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To make an accurate estimation of leaf are index (LAI) based on machine learning methods and images from UAV, we compared the several mainstream machine learning methods for maize LAI prediction, such as Artificial Neural Network method (ANN), Gaussian Process Regression method (GPR), Support Vector Regression method (SVR), and Gradient Boosting Decision Tree (GBDT). For this purpose, field experiments that considering apply of different amount of organic fertilizer, different amount of inorganic fertilizer, different amount of crop residue, and different planting density were carried out. Based on these experiments, field campaign were conducted to obtain UAV multispectral images and LAI data at different growth stages in maize. Based on above data, firstly, correlation analysis was used to select LAI-sensitive spectral indices, and then the Partial Least Squares Regression method (PLSR) and ANN, GPR, SVR, GBDT were coupled to design the LAI prediction models, respectively, and their performance for LAI prediction were compared. The results showed that the LAI prediction model constructed by PLSR+GBDT method had the highest accuracy and the best stability. The models of R² and RMSE values were 0.90 and 0.25, and the verified R² and RMSE values were 0.90 and 0.29 during validation, respectively. The model based on PLSR+GPR model was followed, with R² and RMSE values of 0.86 and 0.30 during calibration, and R² and RMSE values of 0.89 and 0.29 during validation, respectively. Besides, it had faster training speed and could give the uncertainty of the prediction. The model designed by PLSR+ANN method had R² and RMSE values of 0.85 and 0.31 during calibration, and R² and RMSE values of 0.89 and 0.30 during validation, respectively. The model designed by PLSR+SVR method had R² and RMSE values of 0.86 and 0.32, and R² and RMSE values of 0.90 and 0.33, respectively. Therefore, PLSR+GBDT method and PLSR+GPR method are recommended as the optimal methods for designing maize LAI prediction models. [ABSTRACT FROM AUTHOR]

The overuse of fipronil (FPN, a broad-spectrum insecticide) in agriculture has brought great concerns for environmental pollution and food safety. The development of a rapid, reliable, and portable analytical method for the on-site monitoring of FPN is therefore of great significance but is full of challenge. Herein, a novel supramolecular probe using human serum albumin (HSA) as the host and an aggregation-induced emission-active fluorescence probe LIQ-TPA-TZ as the guest was developed for the colorimetric and ratiometric detection of FPN, displaying fast response (30 s), high sensitivity (LOD ∼ 0.05 μM), and good selectivity and anti-interference performance. Moreover, portable paper-based test strips could be facilely obtained and utilized for the determination of FPN, showing colorimetric changes from yellow to orange. This supramolecular probe also demonstrated great potential in real applications for choosing the best cleaning method to reduce the residue rate of FPN on apples. This study provides a versatile tool for the fast and real-time analysis of FPN, which greatly benefits the on-site determination of pesticides with the use of simple testing apparatus. [ABSTRACT FROM AUTHOR]

Hybrid organic‐inorganic perovskites (HOIP) due to their excellent optoelectronic properties and flexible structure have attracted enthusiastic interest. In particular, introducing chirality is a method to enhance compound performance. Herein, we report a multifunctional compound: (3APr)PbBr4 (1) (3APr=3‐Pyrrolidinamine) and corresponding enantiomer R and S‐(3APr)PbBr4 (R/S‐2). Compound 1 show reversible solid‐state phase transition, step‐like dielectric anomaly and broadband yellow emission under uv light excitation. Accompany with phase transition, structure dimension transition from 2D to 1D without space group change. Through introduce chirality, the R/S‐2 display mirror image 1D structural relationship, increased quantum yield from 3.43 % (1) to 13.65 % (R/S‐2) and exhibits corresponding CD signals. Then combine to first‐principles analysis, it was found that fluorescence is attributed to the formation of instantaneous defects during excitation, leading to the formation of self‐trapped excitons (STEs). This finding will further promote the development of multifunctional compound and the study of chiral substitution enhance compound properties. [ABSTRACT FROM AUTHOR]

Organic‐inorganic hybrid multifunctional materials have received significant attention due to their potential applications in smart devices such as phase‐change memory (PCM), resistive random‐access memory (ReRAM). Yet, devices application requires achieving a balance between fast switching speeds, long‐term stability, and high cycling endurance in materials still facing difficulties. Herein, we report two hybrid phase transition compounds: (CMQ)BiCl5(CMQ=Chloromethyl Quinuclidin‐3‐one) (1) and (FMQ)2SbBr5(FMQ=Fluoromethyl Quinuclidin‐3‐one) (2), both two compounds exhibit high phase transition temperature, dual‐stable behavior in dielectric during reversible phase transition process. Notably, 1 exhibit small dielectric change after multiple dielectric state switching. Furthermore, investigate the influence of halogen substitution (F to Cl) for compound properties, which show an Increasing transition temperature (9 K) and raising structural dimension (0D to 1D). Additionally, using first principles, analyze the electronic structure of 1 and find that it has a 3.4 eV wide bandgap, closing to GaN semiconductor (3.4 eV). This work provides a new insight into developing the next‐generation phase transition compounds and chemical modification enhances material performance. [ABSTRACT FROM AUTHOR]