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Effective synthesis and application of single-atom catalysts on supports lacking enough defects remain a significant challenge in environmental catalysis. Herein, we present a universal defect-enrichment strategy to increase the surface defects of CeO2-based supports through H2 reduction pretreatment. The Pt catalysts supported by defective CeO2-based supports, including CeO2, CeZrOx, and CeO2/Al2O3 (CA), exhibit much higher Pt dispersion and CO oxidation activity upon reduction activation compared to their counterpart catalysts without defect enrichment. Specifically, Pt is present as embedded single atoms on the CA support with enriched surface defects (CA-HD) based on which the highly active catalyst showing embedded Pt clusters (PtC) with the bottom layer of Pt atoms substituting the Ce cations in the CeO2 surface lattice can be obtained through reduction activation. Embedded PtC can better facilitate CO adsorption and promote O2 activation at PtC-CeO2 interfaces, thereby contributing to the superior low-temperature CO oxidation activity of the Pt/CA-HD catalyst after activation.

ObjectiveRapid recognition and automatic positioning of table grapes in the natural environment is the prerequisite for the automatic picking of table grapes by the picking robot.MethodsAn rapid recognition and automatic picking points positioning method based on improved K-means clustering algorithm and contour analysis was proposed. First, euclidean distance was replaced by a weighted gray threshold as the judgment basis of K-means similarity. Then the images of table grapes were rasterized according to the K value, and the initial clustering center was obtained. Next, the average gray value of each cluster and the percentage of pixel points of each cluster in the total pixel points were calculated. And the weighted gray threshold was obtained by the average gray value and percentage of adjacent clusters. Then, the clustering was considered as have ended until the weighted gray threshold remained unchanged. Therefore, the cluster image of table grape was obtained. The improved clustering algorithm not only saved the clustering time, but also ensured that the K value could change adaptively. Moreover, the adaptive Otsu algorithm was used to extract grape cluster information, so that the initial binary image of the table grape was obtained. In order to reduce the interference of redundant noise on recognition accuracy, the morphological algorithms (open operation, close operation, images filling and the maximum connected domain) were used to remove noise, so the accurate binary image of table grapes was obtained. And then, the contours of table grapes were obtained by the Sobel operator. Furthermore, table grape clusters grew perpendicular to the ground due to gravity in the natural environment. Therefore, the extreme point and center of gravity point of the grape cluster were obtained based on contour analysis. In addition, the linear bundle where the extreme point and the center of gravity point located was taken as the carrier, and the similarity of pixel points on both sides of the linear bundle were taken as the judgment basis. The line corresponding to the lowest similarity value was taken as the grape stem, so the stem axis of the grape was located. Moreover, according to the agronomic picking requirements of table grapes, and combined with contour analysis, the region of interest (ROI) in picking points could be obtained. Among them, the intersection of the grapes stem and the contour was regarded as the middle point of the bottom edge of the ROI. And the 0.8 times distance between the left and right extreme points was regarded as the length of the ROI, the 0.25 times distance between the gravity point and the intersection of the grape stem and the contour was regarded as the height of the ROI. After that, the central point of the ROI was captured. Then, the nearest point between the center point of the ROI and the grape stem was determined, and this point on the grape stem was taken as the picking point of the table grapes. Finally, 917 grape images (including Summer Black, Moldova, and Youyong) taken by the rear camera of MI8 mobile phone at Jinniu Mountain Base of Shandong Fruit and Vegetable Research Institute were verified experimentally.Results and Discussions]The results showed that the success rate was 90.51% when the error between the table grape picking points and the optimal points were less than 12 pixels, and the average positioning time was 0.87 s. The method realized the fast and accurate localization of table grape picking points. On top of that, according to the two cultivation modes (hedgerow planting and trellis planting) of table grapes, a simulation test platform based on the Dense mechanical arm and the single-chip computer was set up in the study. 50 simulation tests were carried out for the four conditions respectively, among which the success rate of localization for purple grape picking point of hedgerow planting was 86.00%, and the average localization time was 0.89 s; the success rate of localization for purple grape identification and localization of trellis planting was 92.00%, and the average localization time was 0.67 s; the success rate of localization for green grape picking point of hedgerow planting was 78.00%, and the average localization time was 0.72 s; and the success rate of localization for green grape identification and localization of trellis planting was 80.00%, and the average localization time was 0.71 s.ConclusionsThe experimental results showed that the method proposed in the study can meet the requirements of table grape picking, and can provide technical supports for the development of grape picking robot.

[Objective] The effects of winter irrigation on soil water-heat-salt migration and crop growth during the freezing-thawing period were analyzed in order to provide a scientific basis for controlling soil salinization, and to ensure food security. [Methods] Statistical analysis and visualization methods were used to compare and analyze soil water-heat-salt migration and crop growth in winter irrigation and non-winter irrigation areas based on meteorological, soil physicochemical indicators, and crop growth data in an experimental field in the Qingtongxia Irrigation District of Ningxia from 25 October 2018 to 3 October 2020. [Results] Soil temperature changes in the Qingtongxia Irrigation District lagged behind air temperature changes, and the shallow surface soil temperature was more significantly affected by air temperature changes. The soil gradually froze from the surface downward and the freezing depth deepened as the temperature decreased. The frozen layer of soil thawed simultaneously from the soil surface and the bottom of the measurement profile during the thawing period. The entire soil freezing and thawing period lasted for 98 days. The soil moisture content increased significantly after irrigation. During the freezing period, irrigation decreased the temperature of each soil layer by 0.1—1.1 ℃. The variation trends of soil temperature and moisture in different soil layers during the winter irrigation period were basically the same as those during the non-winter irrigation period. After winter irrigation, the salinity of each soil layer decreased, especially in the 0—30 cm soil layer, and salinity decreased significantly. However, after the freezing-thawing period, salt accumulated in the soil. Winter wheat grew taller and corn grew faster in the winter irrigation farmland than in the non-winter irrigation farmland. Average wheat and corn yields increased by 20.64% and 4.20%, respectively, in the winter irrigation farmland. [Conclusion] Winter irrigation affected the distribution of soil water, heat, and salt, and effectively reduced soil salinity, resulting in increased crop growth and yield (especially wheat yield) in the Qingtongxia Irrigation District.