辣椒在成熟期时种植密度预测数据

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

The planting density of chili peppers at the mature stage directly impacts crop growth conditions, the incidence of pests and diseases, and final yield. Reasonably predicting the planting density of chili peppers at the mature stage, and thereby timely adjusting the planting density during the tillering stage, holds significant importance for increasing yield per unit area, optimizing resource utilization, and reducing pests and diseases. This model effectively resolves the predictive relationship between the growth status of chili peppers and their planting density. Relevant data of chili peppers during the tillering stage are collected and investigated, and a multiple linear regression model is employed to predict the planting density of chili peppers. The input variables of this model are, in sequence: disease resistance score, incidence rate (%), SPAD leaf color index, plant height (cm), types of pests and diseases, growth period (days), and number of tillers. The multiple linear regression algorithm analyzes the linear correlation between these input variables and the planting density of chili peppers to determine the weight coefficients associated with each input. The model is constructed using a deep learning framework, and techniques such as the least squares method are applied to calculate the predicted value of chili pepper planting density based on the input data. During the model training phase, the algorithm utilizes the actual measured values of chili pepper planting density at the mature stage for optimization, adjusting the aforementioned weight coefficients to minimize prediction errors. Thus, after the mature stage, each of the above weight coefficients will be dynamically adjusted following a comparison between the actual values and the predicted values.

搜集汇总

数据集介绍

特点

该数据集包含3990条辣椒在成熟期的种植密度预测数据，每月更新，用于通过多元线性回归模型预测种植密度，优化产量和减少病虫害。数据结构包含样本编号、种植时间、抗病评分等11个字段。