Research on improved YOLOv8n based potato seedling detection in UAV remote sensing images

IntroductionAccurate detection of potato seedlings is crucial for obtaining information on potato seedlings and ultimately increasing potato yield. This study aims to enhance the detection of potato seedlings in drone-captured images through a novel lightweight model.MethodsWe established a dataset of drone-captured images of potato seedlings and proposed the VBGS-YOLOv8n model, an improved version of YOLOv8n. This model employs a lighter VanillaNet as the backbone network in-stead of the original YOLOv8n model. To address the small target features of potato seedlings, we introduced a weighted bidirectional feature pyramid network to replace the path aggregation network, reducing information loss between network layers, facilitating rapid multi-scale feature fusion, and enhancing detection performance. Additionally, we incorporated GSConv and Slim-neck designs at the Neck section to balance accuracy while reducing model complexity. ResultsThe VBGS-YOLOv8n model, with 1,524,943 parameters and 4.2 billion FLOPs, achieves a precision of 97.1%, a mean average precision of 98.4%, and an inference time of 2.0ms. Comparative tests reveal that VBGS-YOLOv8n strikes a balance between detection accuracy, speed, and model efficiency compared to YOLOv8 and other mainstream networks. Specifically, compared to YOLOv8, the model parameters and FLOPs are reduced by 51.7% and 52.8% respectively, while precision and a mean average precision are improved by 1.4% and 0.8% respectively, and the inference time is reduced by 31.0%.DiscussionComparative tests with mainstream models, including YOLOv7, YOLOv5, RetinaNet, and QueryDet, demonstrate that VBGS-YOLOv8n outperforms these models in terms of detection accuracy, speed, and efficiency. The research highlights the effectiveness of VBGS-YOLOv8n in the efficient detection of potato seedlings in drone remote sensing images, providing a valuable reference for subsequent identification and deployment on mobile devices

Lipid transporter LSR1 positively regulates leaf senescence in Arabidopsis

Senescence is the final stage in the life history of a leaf, whereby plants relocate nutrients from leaves to other developing organs. Recent efforts have begun to focus on understanding the network-based molecular mechanism that incorporates various environmental signals and leaf age information and involves a complex process with the coordinated actions of multiple pathways. Here, we identified a novel participant, named LSR1 (Leaf Senescence Related 1), that involved in the regulation of leaf senescence. Loss-of-function lsr1-1 mutant showed delayed leaf senescence whereas the overexpression of LSR1 accelerated senescence. LSR1 encodes a lipid transfer protein, and the results show that the protein is located in chloroplast and intercellular space. The LSR1 may be involved in the regulation of leaf senescence by transporting lipids in plants

Integrating the Eigendecomposition Approach and k-Means Clustering for Inferring Building Functions with Location-Based Social Media Data

Understanding the relationship between human activity patterns and urban spatial structure planning is one of the core research topics in urban planning. Since a building is the basic spatial unit of the urban spatial structure, identifying building function types, according to human activities, is essential but challenging. This study presented a novel approach that integrated the eigendecomposition method and k-means clustering for inferring building function types according to location-based social media data, Tencent User Density (TUD) data. The eigendecomposition approach was used to extract the effective principal components (PCs) to characterize the temporal patterns of human activities at building level. This was combined with k-means clustering for building function identification. The proposed method was applied to the study area of Tianhe district, Guangzhou, one of the largest cities in China. The building inference results were verified through the random sampling of AOI data and street views in Baidu Maps. The accuracy for all building clusters exceeded 83.00%. The results indicated that the eigendecomposition approach is effective for revealing the temporal structure inherent in human activities, and the proposed eigendecomposition-k-means clustering approach is reliable for building function identification based on social media data

he Arabidopsis ORGAN SIZE RELATED 2 is involved in regulation of cell expansion during organ growth

