Objective: to achieve accurately and rapidly the mapping of agricultural land use and crop distribution at the township scale. Methods: this study, based on specific methods, such as, time-series remote sensing index threshold classification and maximum likelihood, classifies each land use type and extracts crop spatial information, under the guidance of Sentinel-2A remote sensing images, to carry out agricultural land use mapping at township scale. And the mapping concerned will be verified by comparing with an agricultural spatial information map of a 0.5 m resolution, which is based on WorldVieW-2 fused images. Results: (1) the area accuracy of grain and oil crop land, vegetable land, agricultural facilities land and garden land is fairly good, with 92.93%, 98.98%, 95.71% and 95.14% respectively, and within 8% variation from actual area; (2) the spatial information of plot boundary, farmland road network, and canal network produced by OSM road data and historical high-resolution images was overlayed with the classification results of Sentinel-2A multi-spectral image for mapping, which can improve the accuracy of plot boundary information of classification results for the image with 10 m resolution. Conclusions: the use of multi-source information fusion method, agricultural land use and crop distribution space big data produced by Sentinel-2A optical image, can effectively improve the accuracy and timeliness of land use mapping at the township scale, to provide technical reference for the application of remote sensing big data to carry out agricultural landscape analysis at the township scale, optimization and adjustment of agricultural structure, etc.

The optimized methodology and results of the new characterization in terms of dose and image quality of the X-ray system used in the main pediatric hemodynamics service in Chile are presented. In addition, scattered dose rate values at the operator’s eye level are reported for all acquisition modes available in different thicknesses of absorbent media and angiography. The characterization was performed according to the European DIMOND and SENTINEL protocols adapted to pediatric procedures. The air kerma at the entrance surface (ESAK) was measured and the image quality parameters signal-to-noise ratio (SNR) and a figure of merit (FOM) were calculated. The scattered dose rate was measured in personal dose equivalent units. The ESAK for fluoroscopic modes ranged from 0.2 to 35.6 μGy/image when passing from 4 to 20 cm of polymethyl methacrylate (PMMA). For the cine mode, these values ranged from 2.8 to 160.1 μGy/image. The values of the image quality parameters showed a correct system configuration, although abnormal values were observed in the medium fluoroscopic mode. As for the scattered dose rate at the level of the cardiologist’s eyes, the highest value is PMMA with a thickness of 20 cm, where the cine mode reached 9.41 mSv·h-1. The differences found from previous evaluations can be explained by the deterioration of the system and the change of one of the X-ray tubes.

In agriculture, crop yield and quality are critical for global food supply and human survival. Challenges such as plant leaf diseases necessitate a fast, automatic, economical, and accurate method. This paper utilizes deep learning, transfer learning, and specific feature learning modules (CBAM, Inception-ResNet) for their outstanding performance in image processing and classification. The ResNet model, pretrained on ImageNet, serves as the cornerstone, with introduced feature learning modules in our IRCResNet model. Experimental results show our model achieves an average prediction accuracy of 96.8574% on public datasets, thoroughly validating our approach and significantly enhancing plant leaf disease identification.