Estimation of Potato Biomass and Yield Based on Machine Learning from Hyperspectral Remote Sensing Data

The estimation of potato biomass and yield can optimize the planting pattern and tap the production potential.Based on partial least square(PLSR),multiple linear regression(MLR),support vector machine(SVM),random forest(RF),BP neural network and other machine learning algorithms,the biomass estimation model of potato in different growth stages is constructed by using single variables such as original spectrum,first-order differential spectrum,combined spectrum index and vegetation index(VI)and their coupled combination variables.The accuracy of the models is compared and analyzed,and the best modeling method of biomass in different growth stages is selected.Based on the optimized modeling method,the biomass of each growth stage is estimated,and the yield estimation model of different growth stages is constructed based on the estimation results and the linear regression analysis method,and the accuracy of the model is verified.The results showed that in tuber formation stage,starch accumulation stage and maturity stage,the biomass estimation accuracy based on combination variable was the highest,the best modeling method was MLR and SVM,in tuber growth stage,the best modeling method was MLR,the effect of yield estimation is good.It provides a reference for the algorithm selection of crop biomass and yield models based on machine learning.

生物起搏器:从生物实验到计算机模拟

起搏功能障碍已成为威胁人类健康的一种重大疾病,严重时可能导致心律失常、晕厥,甚至死亡。到目前为止,治疗起搏功能障碍的最佳方案是植入电子起搏器。但是它存在一些缺点,例如电池寿命有限,手术过程具有感染的风险,起搏频率单一等。因此,对生物起搏器的研究显得尤为迫切。生物起搏器不但引起并发症的风险较低,而且能够对生理情绪做出反应,从而有望替代电子起搏器,进行心脏起搏障碍治疗。本文从生物实验和计算机模拟两方面对生物起搏器的发展进行综述。前者主要包括基因疗法和细胞疗法的实验成果,而后者介绍了多尺度的心脏建模从单个细胞到组织切片进行起搏器研究的进展。迄今为止,生物起搏器已被应用于大型哺乳动物实验,但将其应用于临床心脏病治疗,仍有很长的路要走。利用计算机模型对生物起搏器诱发过程进行建模,有望加速研究进程。在本文中,我们首先回顾了生物起搏器实验研究的发展,然后介绍了生物起搏器计算机模型的目前的相关工作。最后,我们提出了基于心脏计算机模型研究生物起搏器的潜在研究方向。