Linsheng Huang

TL;DR: The detection of the severity of yellow rust using the yellow rust-index (YRI) showed a high coefficient of determination between the estimated DI and its observations, suggesting that the NSIs may improve disease detection in precision agriculture application.

TL;DR: Nanoscale multi-dimensional heterojunctions in situ grow at the edge of two-dimensional MXene conductive network and the perovskite solar cells achieve high power conversion efficiency and high moisture-resistance stability.

TL;DR: The deep learning networks provide feasible alternatives for the recognition and quantitation of SERS and perform better than the common machine learning methods.

TL;DR: Hyperspectral reflectance data obtained from an airborne hyperspectral imager were used to model LAI of winter wheat canopy in the 2002 crop growing season and showed that NDVI-like was the most accurate predictor of LAI.

TL;DR: The results reveal that the SVIKDA outperformed conventional linear discriminant approach on detection and classification among healthy wheat leaves and leaves infected with yellow rust, aphids, and powdery mildew and suggest that this method has reliable transferability and great robustness in detecting and discriminating pests and diseases for guiding precision plant protection.