Design of variable screw pitch rib snapping roller and residue cutter for corn harvesters

The blocking between two snapping rollers will seriously constrict the harvesting efficiency for corn harvester. A variable screw pitch rib snapping roller was developed to solve this problem. The comparative experiment between fixed screw pitch rib snapping rollers and variable screw pitch rib snapping rollers illustrated that variable screw pitch ribs can avoid corn-stalk blocking effectively, and it can improve working efficiency by 56.7%. Conservation tillage with standing corn residue was testified that it had a strong control of soil wind erosion. In order to implement this mode of conservation tillage at a production scale, a cutter was developed in this study. Subsequently, two experiments were conducted, one was to test the cutting ratio (defined as the totally cut off stalk population divided by total stalk population), and the other one was to test standing-residue height

Multi-temporal Monitoring for Road Slope Collapse by Means of LUTAN-1 SAR Data and High Resolution Optical Data

Collapse is one of the most destructive natural disaster, being sudden, frequent, and highly concealed, causing large-scale damage. On August 10, 2023, the slope of 108 national highway in Weinan, Shaanxi Province collapsed. The lower edge of the collapse slope body is the Luohe river, and the collapse body rushes into the river to form a barrier lake. Remote sensing technique can provide multiple dimensional information for disaster emergency and management. Lutan-1 SAR satellites are the first group L-band SAR constellation for multiple applications in China. Owing to the precise orbit control ability and high revisit characteristics for Lutan-1 SAR satellites, surface deformation monitoring with centimeter even millimeter accuracy may be achieved. Based on the multi-temporal pre-disaster and post-disaster Lutan-1 SAR data and high resolution optical data, the collapse information including the pre-disaster and post-disaster were extracted and analysed. From July 11 to 27, 2023, the pre-collapse deformation was obtained with the maximum value of 6 cm, and obvious deformation occurred before the collapse. Lutan-1 monitored results pre-collapse can provide certain information for disaster early identification. From July 27 to August 24, 2023, due to the serious incoherence caused by large deformation and ground changes, effective deformation information cannot be obtained based on the InSAR technique. In addition, the collapse information was clearly extracted by the high resolution optical data acquired pre-collapse and post collapse. After the collapse, significant deformation was extracted from August 24 to September 21 with the maximum value of 6 cm, indicating that obvious deformation still occurred over the collapse area. Through the analysis for the series results obtained by SAR and optical data, it is favourable for disaster emergency and management

Comparative study on Leaf disease identification using Yolo v4 and Yolo v7 algorithm

Agriculture is the primary occupation of nearly all nations that feed the world's population. The population growth and rising demand for food require farmers to increase food production to meet the requirements. On the other hand, farming is not regarded as a lucrative occupation, as farmers incur significant losses due to pests and diseases that reduce the quality and quantity of farm produce. Consequently, predicting plant diseases using modern technologies will aid producers in making well-informed decisions early on. This study employs and compares the results of two important computer vision algorithms, YOLOv4 and YOLOv7, for classifying leaf diseases from images of leaves from various plant species. The models are trained with images of individual leaves captured in various environments, imparting resilience and adaptability. Both models annotate and predict leaf diseases with high confidence for each class. Other classification metrics, such as Precision, F1-score, Mean Average Precision, and recall, also demonstrate competitive performance. However, YOLOv7 performs better because its flexible labeling mechanism dynamically learns the class labels. In addition, the work can be expanded to utilize recommendation strategies to predict the extent of injury.Wang Xinming (Dr, professor, Ph.D., Department of Mechanical and Manufacturing Engineering), Tang Sai Hong (Dr professor, Ph.D., Department of Mechanical and Manufacturing Engineering), Mohd Khairol Anuar b. Mohd Ariffin (Dr, professor, Ph.D., Department of Mechanical and Manufacturing Engineering), Mohd Idris Shah b. Ismail (Dr, professor, Ph.D., Department of Mechanical and Manufacturing Engineering)Includes bibliographical references

Updating Active Deformation Inventory Maps in Mining Areas by Integrating InSAR and LiDAR Datasets

Slope failures, subsidence, earthworks, consolidation of waste dumps, and erosion are typical active deformation processes that pose a significant hazard in current and abandoned mining areas, given their considerable potential to produce damage and affect the population at large. This work proves the potential of exploiting space-borne InSAR and airborne LiDAR techniques, combined with data inferred through a simple slope stability geotechnical model, to obtain and update inventory maps of active deformations in mining areas. The proposed approach is illustrated by analyzing the region of Sierra de Cartagena-La Union (Murcia), a mountainous mining area in southeast Spain. Firstly, we processed Sentinel-1 InSAR imagery acquired both in ascending and descending orbits covering the period from October 2016 to November 2021. The obtained ascending and descending deformation velocities were then separately post-processed to semi-automatically generate two active deformation areas (ADA) maps by using ADATool. Subsequently, the PS-InSAR LOS displacements of the ascending and descending tracks were decomposed into vertical and east-west components. Complementarily, open-access, and non-customized LiDAR point clouds were used to analyze surface changes and movements. Furthermore, a slope stability safety factor (SF) map was obtained over the study area adopting a simple infinite slope stability model. Finally, the InSAR-derived maps, the LiDAR-derived map, and the SF map were integrated to update a previously published landslides’ inventory map and to perform a preliminary classification of the different active deformation areas with the support of optical images and a geological map. Complementarily, a level of activity index is defined to state the reliability of the detected ADA. A total of 28, 19, 5, and 12 ADAs were identified through ascending, descending, horizontal, and vertical InSAR datasets, respectively, and 58 ADAs from the LiDAR change detection map. The subsequent preliminary classification of the ADA enabled the identification of eight areas of consolidation of waste dumps, 11 zones in which earthworks were performed, three areas affected by erosion processes, 17 landslides, two mining subsidence zone, seven areas affected by compound processes, and 23 possible false positive ADAs. The results highlight the effectiveness of these two remote sensing techniques (i.e., InSAR and LiDAR) in conjunction with simple geotechnical models and with the support of orthophotos and geological information to update inventory maps of active deformation areas in mining zones.This research was funded by the ESA-MOST China DRAGON-5 project (ref. 59339) and funded by a Chinese Scholarship Council studentship awarded to Liuru Hu (Ref. 202004180062)

