Since the discovery of Raman on the interaction between light and material, and the invention of spectrophotometers by Beckman, the reliance on spectroscopy for sensor development has expanded in different scientific sectors. Inventions such as laser and developments in digital imaging technology have been also crucial to expand spectroscopy-based sensing technology; usually called wavelength sensors. Successful applications of spectroscopy sensing depend on the ability of the material in question to absorb, transmit, and reflect light. Light reflected from solid surfaces are used to create spectral signatures that distinguish one surface from another. In agriculture, and on large-scale applications, spectral signatures can be used to classify crop areas based on vegetation using satellite imaging. On smaller scale, spectral signatures are used to classify biological material based on their composition or quality.
This presentation will demonstrate one important application of spectral signatures in understanding the nutrient status in plant leaves, where our case study is applied on potatoes. Nutrients are absorbed from the soil and transported in different directions inside the plants depending on different factors that include the stage of growth. Many nutrients are needed in the leaves, and it has been found that petioles contain representative amount of nutrients during long periods of the potato growth cycle. However, petiole analysis is a destructive method that we can replace by finding spectral signatures in the leaves if we find the correct tools to collect the data and the correct mathematical methods to interpret them. The ability to collect these data in the field under ambient conditions opens doors to quick and on-the-go decision-making in the future.
Speakers
- Ahmad Al-Mallahi Ph.D. - Assistant Professor and the McCains Research Chair at Dalhousie University
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Who should attend?
- Researchers in crop science, agricultural engineering, precision agriculture
- Technology business developers in the fields of machine learning, crop production
What will you learn?
- The importance and advantages of spectral signatures in developing sensors
- A real-life case study of sensing system development based on spectroscopy
- The usage of statistical modeling to interpret data and find useful information