BACKGROUND: In plants, the growth of an aerial organ to its characteristic size relies on the coordination of cell proliferation and expansion. These two different processes occur successively during organ development, with a period of overlap. However, the mechanism underlying the cooperative and coordinative regulation of cell proliferation and expansion during organ growth remains poorly understood. RESULTS: This study characterized a new Arabidopsis ORGAN SIZE RELATED (OSR) gene, OSR2, which participates in the regulation of cell expansion process during organ growth. OSR2 was expressed primarily in tissues or organs undergoing growth by cell expansion, and the ectopic expression of OSR2 resulted in enlarged organs, primarily through enhancement of cell expansion. We further show that OSR2 functions redundantly with ARGOS-LIKE (ARL), another OSR gene that regulates cell expansion in organ growth. Moreover, morphological and cytological analysis of triple and quadruple osr mutants verified that the four OSR members differentially but cooperatively participate in the regulation of cell proliferation and cell expansion and thus the final organ size. CONCLUSIONS: Our results reveal that OSR2 is functional in the regulation of cell expansion during organ growth, which further implicates the involvement of OSR members in the regulation of both cell proliferation and expansion and thus the final organ size. These findings, together with our previous studies, strongly suggest that OSR-mediated organ growth may represent an evolutionary mechanism for the cooperative regulation of cell proliferation and expansion during plant organogenesis. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12870-014-0349-5) contains supplementary material, which is available to authorized users

Spatially varying impacts of built environment factors on rail transit ridership at station level: A case study in Guangzhou, China

Understanding the relationship between the rail transit ridership and the built environment is crucial to promoting transit-oriented development and sustainable urban growth. Geographically weighted regression (GWR) models have previously been employed to reveal the spatial differences in such relationships at the station level. However, few studies characterized the built environment at a fine scale and associated them with rail transit usage. Moreover, none of the existing studies attempted to categorize the stations for policy-making considering varying impacts of the built environment. In this study, taking Guangzhou as an example, we integrated multi-source spatial big data, such as high spatial resolution remote sensing images, points of interest (POIs), social media and building footprint data to precisely quantify the characteristics of the built environment. This was combined with a GWR model to understand how the impacts of the fine-scale built environment factors on the rail transit ridership vary across the study region. The k-means clustering method was employed to identify distinct station groups based on the coefficients of the GWR model at the local stations. Policy zoning was proposed based on the results and differentiated planning guidance was suggested for different zones. These recommendations are expected to help increase rail transit usage, inform rail transit planning (to relieve the traffic burden on currently crowed lines), and re-allocate industrial and living facilities to reduce the commute for the residents. The policy and planning implications are crucial for the coordinated development of the rail transit system and land use

The varying patterns of rail transit ridership and their relationships with fine-scale built environment factors: Big data analytics from Guangzhou

Investigating the varying ridership patterns of rail transit ridership and their influencing factors at the station level is essential for station planning, urban planning, and passenger flow management. Although many studies have investigated the associations between rail transit ridership and built environment, few studies combined spatial big data to characterize the built environment factors at a fine scale and linked those factors with the varying patterns of rail transit ridership. In this study, we characterized the fine-scale built environment factors in the central urban area of Guangzhou, China, by integrating multi-source geospatial big data including Tencent user data, building footprint and stories, points of interest (POI) data and Google Earth high-resolution images. Six direct ridership models (DRMs) based on the backward stepwise regression method were built to compare the different effects between daily, temporal and directional ridership. The results indicated that number of station entrances/exits and transfer dummy, were positively associated with rail transit ridership, while connecting bus station sites and the parking lots were not significantly related to ridership. Population density and common residences land were found to be dominating factors in promoting morning boarding & evening alighting ridership, which implied that these two factors should be focused on to encourage commuting-purpose rail transit usage. However, the indistinct effect of urban villages on rail transit ridership suggested planners to pay more attentions on urban regeneration at the pedestrian catchment areas (PCAs) with urban villages. High employment density and a large FAR were suggested at the employment-oriented areas owing to their importance in promoting rail transit ridership, especially the morning alighting & evening boarding ridership. Moreover, educational research land use significantly affected weekday ridership while sports land use positively influenced weekend ridership, which suggested planners to pay more attention on the non-commuting trips. The different influencing mechanisms of various types of rail transit ridership highlighted the need to consider land use balance planning and trip demand optimization in highly urbanized metropolises in developing countries