Monitoring monthly surface water dynamics of Dongting Lake using Sentinel-1 data at 10 m

High temporal resolution water distribution maps are essential for surface water monitoring because surface water exhibits significant inner-annual variation. Therefore, high-frequency remote sensing data are needed for surface water mapping. Dongting Lake, the second-largest freshwater lake in China, is famous for the seasonal fluctuations of its inundation extents in the middle reaches of the Yangtze River. It is also greatly affected by the Three Gorges Project. In this study, we used Sentinel-1 data to generate surface water maps of Dongting Lake at 10 m resolution. First, we generated the Sentinel-1 time series backscattering coefficient for VH and VV polarizations at 10 m resolution by using a monthly composition method. Second, we generated the thresholds for mapping surface water at 10 m resolution with monthly frequencies using Sentinel-1 data. Then, we derived the monthly surface water distribution product of Dongting Lake in 2016, and finally, we analyzed the inner-annual surface water dynamics. The results showed that: (1) The thresholds were −21.56 and −15.82 dB for the backscattering coefficients for VH and VV, respectively, and the overall accuracy and Kappa coefficients were above 95.50% and 0.90, respectively, for the VH backscattering coefficient, and above 94.50% and 0.88, respectively, for the VV backscattering coefficient. The VV backscattering coefficient achieved lower accuracy due to the effect of the wind causing roughness on the surface of the water. (2) The maximum and minimum areas of surface water were 2040.33 km2 in July, and 738.89 km2 in December. The surface water area of Dongting Lake varied most significantly in April and August. The permanent water acreage in 2016 was 556.35 km2, accounting for 19.65% of the total area of Dongting Lake, and the acreage of seasonal water was 1525.21 km2. This study proposed a method to automatically generate monthly surface water at 10 m resolution, which may contribute to monitoring surface water in a timely manner

Analysis of regional large-gradient land subsidence in the Alto Guadalentín Basin (Spain) using open-access aerial LiDAR datasets

Land subsidence associated with groundwater overexploitation in the Alto Guadalentín Basin (Spain) aquifer system has been detected during the last decades. In this work, for the first time, we propose a new point cloud differencing methodology to detect land subsidence at basin scale, based on the multiscale model-to-model cloud comparison (M3C2) algorithm. This method is applied to two open-access airborne LiDAR datasets acquired in 2009 and 2016, respectively. First the internal edge connection errors in the different flight lines were addressed by means of a smoothing point cloud method. LiDAR datasets capture information from ground and non-ground points. Therefore, a method combining gradient filtering and cloth simulation filtering (CSF) algorithms was applied to remove non-ground points. The iterative closest point (ICP) algorithm was used for point cloud registration of both point clouds exhibiting a very stable and robust performance. The results show that vertical deformation rates are up to −14 cm/year in the basin from 2009 to 2016, in agreement with the displacement reported by previous studies. LiDAR results have been compared to the velocity measured by continuous GNSS stations and an InSAR dataset. For the GNSS-LiDAR and InSAR-LiDAR comparison, we computed a common 100 × 100 m grid in order to assess any similarities and discrepancies. The results show a good agreement between the vertical displacements obtained from the three different surveying techniques. Furthermore, LiDAR results were compared with the distribution of compressible soil thickness showing a clear relationship. The study underlines the potential of open-access and non-customized LiDAR to monitor the distribution and magnitude of vertical deformations in areas prone to be affected by groundwater-withdrawal-induced land subsidence.This research was funded by the ESA-MOST China DRAGON-5 project (ref. 59339) and by a Chinese Scholarship Council studentship awarded to Liuru Hu (Ref. 202004180062). María I. Navarro-Hernández and Guadalupe Bru are funded by the PRIMA programme supported by the European Union under grant agreement No 1924, project RESERVOIR

Seasonal and Diurnal Variations of Atmospheric Non-Methane Hydrocarbons in Guangzhou, China

In recent decades, high ambient ozone concentrations have become one of the major regional air quality issues in the Pearl River Delta (PRD) region. Non-methane hydrocarbons (NMHCs), as key precursors of ozone, were found to be the limiting factor in photochemical ozone formation for large areas in the PRD. For source apportioning of NMHCs as well as ozone pollution control strategies, it is necessary to obtain typical seasonal and diurnal patterns of NMHCs with a large pool of field data. To date, few studies have focused on seasonal and diurnal variations of NMHCs in urban areas of Guangzhou. This study explored the seasonal variations of most hydrocarbons concentrations with autumn maximum and spring minimum in Guangzhou. The diurnal variations of most anthropogenic NMHCs typically showed two-peak pattern with one at 8:00 in the morning and another at 20:00 in the evening, both corresponding to traffic rush hours in Guangzhou, whereas isoprene displayed a different bimodal diurnal curve. Propene, ethene, m, p-xylene and toluene were the four largest contributors to ozone formation in Guangzhou, based on the evaluation of individual NMHCs’ photochemical reactivity. Therefore, an effective strategy for controlling ozone pollution may be achieved by the reduction of vehicle emissions in Guangzhou