RESUMO
The kidney bean is an important cash crop whose growth and yield are severely affected by brown spot disease. Traditional target detection models cannot effectively screen out key features, resulting in model overfitting and weak generalization ability. In this study, a Bi-Directional Feature Pyramid Network (BiFPN) and Squeeze and Excitation (SE) module were added to a YOLOv5 model to improve the multi-scale feature fusion and key feature extraction abilities of the improved model. The results show that the BiFPN and SE modules show higher heat in the target location region and pay less attention to irrelevant environmental information in the non-target region. The detection Precision, Recall, and mean average Precision (mAP@0.5) of the improved YOLOv5 model are 94.7%, 88.2%, and 92.5%, respectively, which are 4.9% higher in Precision, 0.5% higher in Recall, and 25.6% higher in the mean average Precision compared to the original YOLOv5 model. Compared with the YOLOv5-SE, YOLOv5-BiFPN, FasterR-CNN, and EfficientDet models, detection Precision improved by 1.8%, 3.0%, 9.4%, and 9.5%, respectively. Moreover, the rate of missed and wrong detection in the improved YOLOv5 model is only 8.16%. Therefore, the YOLOv5-SE-BiFPN model can more effectively detect the brown spot area of kidney beans.
RESUMO
Leaf area index (LAI) is an essential indicator for crop growth monitoring and yield prediction. Real-time, non-destructive, and accurate monitoring of crop LAI is of great significance for intelligent decision-making on crop fertilization, irrigation, as well as for predicting and warning grain productivity. This study aims to investigate the feasibility of using spectral and texture features from unmanned aerial vehicle (UAV) multispectral imagery combined with machine learning modeling methods to achieve maize LAI estimation. In this study, remote sensing monitoring of maize LAI was carried out based on a UAV high-throughput phenotyping platform using different varieties of maize as the research target. Firstly, the spectral parameters and texture features were extracted from the UAV multispectral images, and the Normalized Difference Texture Index (NDTI), Difference Texture Index (DTI) and Ratio Texture Index (RTI) were constructed by linear calculation of texture features. Then, the correlation between LAI and spectral parameters, texture features and texture indices were analyzed, and the image features with strong correlation were screened out. Finally, combined with machine learning method, LAI estimation models of different types of input variables were constructed, and the effect of image features combination on LAI estimation was evaluated. The results revealed that the vegetation indices based on the red (650 nm), red-edge (705 nm) and NIR (842 nm) bands had high correlation coefficients with LAI. The correlation between the linearly transformed texture features and LAI was significantly improved. Besides, machine learning models combining spectral and texture features have the best performance. Support Vector Machine (SVM) models of vegetation and texture indices are the best in terms of fit, stability and estimation accuracy (R2 = 0.813, RMSE = 0.297, RPD = 2.084). The results of this study were conducive to improving the efficiency of maize variety selection and provide some reference for UAV high-throughput phenotyping technology for fine crop management at the field plot scale. The results give evidence of the breeding efficiency of maize varieties and provide a certain reference for UAV high-throughput phenotypic technology in crop management at the field scale.
RESUMO
During atmospheric precipitation, the evaporation of raindrops falling from the bottom of cloud layer to the ground and passing through unsaturated air, a process was called sub-cloud secondary evaporation, which will change the isotopic composition of precipitation. Using the hydrogen and oxygen stable isotope method to understand the temporal and spatial variation of secondary evaporation effect under clouds and its causes is important to understand regional water cycle process. Based on hourly meteorological data of 187 meteorological stations in Shaanxi-Gansu-Ningxia region from March 2018 to February 2019, the spatial and temporal variations of evaporation surplus ratio (f) and precipitation excess deuterium variation (Δd) were analyzed using the improved Ste-wart model, and the relationships between f and meteorological elements and Δd were examined. The results showed that, at the hourly scale, the minimum values of f and Δd in all provinces of the region appeared in the daytime, and the maximum values appeared in the night, indicating that the secondary evaporation effect under the cloud was more obvious in the daytime. At the monthly scale, the monthly variation trend of f and Δd in each province was relatively consistent, with the minimum value appearing in the summer half year, and the maximum value appearing in the winter half year, indicating that the second evaporation effect under cloud was more significant in the summer half year. From the spatial perspective, the spatial variation of f and Δd values in the region was consistent with that at the seasonal scale. In spring, the eastern and western regions were larger while the central part was smaller. In summer, the northwest region was smaller, and other regions were larger. In autumn, it decreased from south to north. In winter, the central and southern regions were smaller, and the western and northeast regions were larger. The spatial differences of secondary evaporation effects under clouds in different seasons was significant. The slopes of the linear relationship between f and Δd in Shaanxi, Gansu and Ningxia provinces were all less than 1·%-1, which may be caused by the arid and semi-arid climate in this area. When air temperature was higher and the relative humidity, vapor pressure, precipitation and raindrop diameter were smaller, the value of Δd was smaller, and the secondary evaporation effect under the cloud was more obvious.
Assuntos
Monitoramento Ambiental , Chuva , China , Isótopos de Oxigênio/análise , Estações do AnoRESUMO
As plant species for riparian ecological restoration in northern China, Tamarix ramosissima and Salix matsudana play an important role in river protection, flood control, regional climate regulation, and landscape construction of riparian vegetation. Two sampling sites were selected in the riparian zones along the Lanzhou section of Yellow River, where plant xylems and potential water sources were collected. The direct comparison method, Bayesian mixture model MixSIAR and the proportional similarity index (PS index) were used to determine the proportions of water utilization for each potential water source and the relationship of two species in water utilization. The results showed that shallow soil (0-30 cm) was the main water source during growing season, with utilization ratio being 28.3% for T. ramosissima and 24.4% for S. matsudana. For T. ramosissima, river water had the lowest contribution (16.6%), and for S. matsudana, groundwater contributed the least (17.9%). In the months with low soil moisture, plants increased the utilization ratios of river water and groundwater. The PS index at the sampling site S1 and S2 was 91.0% and 87.7%, respectively. On a monthly basis, the index in May was the highest, indicating an inter-month divergence in water use relationship. At the floodplain, there were even utilization ratios for each potential water source, which is an optimal strategy to obtain water from each potential source to the maximum extent. Our results provided theoretical basis for riparian tourism development along the Lanzhou section of the Yellow River and plant water management in environment protection in the Yellow River Basin.