Accurate cotton maps are crucial for monitoring cotton growth and precision management. The paper proposed a county-scale cotton mapping method by using random forest (RF) feature selection algorithm and classifier based on selecting multi-features, including spectral, vegetation indices, and texture features. The contribution of texture features to cotton classification accuracy was also explored in addition to spectral features and vegetation index. In addition, the optimal classification time, feature importance, and the best classifier on the cotton extraction accuracy were evaluated. The results showed that the texture feature named the gray level co-occurrence matrix (GLCM) is effective for improving classification accuracy, ranking second in contribution among all studied spectral, VI, and texture features. Among the three classifiers, the RF showed higher accuracy and better stability than support vector machines (SVM) and artificial neural networks (ANN). The average overall accuracy (OA) of the classification combining multiple features was 93.36%, 7.33% higher than the average OA of the single-time spectrum, and 2.05% higher than the average OA of the multi-time spectrum. The classification accuracy after feature selection by RF can still reach 92.12%, showing high accuracy and efficiency. Combining multiple features and random forest methods may be a promising county-scale cotton classification method.
The aim is to investigate that 17β-estradiol (E2)/estrogen receptors (ERs) activation normalizes splenic CD4 + T lymphocytes proliferation and cytokine production through inhibition of endoplasmic reticulum stress (ERS) following hemorrhage. The results showed that hemorrhagic shock (hemorrhage through femoral artery, 38–42 mmHg for 90 min followed by resuscitation of 30 min and subsequent observation period of 180 min) decreased the CD4+ T lymphocytes proliferation and cytokine production after isolation and incubation with Concanavalin A (5 μg/mL) for 48 h, induced the splenic injury with evidences of missed contours of the white pulp, irregular cellular structure, and typical inflammatory cell infiltration, upregulated the expressions of ERS biomarkers 78 kDa glucose-regulated protein (GRP78) and activating transcription factor 6 (ATF6). Either E2, ER-α agonist propyl pyrazole triol (PPT) or ERS inhibitor 4-Phenylbutyric acid administration normalized these parameters, while ER-β agonist diarylpropionitrile administration had no effect. In contrast, administrations of either ERs antagonist ICI 182,780 or G15 abolished the salutary effects of E2. Likewise, ERS inducer tunicamycin induced an adverse effect similarly to that of hemorrhagic shock in sham rats, and aggravated shock-induced effects, also abolished the beneficial effects of E2 and PPT, respectively. Together, the data suggest that E2 produces salutary effects on CD4+ T lymphocytes function, and these effects are mediated by ER-α and GPR30, but not ER-β, and associated with the attenuation of hemorrhagic shock-induced ERS.
In order to realize cotton growth monitoring, a cotton planting monitoring system based on image processing technology was proposed. The system requires camera to collect cotton canopy images, leaf areometer to detect the leaf area index, and spectrometer to detect the normalized vegetation index ( NDVI ) and the vegetation index ( RVI ). Due to different types of data, based on the establishment of the output transmission system, the canopy coverage was calculated by the image processing method, and there was a linear relationship between canopy coverage NDVI and RVI . The leaf area index (LAI) of N content was established, and the model of dry matter accumulation and canopy coverage was exponential. The experiment result shows that the linear and exponential coefficients of the model k and b increased with the increase of the nitrogen application rate. The fitting determination coefficient remained high under different nitrogen application rates. The fitting coefficient R2 of the three models in the two test fields ranged from 0.83 to 0.923, which also met the needs of model evaluation. The system was used to detect the cotton field with good accuracy.
Studies of traits related to nitrogen (N)-use efficiency (NUE) in wheat cultivars are important for breeding N-efficient cultivars. Canopy structure has a major effect on NUE, as it determines the distribution of light and N. However, the mechanism by which canopy structure affects the distribution of light and N within the canopy remains unclear. The N-efficient winter wheat varieties YM49 and ZM27 and N-inefficient winter wheat varieties XN509 and AK58 were grown in the field under two N levels. Light transmittance was enhanced, and the leaf area index and photosynthetically active radiation were lower in the N-efficient cultivar population, which was characterized by moderately sized flag leaves, a low frequency of canopy leaf curling, a low light attenuation coefficient (KL), and high plant compactness. Reductions in the amount of shade increased the distribution of light and N resources to the middle and lower layers. The photosynthetic rate, transpiration rate, instant water-use efficiency, and canopy photosynthetic NUE were higher, N remobilization of the upper and middle canopy leaves was reduced, and the leaf N content was high in the N-efficient cultivars. A higher ratio of the N extinction coefficient (KN) to KL reflects the assimilation ability of the N-efficient winter wheat cultivars, resulting in improved canopy structure and distribution of light and N, higher 1000-grain weight and grain yield, and significantly increased light and NUE. An improved match between gradients of light and N in the leaf canopy promotes balanced C and N metabolism and reduces energy and nutrient losses. This should be a goal when breeding N-efficient wheat cultivars and implementing tillage regimes.
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