A5100648568- Remote Sensing in Agriculture
- Remote Sensing and Land Use
- Smart Agriculture and AI
- Spectroscopy and Chemometric Analyses
- Remote Sensing and LiDAR Applications
- Leaf Properties and Growth Measurement
- Advanced Computational Techniques and Applications
- Advanced Algorithms and Applications
- Soil Geostatistics and Mapping
- Insect and Arachnid Ecology and Behavior
- Environmental and Agricultural Sciences
- Date Palm Research Studies
- Land Use and Ecosystem Services
- Rough Sets and Fuzzy Logic
- Plant Virus Research Studies
- Plant Pathogens and Fungal Diseases
- Regional Economic and Spatial Analysis
- AI in cancer detection
- Insect-Plant Interactions and Control
- Water Quality Monitoring and Analysis
- Environmental Quality and Pollution
- Insect Pest Control Strategies
- Mycotoxins in Agriculture and Food
- Radiomics and Machine Learning in Medical Imaging
- Fibroblast Growth Factor Research
Henan Agricultural University
2013-2025
Anhui University
2020-2022
Hebei Agricultural University
2019
Shandong University
2015
People 's Hospital of Jilin Province
2014
Institute of Plant Protection
2008-2010
Chinese Academy of Sciences
2009
Beijing Jiaotong University
2008
Jilin Normal University
2005
Abstract Background To accurately estimate winter wheat leaf area index (LAI) using unmanned aerial vehicle (UAV) hyperspectral imagery is crucial for crop growth monitoring, fertilization management, and development of precision agriculture. Methods The UAV imaging data, Analytical Spectral Devices (ASD) LAI were simultaneously obtained at main stages (jointing stage, booting filling stage) various varieties under nitrogen fertilizer treatments. characteristic bands related to extracted...
Accurate and timely crop yield estimation is critical for food security sustainable development. The rapid development of unmanned aerial vehicles (UAVs) offers a new approach to acquire high spatio-temporal resolution imagery farmland at low cost. In order realize the full potential UAV platform sensor, machine learning has been introduced estimate yield, but shortages field measurements have troubled researchers. this article, CW-RF model, wheat model suitable North China plain, was...
Abstract Background Wheat yield is influenced by the number of ears per unit area, and manual counting has traditionally been used to estimate wheat yield. To realize rapid accurate ear counting, K-means clustering was for automatic segmentation images captured hand-held devices. The segmented data set constructed creating four categories image labels: non-wheat ear, one two ears, three which then sent into convolution neural network (CNN) model training testing reduce complexity model....
Abstract Background Fractional vegetation cover (FVC) is an important parameter for evaluating crop-growth status. Optical remote-sensing techniques combined with the pixel dichotomy model (PDM) are widely used to estimate cropland FVC medium high spatial resolution on ground. However, PDM-based estimation limited by effects stemming from variation of crop canopy chlorophyll content (CCC). To overcome this difficulty, we propose herein a “fan-shaped method” (FSM) that uses CCC spectral index...
Grain count is crucial to wheat yield composition and estimating parameters. However, traditional manual counting methods are time-consuming labor-intensive. This study developed an advanced deep learning technique for the segmentation model of grains. has been rigorously tested on three distinct varieties: 'Bainong 307', 'Xinmai 26', 'Jimai 336', it achieved unprecedented predictive accuracy.The images ears were taken with a smartphone at late stage grain filling. We used image processing...
Maize is a globally important cereal crop, however, maize leaf disease one of the most common and devastating diseases that afflict it. Artificial intelligence methods face challenges in identifying classifying due to variations image quality, similarity among diseases, severity, limited dataset availability, interpretability. To address these challenges, we propose residual-based multi-scale network (MResNet) for multi-type from images. MResNet consists two residual subnets with different...
