A5100687954- Remote Sensing in Agriculture
- Remote Sensing and Land Use
- Remote Sensing and LiDAR Applications
- Advanced Neural Network Applications
- Video Surveillance and Tracking Methods
- Advanced Image and Video Retrieval Techniques
- Remote-Sensing Image Classification
- Autonomous Vehicle Technology and Safety
- Land Use and Ecosystem Services
- Robotics and Sensor-Based Localization
- Plant Surface Properties and Treatments
- Leaf Properties and Growth Measurement
- Smart Agriculture and AI
- Astronomy and Astrophysical Research
- Crystallography and molecular interactions
- IoT and Edge/Fog Computing
- Cloud Computing and Resource Management
- Image Retrieval and Classification Techniques
- X-ray Diffraction in Crystallography
- Environmental Changes in China
- Infrared Target Detection Methodologies
- Distributed and Parallel Computing Systems
- Advanced Combustion Engine Technologies
- Advanced Vision and Imaging
- Vehicle License Plate Recognition
Nanjing Institute of Agricultural Mechanization
2013-2025
Ministry of Agriculture and Rural Affairs
2020-2025
Chinese Academy of Agricultural Sciences
2023-2025
Tsinghua University
2021-2024
Changzhou University
2023-2024
Wuhan University
2014-2024
Jingdong (China)
2023-2024
Nanjing Medical University
2022-2024
Hubei Academy of Agricultural Sciences
2011-2024
National University of Defense Technology
2022-2024
SPHEREx (Spectro-Photometer for the History of Universe, Epoch Reionization, and Ices Explorer) ( http://spherex.caltech.edu ) is a proposed all-sky spectroscopic survey satellite designed to address all three science goals in NASA's Astrophysics Division: probe origin destiny our Universe; explore whether planets around other stars could harbor life; evolution galaxies. will scan series Linear Variable Filters systematically across entire sky. The data set contain R=40 spectra fir...
The accurate assessment of rice yield is crucially important for China's food security and sustainable development. Remote sensing (RS), as an emerging technology, expected to be useful estimation especially at regional scales. With the development unmanned aerial vehicles (UAVs), a novel approach RS has been provided, it possible acquire high spatio-temporal resolution imagery on scale. Previous reports have shown that predictive ability vegetation index (VI) decreased under influence...
The accurate estimation of rice LAI is particularly important to monitor growth status. Remote sensing, as a non-destructive measurement technology, has been proved be useful for estimating vegetation parameters, especially at large scale. With the development unmanned aerial vehicles (UAVs), this novel remote sensing platform widely used provide images which have much higher spatial resolution. Previous reports shown that spectral feature could an effective indicator estimate parameters....
The accurate quantification of yield in rapeseed is important for evaluating the supply vegetable oil, especially at regional scales.This study developed an approach to estimate with remotely sensed canopy spectra and abundance data by spectral mixture analysis. A six-band image studied plots was obtained unmanned aerial vehicle (UAV) system during flowering stage. Several widely used vegetation indices (VIs) were calculated from reflectance derived UAV image. And plot-level flower, leaf...
Accurate estimation of above ground biomass (AGB) is very important for crop growth monitoring. The objective this study was to estimate rice by utilizing structural and meteorological features with widely used spectral features. Structural were derived from the triangulated irregular network (TIN), which directly built structure motion (SfM) point clouds. Growing degree days (GDD) as feature. Three models AGB, including simple linear regression (SLR) model, exponential (SER) machine...
Abstract Background Rice is one of the most important grain crops worldwide. The accurate and dynamic monitoring Leaf Area Index (LAI) provides information to evaluate rice growth production. Methods This study explores a simple method remotely estimate LAI with Unmanned Aerial Vehicle (UAV) imaging for variety cultivars throughout entire growing season. Forty eight different were planted in site field campaigns conducted once week. For each campaign, several widely used vegetation indices...
This study aimed to address the problems of low detection accuracy and inaccurate positioning small-object in remote sensing images. An improved architecture based on Swin Transformer YOLOv5 is proposed. First, Complete-IOU (CIOU) was introduced improve K-means clustering algorithm, then an anchor appropriate size for dataset generated. Second, a modified CSPDarknet53 structure combined with proposed retain sufficient global context information extract more differentiated features through...
