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Accurate and rapid estimation of leaf nitrogen content (LNC) in winter wheat using hyperspectral techniques is important for growth monitoring and accurate fertilization. Based on field experiments conducted over four years at three ecological sites with four different nitrogen (N) application treatments and four different N-efficient wheat varieties in Henan Province, the canopy spectra and LNC of winter wheat were obtained simultaneously in the main growth stages. The original spectrum was subjected to continuum removal (CR), the correlation between LNC and various vegetation indices was systematically analysed, and the optimal vegetation index estimation model for LNC was constructed. Simultaneously, discrete wavelet transform (DWT) was used to further compress and extract the CR spectrum. The model was combined with partial least squares regression (PLSR) and K-nearest neighbour (KNN) algorithms to estimate LNC in winter wheat. The results showed that the spectrum treated with CR was significantly improved in its correlation with LNC and that the model established based on normalized vegetation index NDVI (CR728, CR977) combined with the CR spectrum was better compared to existing vegetation indices. The calibration and validation coefficients of determination (R2) and root mean square error (RMSE) were 0.816 and 0.799, and 0.352% and 0.342%, respectively. DWT was used to transform the CR spectrum, and the results showed that a combination of the CR spectrum and a sym8 wavelet function with PLSR based on the approximation coefficients at decomposition level 2 predicted LNC most accurately. The coefficient of determination RC2, root mean square error RMSEC, and relative percent deviation (RPDC) of the calibration set were 0.884, 0.279%, and 2.932, respectively, and RV2, RMSEV, and RPDV of the validation set were 0.855, 0.291%, and 2.619, respectively, indicating that the model had good stability and predictive ability. Combination of CR and DWT improved the modelling accuracy of wheat LNC and showed better prediction results for multi-year and multi-point samples. The results of the study can provide a basis and reference for rapid monitoring of LNC in winter wheat. [ABSTRACT FROM AUTHOR]

Remote controlled soft actuators have attracted ever-increasing interest in industrial, medical, robotics, and engineering fields. Soft actuators are charming than normal tools in executing dedicate tasks due to small volume and flexible body they have. However, it remains a challenge to design soft actuator that can adapt to multi-environments under remote stimuli with promising nano materials. Herein, we have developed a kind of near-infrared laser driven soft actuators with multi locomotive modes based on WSe2 and graphene nanosheets heterojunction. Different locomotion modes are driven by photothermal effect induced deformation to adapt to different working conditions. Moreover, the specially designed gripper driven by pulsed laser can lift a heavy load which is four times of its weight. This work broadens the choice of advanced nanomaterials for photothermal conversion of soft actuators. It is promising to realize applications including photothermal therapy and complex environment detection through the combination of the intelligent robot design and optical fiber system. [ABSTRACT FROM AUTHOR]