ABSTRACT
Electromagnetically induced absorption (EIA) exhibits abnormal dispersion and novel fast-light features, making it a crucial aspect of nanophotonics. Here, the EIA phenomenon is numerically predicted in a compact plasmonic waveguide system by introducing a slot resonator above a square cavity. Simulation results reveal that the EIA response can be easily tuned by altering the structure's parameters, and double EIA valleys can be observed with an additional slot resonator. Furthermore, the investigated structures demonstrate a fast-light effect with an optical delay of â¼ -1.0 ps as a result of aberrant dispersion at the EIA valley, which enable promising applications in the on-chip fast-light area. Finally, a plasmonic nanosensor with a sensitivity of â¼1200â nm/RIU and figure of merit of â¼16600 is achieved based on Fano resonance. The special features of our suggested structure are applicable in realization of various integrated components for the development of multifunctional high-performance nano-photonic devices.
ABSTRACT
The normalized difference vegetation index (NDVI) is widely used in remote sensing to monitor plant growth and chlorophyll levels. Usually, a multispectral camera (MSC) or hyperspectral camera (HSC) is required to obtain the near-infrared (NIR) and red bands for calculating NDVI. However, these cameras are expensive, heavy, difficult to geo-reference, and require professional training in imaging and data processing. On the other hand, the RGBN camera (NIR sensitive RGB camera, simply modified from standard RGB cameras by removing the NIR rejection filter) have also been explored to measure NDVI, but the results did not exactly match the NDVI from the MSC or HSC solutions. This study demonstrates an improved NDVI estimation method with an RGBN camera-based imaging system (Ncam) and machine learning algorithms. The Ncam consisted of an RGBN camera, a filter, and a microcontroller with a total cost of only $70 ~ 85. This new NDVI estimation solution was compared with a high-end hyperspectral camera in an experiment with corn plants under different nitrogen and water treatments. The results showed that the Ncam with two-band-pass filter achieved high performance (R2 = 0.96, RMSE = 0.0079) at estimating NDVI with the machine learning model. Additional tests showed that besides NDVI, this low-cost Ncam was also capable of predicting corn plant nitrogen contents precisely. Thus, Ncam is a potential option for MSC and HSC in plant phenotyping projects.
Subject(s)
Machine Learning , Plant Leaves , Zea mays , Algorithms , ChlorophyllABSTRACT
Portable devices for measuring plant physiological features with their isolated measuring chamber are playing an increasingly important role in plant phenotyping. However, currently available commercial devices of this type, such as soil plant analysis development (SPAD) meter and spectrometer, are dot meters that only measure a small region of the leaf, which does not perfectly represent the highly varied leaf surface. This study developed a portable and high-resolution multispectral imager (named LeafScope) to in-vivo image a whole leaf of dicotyledon plants while blocking the ambient light. The hardware system is comprised of a monochrome camera, an imaging chamber, a lightbox with different bands of light-emitting diodes (LEDs) array, and a microcontroller. During measuring, the device presses the leaf to lay it flat in the imaging chamber and acquires multiple images while alternating the LED bands within seconds in a certain order. The results of an experiment with soybean plants clearly showed the effect of nitrogen and water treatments as well as the genotype differences by the color and morphological features from image processing. We conclude that the low cost and easy to use LeafScope can provide promising imaging quality for dicotyledon plants, so it has great potential to be used in plant phenotyping.