This study developed an approach for remote estimation of Vegetation Fraction (VF) and Flower (FF) in oilseed rape, which is a crop species with conspicuous flowers during reproduction. Canopy reflectance green, red, red edge NIR bands was obtained by camera system mounted on unmanned aerial vehicle (UAV) when rape the vegetative growth flowering stage. The relationship several widely-used Indices (VI) vs. VF tested found to be different phenology stages. At same flowering, canopy increased...
Leaf area index (LAI) estimation is very important, and not only for canopy structure analysis yield prediction. The unmanned aerial vehicle (UAV) serves as a promising solution LAI due to its great applicability flexibility. At present, vegetation (VI) still the most widely used method in because of fast speed simple calculation. However, VI reflects spectral information ignores texture images, so it difficult adapt unique complex morphological changes rice different growth stages. In this...
Estimating the crop leaf area index (LAI) accurately is very critical in agricultural remote sensing, especially monitoring growth and yield prediction. The development of unmanned aerial vehicles (UAVs) has been significant recent years extensively applied sensing (RS). vegetation (VI), which reflects spectral information, a commonly used RS method for estimating LAI. Texture features can reflect differences canopy structure rice at different stages. In this research, was developed to...
The success of ChatGPT has recently attracted numerous efforts to replicate it, with instruction-tuning strategies being a key factor in achieving remarkable results. Instruction-tuning not only significantly enhances the model's performance and generalization but also makes generated results more consistent human speech patterns. However current research rarely studies impact different amounts instruction data on model performance, especially real-world use cases. In this paper we explore...
Multi-modal fusion is a basic task of autonomous driving system perception, which has attracted many scholars' attention in recent years. The current multi-modal methods mainly focus on camera data and LiDAR data, but pay little to the kinematic information provided by sensors vehicle, such as acceleration, vehicle speed, angle rotation. These are not affected complex external scenes, so it more robust reliable. In this article, we introduce existing application fields research progress...
Abstract Plant diseases and pest infections are major factors that undermine the growth of plants along with their life cycle. Optical image-based plant disease detection provides an efficient low cost way for real-time monitoring management. In recent years, thriving development deep learning techniques in a variety communities has validated its great performance image interpretation understanding. Existing learning-based methods classification mostly adopt convolutional neural networks...
The effective and accurate aboveground biomass (AGB) estimation facilitates evaluating crop growth site-specific management. Considering that rice accumulates AGB mainly through green leaf photosynthesis, we proposed the photosynthetic accumulation model (PAM) its simplified version compared them for estimating AGB. These methods estimate of various cultivars throughout growing season by integrating vegetation index (VI) canopy height based on images acquired unmanned aerial vehicles (UAV)....
As a natural language assistant, ChatGPT is capable of performing various tasks, including but not limited to article generation, code completion, and data analysis. Furthermore, has consistently demonstrated remarkable level accuracy reliability in terms content evaluation, exhibiting the capability mimicking human preferences. To further explore ChatGPT's potential this regard, study conducted assess its ability rank content. In order do so, test set consisting prompts created, covering...
Despite some rudimentary handling vehicles employed in the labor-intensive harvesting and transportation of greenhouse vegetables, research on intelligent uncrewed transport remains limited. Herein, an vehicle was designed for solanaceous vegetable harvesting. Its overall structure path planning were tailored to environment, with specially components, including electric crawler chassis, unloading mechanism, control system. A SLAM system based fusion LiDAR inertial navigation ensures precise...
To address the challenges of low efficiency and poor quality in transplantation roots stems Chinese medicinal herbs, an electromechanical control system for herb was studied. The electronic employs STM32 single-chip microcomputer as main controller, utilizes a Hall sensor to capture movement speed transplanter, encoder monitor working DC drum motor provide feedback system, drives belt conveyor transplanter using motor. fuzzy PID algorithm is used adjust real time based on difference between...
Multi-modal sensor fusion techniques have promoted the development of autonomous driving, while perception in complex environment remains a challenging problem. In order to tackle problem, we propose Open Perception dataset (OpenMPD), multi-modal benchmark objected at difficult examples. Compared with existing datasets, OpenMPD focuses more on those traffic scenes urban areas overexposure or darkness, crowded environment, unstructured roads and intersections. It acquires data through vehicle...
Vehicle re-identification is the task of identifying same vehicle in different environments and from angles cameras. It more challenging than humans: 1)small differences between vehicles model make it difficult to capture their subtle characteristics; 2)vehicles types colors may have similar characteristics viewpoints or external conditions. To address these challenges, we propose a TVG-ReID network, using Transformer network enhance features extracted CNN backbone network. A knowledge graph...