基于GPR模型的草地地上生物量估算软件设计

Design of grassland aboveground biomass estimation software based on GPR model

草地生物量的研究对植物生态学和植物生产都具有重要的意义。草地生物量研究有助于更好地了解草地生态系统,草地生物量是草地生态系统健康评价、草地资源可持续开发利用的标准,可以帮助我们更好地管理草地。近年来,机器学习在估算草地生物量方面的研究取得了一定的进展。而高斯回归模型是一种有效的机器学习方法,可以用来估算草地生物量。它的优点在于可以利用草地的多种信息(如气候、土壤、植物特征等),从而更准确地预测草地的生物量,对正确识别草地特征和提高草地生物量估算的准确性有重要的意义。然而高斯回归模型具有一定的复杂性,对无程序基础的研究者来说实现具有一定的难度,由于它需要调整大量的参数,使模型能够很好地拟合数据,这需要大量的计算时间和计算资源,更需要不断尝试超参数的优化。如果不断更改代码的话,耗时耗力,并且需要一定程度的代码基础,因此设计基于GPR模型的草地生物量估算软件就十分具有必要性。

The study of grassland biomass is of great significance to plant ecology and plant production.Grassland biomass research helps to better understand the grassland ecosystem.Grassland biomass is the standard for grassland ecosystem health assessment and sustainable development and utilization of grassland resources,which helps us to better manage grassland.In recent years,machine learning has made some progress in estimating grassland biomass.The Gaussian regression model is an effective machine learning method that can be used to estimate grassland biomass.Its advantage is that it can use a variety of information of grassland(such as climate,soil,plant characteristics,etc.)to predict grassland biomass more accurately,which is of great significance for correctly identifying grassland characteristics and improving the accuracy of grassland biomass estimation.However,the Gaussian regression model has a certain complexity,and it is difficult for researchers without program basis to implement it.Because it needs to adjust a large number of parameters,so that the model can fit the data well,which requires a lot of computing time and computing resources.It is necessary to constantly try to optimize the hyper parameters.If the code is constantly changed,it is time‑consuming and requires a certain degree of code base.Therefore,it is necessary to design a grassland biomass estimation software based on GPR model.