Summary The rapid selection of salinity‐tolerant crops to increase food production in salinized lands is important for sustainable agriculture. Recently, high‐throughput plant phenotyping technologies have been adopted that use plant morphological and physiological measurements in a non‐destructive manner to accelerate plant breeding processes. Here, a hyperspectral imaging (HSI) technique was implemented to monitor the plant phenotypes of 13 okra (Abelmoschus esculentus L.) genotypes after 2 and 7 days of salt treatment. Physiological and biochemical traits, such as fresh weight, SPAD, elemental contents and photosynthesis‐related parameters, which require laborious, time‐consuming measurements, were also investigated. Traditional laboratory‐based methods indicated the diverse performance levels of different okra genotypes in response to salinity stress. We introduced improved plant and leaf segmentation approaches to RGB images extracted from HSI imaging based on deep learning. The state‐of‐the‐art performance of the deep‐learning approach for segmentation resulted in an intersection over union score of 0.94 for plant segmentation and a symmetric best dice score of 85.4 for leaf segmentation. Moreover, deleterious effects of salinity affected the physiological and biochemical processes of okra, which resulted in substantial changes in the spectral information. Four sample predictions were constructed based on the spectral data, with correlation coefficients of 0.835, 0.704, 0.609 and 0.588 for SPAD, sodium concentration, photosynthetic rate and transpiration rate, respectively. The results confirmed the usefulness of high‐throughput phenotyping for studying plant salinity stress using a combination of HSI and deep‐learning approaches.

Corncob, as a sustainable biomass waste, is mainly composed of hemicellulose. Herein, on the basis of natural corncob as substrates, Fe/N co-doped porous carbon spheres were designed via a consecutive FeCl3-mediated hydrothermal reaction and mild KHCO3 activation route for supercapacitor electrode materials. Owing to the low hydrolysis temperature of hemicellulose and hydrolysis promotion of Fe3+, the corncob-derived hydrochar exhibited special carbon sphere morphology. Interestingly, the carbon sphere morphology was well-preserved upon the melamine-mediated KHCO3 activation. As a result of the short ion diffusion distance, unique packing architecture, and developed micro–mesoporous structure of the carbon spheres, optimized CCAC-Fe-M-50% manifested superior ion transfer kinetics and rate performances (87% up to 20 A g–1). Meanwhile, the electrochemical investigation of CCAC-Fe-M-50% in a three-electrode setup illustrated high capacitance (338 F g–1 at 1 A g–1). In a two-electrode setup, the CCAC-Fe-M-50%||CCAC-Fe-M-50% device revealed supreme cyclability (102.7% retention after 5000 cycles) and extremely low R ct (0.59 Ω) and R s (4.54 Ω). These superior properties were attributed to the large S BET (2305.7 m2 g–1), the multiple redox possibilities (Fe3+, Fe2+, and N functional groups), and the carbon sphere morphology with a micro–mesoporous structure, which enhanced ion physisorption, pseudocapacitance, and electrolyte/ion diffusion, respectively. Besides, the fabricated CCAC-Fe-M-50%||CCAC-Fe-M-50% device in a neutral electrolyte demonstrated a superb energy density (E D) of 18.60 Wh kg–1 at the power density (P D) of 455 W kg–1. The currently presented strategy with superior results might lead to the novel development of biomass-based ultraperformance electrode materials for supercapacitors and other high-tech applications.

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The feasibility of using the fourier transform infrared (FTIR) spectroscopic technique with a stacked sparse auto-encoder (SSAE) to identify orchid varieties was studied. Spectral data of 13 orchids varieties covering the spectral range of 4000–550 cm−1 were acquired to establish discriminant models and to select optimal spectral variables. K nearest neighbors (KNN), support vector machine (SVM), and SSAE models were built using full spectra. The SSAE model performed better than the KNN and SVM models and obtained a classification accuracy 99.4% in the calibration set and 97.9% in the prediction set. Then, three algorithms, principal component analysis loading (PCA-loading), competitive adaptive reweighted sampling (CARS), and stacked sparse auto-encoder guided backward (SSAE-GB), were used to select 39, 300, and 38 optimal wavenumbers, respectively. The KNN and SVM models were built based on optimal wavenumbers. Most of the optimal wavenumbers-based models performed slightly better than the all wavenumbers-based models. The performance of the SSAE-GB was better than the other two from the perspective of the accuracy of the discriminant models and the number of optimal wavenumbers. The results of this study showed that the FTIR spectroscopic technique combined with the SSAE algorithm could be adopted in the identification of the orchid varieties.

Charring agent (dipentaerythritol (DPER) biphosphite) was synthesized and characterized by Fourier transform infrared (FTIR), hydrogen nuclear magnetic resonance, and elemental analysis. Then, three halogen-free intumescent flame retardants, DPER biphosphite trimethyl phosphate (DTMP), DPER biphosphite triethyl phosphate (DTEP), and DPER biphosphite dimethyl methyl phosphonate (DDMMP) were prepared and highly thermally stable polymers were obtained. FTIR demonstrated that new peaks of P-C and P=O were formed in the three FRs in comparison with that of DPER biphosphite. Thermogravimetric analysis indicated that DPER biphosphite could have a profound effect on increasing char yield and P-C bonds confer a higher thermal stability to the polymers. The results of UL-94 burn test showed that the polymers prepared had good flame retardance.