山药在成熟期时种植密度预测数据

山药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。合理预测山药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山药生长状况与种植密度之间的预测关系。通过调查采集山药在分蘖期的相关数据，并使用多元线性回归模型预测山药种植密度，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山药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山药种植密度预测值。在模型训练过程中，算法会利用最终在成熟期测得的山药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 Chinese yam at the mature stage directly affects the crop’s growth conditions, pest and disease incidence, and final yield. Accurately predicting the target planting density of Chinese yam for the mature stage and making timely adjustments during the tillering stage is of great significance for increasing yield per unit area, optimizing resource utilization, and reducing pests and diseases. This model effectively constructs the predictive relationship between the growth status of Chinese yam and its planting density. By collecting relevant phenotypic data of Chinese yam during the tillering stage via field surveys, the multiple linear regression model is employed to predict the planting density of Chinese yam. The input variables of the model, in sequential order, are: disease resistance score, disease incidence rate (%), leaf color index (SPAD), plant height (cm), type of pests and diseases, growth duration (days), and tiller number. The multiple linear regression algorithm analyzes the linear correlation between these input variables and the planting density of Chinese yam to derive the weight coefficients associated with each input variable. The model is built using a deep learning framework, and technologies such as the least squares method are utilized to calculate the predicted planting density of Chinese yam based on the input data. During the model training phase, the algorithm optimizes by utilizing the actual measured planting density values of Chinese yam obtained at the mature stage, adjusting the aforementioned weight coefficients to minimize prediction error. Accordingly, each of the aforementioned weight coefficients will be dynamically adjusted after the mature stage by comparing the actual measured values with the predicted values.

搜集汇总

数据集介绍

特点

该数据集包含山药在成熟期的种植密度预测相关数据，共4098条记录，每月更新。通过多元线性回归模型预测种植密度，旨在优化资源使用、减少病虫害并提高产量。