A5102026163- Remote Sensing in Agriculture
- Smart Agriculture and AI
- Spectroscopy and Chemometric Analyses
- Leaf Properties and Growth Measurement
- Remote Sensing and Land Use
- Remote Sensing and LiDAR Applications
- Rice Cultivation and Yield Improvement
- Land Use and Ecosystem Services
- Optical Systems and Laser Technology
- Plant Water Relations and Carbon Dynamics
- Infrared Target Detection Methodologies
- Calibration and Measurement Techniques
- Crop Yield and Soil Fertility
- Environmental and Agricultural Sciences
- Plant responses to water stress
- GABA and Rice Research
- Environmental Changes in China
- Plant responses to elevated CO2
- Nitrogen and Sulfur Effects on Brassica
- Plant nutrient uptake and metabolism
- Optical Polarization and Ellipsometry
- Carbon and Quantum Dots Applications
- Advanced Fluorescence Microscopy Techniques
- Plant tissue culture and regeneration
- Simulation and Modeling Applications
Yangzhou University
2016-2025
Aerospace Information Research Institute
2023-2024
Chinese Academy of Sciences
2024
Jiangsu Academy of Agricultural Sciences
2021-2024
Ministry of Agriculture and Rural Affairs
2021-2023
Shenzhen Bay Laboratory
2022
Beihang University
2009-2021
Huazhong Agricultural University
2013-2021
Academy of Opto-Electronics
2014-2019
Henan University of Urban Construction
2018
Accurately monitoring the crop water conditions (CWC) is vital for agricultural management. Traditional in situ measurements are limited by inefficiency and lack of spatial information. However, development unmanned aerial vehicle (UAV) applications agriculture now provides a high throughput cost-effective method to obtain field growth Unfortunately, current UAV-based drought indices do not capture time series information, or accuracy limited. This study uses multispectral thermal...
Point cloud segmentation is necessary for obtaining highly precise morphological traits in plant phenotyping. Although a huge development has occurred point segmentation, the of clouds from complex leaves still remains challenging. Rapeseed are critical cultivation and breeding, yet traditional two-dimensional imaging susceptible to reduced accuracy due occlusions between plants. The current study proposes use binocular stereo-vision technology obtain three-dimensional (3D) rapeseed at...
Developing accurate, non-destructive, and automated methods for monitoring the phenotypic traits of rapeseed is crucial improving yield quality in modern agriculture. We used a line laser binocular stereo vision technology system to obtain three-dimensional (3D) point cloud data different varieties (namely Qinyou 7, Zheyouza 108, Huyou 039) at seedling stage, were extracted from those clouds. After pre-processing clouds with denoising segmentation, plant height, leaf length, width, area...
Abstract Background The number grain per panicle of rice is an important phenotypic trait and a significant index for variety screening cultivation management. methods that are currently used to count the grains manually conducted, making them labor intensive time consuming. Existing image-based counting had difficulty in separating overlapped grains. Results In this study, we aimed develop image analysis-based method quickly quantify panicle. We compared accuracy several among different...
Grain number is crucial for analysis of yield components and assessment effects cultivation measures. The grain per spike thousand-grain weight can be measured by counting grains manually, but it time-consuming, tedious error-prone. Previous image processing algorithms cannot work well with different backgrounds sizes. This study used deep learning methods to resolve the limitations traditional algorithms. Wheat datasets were collected in scenarios three varieties, six background two...
A rapid growth in world population is putting a pressure on agricultural products due to their worldwide demand. Contrastively, activities developed during green revolution are becoming unsustainable current climatic change and surging population. Therefore, innovative techniques the need of time combat rising food Nanotechnology, that offering more sustainable resilient system with improvement security, an important driver bring agri-tech revolution. Here, we explored opportunities...
Abstract Estimating wheat yield accurately is crucial for efficient agricultural management. While canopy spectral information widely used this purpose, the incorporation of volumetric features (CVFs) remains underexplored. This study bridges gap by utilizing unmanned aerial vehicle (UAV) multispectral imaging to capture images and elevation data at key developmental stages—gestation flowering stages. We innovatively leveraged differences between these stages calculate height, develop a...
Effective estimation of crop yields at a regional scale holds significant importance in facilitating decision-making within the agricultural sector, thereby ensuring grain security. However, traditional ground-based measurement techniques suffer from inefficiencies, and there exists need for reliable, precise, effective method estimating rice yields. In this study, we employed four machine-learning techniques: partial least squares regression (PLSR), support vector (SVR), random forest...
Abstract Thermally activated delay fluorescence (TADF) has great potential for information encryption, temperature detection, and bioimaging due to its long‐lived luminescence, temperature‐sensitive high signal‐to‐noise ratio. However, it is still a challenge establish TADF in aqueous environments. In this study, the composite with (M‐FNCDs) prepared using fluorine‐nitrogen co‐doped carbon dots (FNCDs) melamine. It worth mentioning that M‐FNCDs show stable under long‐wavelength excitation...
The number of maize seedlings is a key determinant yield. Thus, timely, accurate estimation helps optimize and adjust field management measures. Differentiating “multiple in single hole” accurately using deep learning object detection methods presents challenges that hinder effectiveness. Multivariate regression techniques prove more suitable such cases, yet the presence weeds considerably affects accuracy. Therefore, this paper proposes weed identification method combines shape features...
Real-time monitoring of rice-wheat rotation areas is crucial for improving agricultural productivity and ensuring the overall yield rice wheat. However, current methods mainly rely on manual recording observation, leading to low efficiency. This study addresses challenges progress time-consuming labor-intensive nature process. By integrating Unmanned aerial vehicle (UAV) image analysis technology deep learning techniques, we proposed a method precise in areas. The was initially used extract...
Fluorescence imaging is a key tool for visualizing the morphology and dynamics of nucleic acids (DNA RNA) in living cells to understand their role regulating growth, development, reproduction organisms. However, effective probes capable simultaneously targeting both DNA RNA, as well tools analyzing distribution relative ratios organisms, are currently lacking. Therefore, fluorine-nitrogen codoped carbon dots with two-photon absorption (F-NCDs) were synthesized by hydrothermal method...
The morphology of wheat leaves is a key indicator crop stand quality and photosynthetic capacity, with sowing date being critical factor influencing leaf morphology. To investigate the effects time on growth, development, phenotypes, this study utilized image analysis technology to systematically extract phenotypic traits winter leaves, including effective area, color, shape. results demonstrated that delayed significantly affected color characteristics leaves. Specifically, length width...