马铃薯在生长期时茎粗值预测数据

可以用于马铃薯茎粗预测，输入为土壤类型、肥料使用、灌溉方式、植株高度(cm)、叶面积指数、根系长度(cm)、马铃薯产量（亩产量）、根系主要分布范围(cm)、马铃薯根系数量、根茎长(cm)、叶绿素含量（mg/g）、叶片数量。输出为马铃薯茎粗预测。该模型帮助解决了马铃薯茎粗和马铃薯状况的关系建模的问题。马铃薯茎粗值对马铃薯根的生长有着重要的影响，通过预测马铃薯茎粗值，可有效、合理的种植马铃薯，保证马铃薯的生长和品质，提高其生产效益。通过调查采集马铃薯数据，并使用传统算法和多元线性回归算法预测马铃薯叶片数量。该模型的输入为土壤类型、肥料使用、灌溉方式、植株高度(cm)、叶面积指数、根系长度(cm)、马铃薯产量（亩产量）、根系主要分布范围(cm)、马铃薯根系数量、根茎长(cm)、叶绿素含量（mg/g）、叶片数量。多元线性回归算法通过分析这些输入变量与马铃薯茎粗预测值之间的线性关系，确定每个变量的权重系数。在模型训练过程中，算法会利用马铃薯茎粗实际值进行优化，调整权重系数以最小化预测误差。模型通过最小二乘法等技术，根据输入的数据计算马铃薯茎粗，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，准确预测马铃薯茎粗值，保证马铃薯的生长和品质，提高其生产效益。

This dataset is designed for potato stem diameter prediction. The input variables are soil type, fertilizer application, irrigation method, plant height (cm), leaf area index, root length (cm), potato yield per mu, main root distribution range (cm), number of potato roots, rhizome length (cm), chlorophyll content (mg/g), and number of leaves. The output is the predicted potato stem diameter. This model addresses the problem of modeling the relationship between potato stem diameter and potato growth status. Potato stem diameter has a significant impact on root growth; predicting stem diameter enables rational and effective potato cultivation, ensures crop growth and quality, and enhances production efficiency. Potato data was collected through surveys, and traditional algorithms and multiple linear regression were used to predict the number of potato leaves. The input of this model consists of the 12 aforementioned variables. The multiple linear regression algorithm analyzes the linear relationship between these input variables and the predicted potato stem diameter values to determine the weight coefficient of each variable. During model training, the algorithm optimizes by utilizing the actual potato stem diameter values, adjusting the weight coefficients to minimize prediction errors. The model computes potato stem diameter based on input data using techniques such as the least squares method to obtain the final prediction result. Through this process, the model comprehensively considers multiple input variables to accurately predict potato stem diameter, ensuring potato growth and quality and improving production efficiency.