Plot-level rapid screening for photosynthetic parameters using proximal hyperspectral imaging.

Area of Science:

• Plant Physiology
• Spectroscopy
• Agricultural Science

Background:

• Traditional photosynthesis measurements are slow and destructive, hindering crop research.
• Identifying crop germplasm with higher photosynthetic capacity is crucial for agricultural advancement.

Purpose of the Study:

• To develop and validate a rapid, plot-level screening tool for quantifying photosynthetic parameters and pigment content.
• To enable high-throughput field phenotyping for improved crop breeding.

Main Methods:

• Utilized hyperspectral reflectance (400-900 nm) from sunlit leaf pixels of field-grown *Nicotiana tabacum*.
• Applied partial least squares regression (PLSR) to build predictive models for photosynthetic traits.
• Collected data over two growing seasons (2017 and 2018) for model validation.

Main Results:

• Successfully predicted key photosynthetic parameters like Vc,max (R2=0.79) and J1800 (R2=0.59).
• Accurately predicted pigment and nutrient content, including chlorophyll (R2=0.87) and nitrogen (R2=0.49).
• A single VNIR camera proved sufficient, indicating a cost-effective approach.

Conclusions:

• The developed hyperspectral imaging pipeline offers a rapid and non-destructive method for assessing photosynthetic performance.
• This high-throughput screening tool can significantly accelerate breeding programs for crops with enhanced photosynthetic efficiency.
• The methodology is adaptable to various cropping systems and species.