Technology Details
Technology Background & Future Potential
Our research found that the mechanism of plant defense system against biotic stress has impacts on a spectral behavior of plant leaves and satellite remote sensing technologies are a useful tool to detect the spectral behavior changes from a biotic stress.
Satellite remote sensing technology has many potential opportunities over other remote sensing tools with offering the cost-effective extraction of long-term series of consistent and comparable data. As a result of recent advances in technology, satellite imagery has been improved in both spatial and temporal resolution, which will make it a more important remote sensing tool in the future. However, there are also some current technical issues, which are related to poor resolution and high cost obtaining high quality data. The current satellite imagery can only indicate the problem canopy area without detailed information about the cause of the problem. To solve the problems, a definite relationship should be established between the ground observed physiology-based plant health data (water availability, nutrition status, and canopy development) and the satellite plant canopy phenology data.
Our service of analyzing satellite remote sensing data with the help of other physiology-based plant health data include chlorophyll fluorescence spectrometry will offer great opportunities to detect spectral signatures of early stress symptom in crop plants. Further, our data will be applied on scalable solutions using big data and predictive analytics to address farm-related issues and make better farm-related decisions in order to save energy, increase efficiency, optimize herbicide and pesticide application. We develop a spectral library for agricultural sectors using satellite imaging service, which creates opportunity of new and ambitious applications for data based digital farm management. Our technology will help growers to develop precision agriculture practice such as soil and water stress observation, site-specific nutrition management, and supporting integrated pest management with early detection of crop diseases.
Remote Sensing
Remote Sensing
A non-contact technique for obtaining information on an object, by measuring the electromagnetic energy reflected by the surface of substances such as plant and soil
Multispectral Sensors
Sensors measure surface reflectance across space for two or more wide bands of wavelengths, which then serve as a base for calculating VIs such as the NDVI
Hyperspectral sensors
Sensors include high-resolution optical techniques with an increased spectral resolution. They provide data with high complexity, covering a spectral range of up to 350 to 2,500 nm and a possible narrow spectral resolution below 1 nm
Hyperspectral vs. Multispectral
The multispectral imagery can only indicate the problem canopy area without detailed information about cause of the problem. While hyperspectral sensors with advanced narrow bend spectral imagery have shown an enormous potential to provide new insights into remote sensing of plant stresses and especially, for the detection of plant diseases. Simply, multispectral imagery can only tell forested areas in a map, while hyperspectral imagery can tell specific tree species in the forest.
Chlorophyll Fluorescence Sensors
Chlorophyll fluorescence (Chl-F) is an emitted light energy in photosynthetic tissues upon excitation with natural or artificial light in the red and red edge spectrums to disperse the excessive photosynthesis energy and to protect the chloroplast from oxidative damage.
Chlorophyll is the primary molecules that absorb the light energy and convert the energy for photosynthesis and is also a key indicator of the physiological status of a plant canopy.
Active Chl-F measurements have conventionally been measured upon excitation with artificial lighting systems, generally by lamps or LEDs. However, technical limitations make very difficult the measurement of Chl-F remotely, mainly for the analysis of quenching kinetics. This problem could be solved by the analysis of hyperspectral reflectance indices correlating with Chl-F parameters.
[BBBfly News] Publication about plant virus detection
[ScienceDirect] Smart Agricultural Technology
Volume 8, August 2024,100464