A5101013372- Remote Sensing in Agriculture
- Remote Sensing and LiDAR Applications
- Leaf Properties and Growth Measurement
- Smart Agriculture and AI
- Spectroscopy and Chemometric Analyses
- Image Retrieval and Classification Techniques
- Meat and Animal Product Quality
- Advanced Neural Network Applications
- Animal Behavior and Welfare Studies
- Effects of Environmental Stressors on Livestock
- Advanced Image and Video Retrieval Techniques
- Plant Pathogens and Fungal Diseases
- Water Quality Monitoring and Analysis
- Powdery Mildew Fungal Diseases
Shanghai Academy of Agricultural Sciences
2019-2025
Ministry of Agriculture and Rural Affairs
2022-2025
Agricultural Information Institute
2019-2022
Shanghai Institute for Science of Science
2019-2022
This study combines hyperspectral imaging technology with biochemical parameter analysis to facilitate the disease severity evaluation and early detection of lettuce downy mildew. The results reveal a significant negative correlation between index (DI) levels flavonoids (r = −0.523) anthocyanins −0.746), indicating role these secondary metabolites in enhancing plant resistance. Analysis data identified that spectral regions (410–503 nm, 510–615 630–690 nm) vegetation indices like PRI ARI2...
Estimation of crop biophysical and biochemical characteristics is the key element for growth monitoring with remote sensing. With application unmanned aerial vehicles (UAV) as a sensing platform worldwide, it has become important to develop general estimation models, which can interpret data crops by different sensors in agroclimatic regions into comprehensible agronomy parameters. Leaf chlorophyll content (LCC), be measured soil plant analysis development (SPAD) value using SPAD-502...
The cold stress is one of the most important factors for affecting production throughout year, so effectively evaluating frost damage great significant to determination tolerance in lettuce. We proposed a high-throughput method estimate lettuce FDI based on remote sensing. Red-Green-Blue (RGB) and multispectral images open-field suffered from were captured by Unmanned Aerial Vehicle platform. Pearson correlation analysis was employed select FDI-sensitive features RGB images. Then models...
Traditional lettuce counting relies heavily on manual labor, which is laborious and time-consuming. In this study, a simple efficient method for localization proposed, based only field images acquired by an unmanned aerial vehicle (UAV) equipped with RGB camera. method, new model the weak supervised deep learning (DL) approach developed, called LettuceNet. The LettuceNet network adopts more lightweight design that point-level labeled to train accurately predict number location information of...
Rice lodging may result in serious yield loss. Manual in-situ assessments of are inefficient and inaccurate. Therefore, this paper explores the potential unmanned aerial vehicle (UAV) image evaluating rice lodging. Multispectral red–green–blue (RGB) cameras mounted on UAV platforms were used to acquire images two paddies Shanghai, China. The features non-lodged lodged rice, including their spectral reflectance, vegetation indices, texture, colour, extracted analysed optimize indicators for...
Abstract At this stage, the domestic animal husbandry industry has a very important support role in market economy, and sheep received extensive attention as one of industries. Limb movements activities can directly reflect adaptability to breeding environment conditions, provide richer scientific technical experience for breeding. The target detection is an prerequisite grasping movement behavior sheep. Therefore, how efficiently accurately identify detect become key development at stage....
Accurate counting of the number rice panicles per unit area is essential for yield estimation. However, intensive planting, complex growth environments, and overlapping leaves in paddy fields pose significant challenges precise panicle detection. In this study, we propose YOLO-Rice, a detection model based on You Only Look Once version 8 nano (YOLOv8n). The employs FasterNet, lightweight backbone network, incorporates two-layer head to improve performance while reducing overall size....
Soil Plant Analysis Development (SPAD) Chlorophyll Meter reading was used to effectively characterize chlorophyll content, which is an important indicator of the health status plant leaves. In this study, hyperspectral images apple leaves infected by mosaic virus (ApMV) were captured, and their SPAD values measured. The spectral reflectance with varying degree infection disease significantly different. particular, in visible wavebands a more serious higher than that less severe infection....