Features
Dedicated for the M350 RTK/M400, SIF Imaging Detection, 8-Band DJI SIF Imaging Multispectral Yusense FD500 Pro
This is the industry’s first frame-type SIF multispectral camera, integrating five 1.3M-pixel multispectral channels with one main SIF channel and two auxiliary SIF channels, enabling dual remote sensing and video detection modes. The SIF channel effectively addresses the industry challenges of low spatial resolution for non-imaging SIF detection and high cost for imaging hyperspectral SIF detection, making drone-mounted SIF imaging detection at the plant and leaf level possible.
- 1 SIF main channel + 2 SIF auxiliary channels + 5 multi-spectrum
- Synchronous detection of downlink radiation, with reflectivity measurement error less than ±5%
- Ultra-narrowband high-precision imaging, SIF main channel central wavelength 760.8nm±0.3nm, FWHM≤1.5nm
- SIF main channel (width 77m×58m@h=120m, GSD=9.60cm@h=120m)
- DJI M350 RTK/M400 drone customization, plug and play
It can meet the needs of scientific research applications in the field of botany, such as gene phenotyping, pathological research, breeding and resistance selection, growth process simulation, and industrial applications such as vegetation drought stress, greening growth monitoring, and agricultural and forestry disaster warning.
Typical application Multispectral Yusense FD500 Pro
Vegetation Phenotyping Research
Solar-induced chlorophyll fluorescence (SIF) multispectral technology captures the weak fluorescence signals emitted by plant photosynthesis, providing a non-invasive monitoring method for plant phenotyping. This technology can quantify crop photosynthetic efficiency, water stress, and nitrogen status, significantly improving the efficiency of phenomic analysis in stress tolerance breeding and precision agriculture.


Crop Growth Monitoring
Solar-induced chlorophyll fluorescence (SIF) multispectral technology enables real-time, non-destructive monitoring of crop growth by detecting the fluorescence signals released by crop photosynthesis. This technology accurately quantifies photosynthetic efficiency, water stress, and nutrient status, effectively avoiding errors caused by spectral signal saturation. Its narrow bandwidth suppresses imaging noise, providing critical data support for precision agriculture and significantly improving the timeliness and accuracy of field management.
Forestry Growth Survey
By capturing abnormal fluorescence signal changes in the photosynthetic system of stressed trees, early identification of changes in plant respiration levels caused by pests and diseases can be achieved. Its characteristic wavelengths can effectively distinguish between pest and disease stress and natural aging, providing a quantitative basis for targeted prevention and control, significantly improving the efficiency of forest health monitoring.
