between rice growth status and planting density. Relevant data of rice during the tillering stage are collected through surveys, and a multiple linear regression model is employed to predict rice planting density. The input variables of the model are, in order: disease resistance score, incidence rate (%), leaf color index (SPAD), plant height (cm), panicle length (cm), pest and disease type, growth period (days), and tiller number. The multiple linear regression algorithm determines the weight coefficients corresponding to each input variable by analyzing the linear relationship between these inputs and rice planting density. The model is constructed using a deep learning framework, and calculates the predicted rice planting density from the input data through techniques such as the least squares method. During model training, the algorithm optimizes by utilizing the actual measured values of rice planting density obtained at the final flowering stage, adjusting the aforementioned weight coefficients to minimize prediction errors. Therefore, each of the above weight coefficients will be dynamically adjusted by the algorithm after the flowering stage based on the comparison between actual and predicted values.

搜集汇总

数据集介绍

特点

该数据集由杭州旭卉科技有限责任公司登记，包含4320条记录，每月更新，用于预测水稻在开花期的种植密度。数据集包含12个字段，如样本编号、种植时间、抗病评分等，通过多元线性回归模型预测种植密度，并应用于优化水稻产量和减少病虫害。