Combination of effective color information and machine learning for rapid prediction of soil water content

Soil water content(SWC)is one of the critical indicators in various fields such as geotechnical engineering and agriculture.To avoid the time-consuming,destructive,and laborious drawbacks of conventional SWC measurements,the image-based SWC prediction is considered based on recent advances in quantitative soil color analysis.In this study,a promising method based on the Gaussian-fitting gray histogram is proposed for extracting characteristic parameters by analyzing soil images,aiming to alleviate the interference of complex surface conditions with color information extraction.In addition,an identity matrix consisting of 32 characteristic parameters from eight color spaces is constituted to describe the multi-dimensional information of the soil images.Meanwhile,a subset of 10 parameters is identified through three variable analytical methods.Then,four machine learning models for SWC prediction based on partial least squares regression(PLSR),random forest(RF),support vector machines regression(SVMR),and Gaussian process regression(GPR),are established using 32 and 10 characteristic parameters,and their performance is compared.The results show that the characteristic parameters obtained by Gaussian-fitting can effectively reduce the interference from soil surface conditions.The RGB,CIEXYZ,and CIELCH color spaces and lightness parameters,as the inputs,are more suitable for the SWC prediction models.Furthermore,it is found that 10 parameters could also serve as optimal and generalizable predictors without considerably reducing prediction accuracy,and the GPR model has the best prediction performance(R^(2)≥0.95,RMSE≤2.01%,RPD≥4.95,and RPIQ≥6.37).The proposed image-based SWC predictive models combined with effective color information and machine learning can achieve a transient and highly precise SWC prediction,providing valuable insights for mapping soil moisture fields.

Modulated illumination localization microscopy-enabled sub-10 nm resolution

Optical microscopy is an essential tool for exploring the structures and activities of cells and tissues.To break the limit of resolution caused by diffraction,researchers have made continuous advances and innovations to improve the resolution of optical microscopy since the 1990s.These contributions,however,still make sub-10nm imaging an obstacle.Here,we name a series of technologies as modulated illumination localization microscopy(MILM),which makes ultra-high-resolution imaging practical.Besides,we review the recent progress since 2017 when MINFLUX was proposed and became the inspiration and foundation for the follow-up devel-opment of MILM.This review divides MILM into two types:point-scanning and wide-field.The schematics,principles and future research directions of MILM are discussed elaborately.

Fluorescence interference structured illumination microscopy for 3D morphology imaging with high axial resolution

Imaging three-dimensional,subcellular structures with high axial resolution has always been the core purpose of fluorescence microscopy.However,trade-offs exist between axial resolution and other important technical indicators,such as temporal resolution,optical power density,and imaging process complexity.We report a new imaging modality,fluorescence interference structured illumination microscopy(FI-SIM),which is based on three-dimensional structured illumination microscopy for wide-field lateral imaging and fluorescence interference for axial reconstruction.FI-SIM can acquire images quickly within the order of hundreds of milliseconds and exhibit even 30 nm axial resolution in half the wavelength depth range without z-axis scanning.Moreover,the relatively low laser power density relaxes the requirements for dyes and enables a wide range of applications for observing fixed and live subcellular structures.