大豆在成熟期时种植密度预测数据

大豆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。合理预测大豆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大豆生长状况与种植密度之间的预测关系。通过调查采集大豆在分蘖期的相关数据，并使用多元线性回归模型预测大豆种植密度，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大豆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大豆种植密度预测值。在模型训练过程中，算法会利用最终在成熟期测得的大豆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。

Soybean planting density at maturity directly affects crop growth conditions, pest and disease incidence, and final yield. Accurately predicting soybean planting density at maturity and enabling timely adjustment of planting density during the tillering stage is of great significance for improving per-unit area yield, optimizing resource utilization, and reducing pest and disease damage. This model effectively models the predictive relationship between soybean growth status and planting density. Relevant data of soybeans during the tillering stage are collected through surveys, and a multiple linear regression model is used to predict soybean planting density. The input variables of this model are, in order: disease resistance score, incidence rate (%), leaf color index (SPAD), plant height (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 soybean planting density. The model is constructed using a deep learning framework, and calculates the predicted soybean planting density from the input data through techniques such as ordinary least squares. During model training, the algorithm optimizes by utilizing the actual soybean planting density values finally measured at maturity, adjusting the aforementioned weight coefficients to minimize prediction error. Thus, each of the above weight coefficients will be dynamically adjusted after maturity by the algorithm based on the comparison between actual and predicted values.

搜集汇总

数据集介绍

特点

该数据集包含4213条大豆在成熟期的种植密度预测数据，每月更新。数据通过多元线性回归模型预测种植密度，输入量包括抗病评分、发病率、叶片颜色指数等，用于优化大豆种植密度，提高产量和减少病虫害。