Crop leaf chlorophyll content (LCC) and fractional vegetation cover (FVC) are crucial indicators for assessing crop health, growth development, maturity. In contrast to the traditional manual collection of trait parameters, unmanned aerial vehicle (UAV) technology rapidly generates LCC FVC maps breeding materials, facilitating prompt assessments maturity information. This study addresses following research questions: (1) Can image features based on pretrained deep learning networks ensemble...
Wheat is one of the most important staple crops globally. Timely mapping and monitoring wheat harvests are essential for efficiently scheduling large-scale harvesters, ensuring timely completion harvest, maintaining grain quality. Traditional manual survey methods obtaining harvest information neither highly accurate nor cost-effective do not meet needs agricultural management departments. This study introduces two novel indices detection: optical-band brightness index (OBHI) visible-band...
The incidence of breast cancer is increasing rapidly around the world. Accurate classification subtype from hematoxylin and eosin images key to improve precision treatment. However, high consistency disease subtypes uneven distribution cells seriously affect performance multi-classification methods. Furthermore, it difficult apply existing methods multiple datasets. In this article, we propose a collaborative transfer network (CTransNet) for histopathological images. CTransNet consists...
Cotton aphids (Aphis gossypii Glover) pose a significant threat to cotton growth, exerting detrimental effects on both yield and quality. Conventional methods for pest disease surveillance in agricultural settings suffer from lack of real-time capability. The use edge computing devices processing aphid-damaged leaves captured by field cameras holds practical research value large-scale control measures. mainstream detection models are generally large size, making it challenging achieve with...
Fusarium head blight (FHB) is a major disease threatening worldwide wheat production. FHB short cycle and highly destructive under conducive environments. To provide technical support for the rapid detection of disease, we proposed to develop new index (FDI) based on spectral data 374–1050 nm. This study was conducted through analysis reflectance healthy diseased ears at flowering filling stages by hyperspectral imaging technology random forest method. The characteristic wavelengths selected...
Timely and accurate monitoring of fractional vegetation cover (FVC), leaf chlorophyll content (LCC), maturity breeding material are essential for companies. This study aimed to estimate LCC FVC on the basis remote sensing monitor distribution. We collected UAV-RGB images at key growth stages soybean, namely, podding (P1), early bulge (P2), peak (P3), (P4) stages. Firstly, based above multi-period data, four regression techniques, partial least squares (PLSR), multiple stepwise (MSR), random...
Osteoarthritis (OA) is a progressive and chronic disease. Identifying the early stages of OA disease important for treatment care patients. However, most state-of-the-art methods only use single-modal data to predict status, so that these usually ignore complementary information in multi-modal data. In this study, we develop an integrated learning method (MMLM) uses interpretable strategy select fuse clinical, imaging, demographic features classify grade early-stage knee MMLM applies XGboost...
Maize is a globally important cereal and fodder crop. Accurate monitoring of maize planting densities vital for informed decision-making by agricultural managers. Compared to traditional manual methods collecting crop trait parameters, approaches using unmanned aerial vehicle (UAV) remote sensing can enhance the efficiency, minimize personnel costs biases, and, more importantly, rapidly provide density maps fields. This study involved following steps: (1) Two UAV sensing-based were developed...
Aphis gossypii Glover is a major insect pest in cotton production, which can cause yield reduction severe cases. In this paper, we proposed the A. infestation monitoring method, identifies level of at seedling stage, and improve efficiency early warning forecasting gossypii, achieve precise prevention cure according to predicted level. We used smartphones collect images compiled an image data set. And then constructed, trained, tested three different recognition models based on Faster...
In response to the issues of high complexity and low efficiency associated with current reliance on manual sampling instrumental measurement for obtaining maize leaf area index (LAI), this study constructed a image dataset comprising 624 images from three growth stages summer in Henan region, namely jointing stage, small trumpet large stage. Furthermore, LAI estimation model named LAINet, based an improved convolutional neural network (CNN), was proposed. carried out at these key stages....