花芸豆在成熟期时种植密度预测数据

花芸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。合理预测花芸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花芸豆生长状况与种植密度之间的预测关系。通过调查采集花芸豆数据，并使用传统算法和多元线性回归算法预测花芸豆发病率。该模型的输入为种植密度、叶片颜色指数(SPAD)、株高(cm)、穗长(cm)、生育期(天)、分蘖数。多元线性回归算法通过分析这些输入变量与花芸豆发病率之间的线性关系，确定每个输入量相关的权重系数分别为w1、w2、w3、w4、w5和w6，根据输入的数据计算花芸豆发病率预测值,花芸豆发病率预测值=种植密度*w1+叶片颜色指数*w2+株高*w3+穗长*w4+生育期*w5+分蘖数*w6，从而得出最终结果。通过这样的过程，模型能够将多个输入变量综合考虑，准确预测花芸豆发病率，提高农民的收入和粮食生产能力。

The planting density of pinto beans at maturity directly affects the crop’s growing conditions, pest and disease incidence, and final yield. Accurately predicting the planting density of pinto beans at maturity and timely adjusting the density during the tillering stage is of great significance for increasing per-unit area yield, optimizing resource utilization, and reducing pests and diseases. This model effectively addresses the predictive relationship between the growth status of pinto beans and their planting density. By collecting pinto bean data through field surveys and using traditional algorithms and multiple linear regression (MLR) to predict the disease incidence of pinto beans, the model takes the following variables as inputs: planting density, soil and plant analyzer development (SPAD) value (leaf color index), plant height (cm), ear length (cm), growth period (days), and tiller number. The multiple linear regression algorithm analyzes the linear relationship between these input variables and the disease incidence of pinto beans, and determines the weight coefficients corresponding to each input as w1, w2, w3, w4, w5 and w6. The predicted disease incidence value of pinto beans is calculated as: Predicted Disease Incidence = Planting Density × w1 + SPAD Value × w2 + Plant Height × w3 + Ear Length × w4 + Growth Period × w5 + Tiller Number × w6, which yields the final prediction result. Through this process, the model comprehensively considers multiple input variables to accurately predict the disease incidence of pinto beans, thereby improving farmers' income and food production capacity.

搜集汇总

数据集介绍

特点

该数据集包含4494条花芸豆在成熟期的种植密度预测数据，每月更新，用于优化种植密度以提高产量和减少病虫害。数据通过多元线性回归算法预测花芸豆发病率，考虑了种植密度、叶片颜色指数等多个变量。