The Phase One P3's medium-format sensor, ultra-high resolution, and long focal length options make it a strong candidate for structural trait analysis in plant phenotyping.
1. Canopy Cover Estimation
The P3’s medium-format sensor captures highly detailed aerial imagery, allowing researchers to accurately measure vegetation cover.
Using image segmentation techniques, scientists can classify different land cover types and estimate the percentage of ground covered by plant material. This is critical for monitoring growth stages, assessing crop competition, and evaluating field uniformity over time.
2. Leaf Area Index (LAI) Calculation
The P3’s sharp resolution and accurate colour reproduction enable detailed imaging of individual leaves. By applying machine learning-based segmentation or traditional image analysis, researchers can measure Leaf Area Index (LAI), which is essential for understanding plant health and photosynthetic efficiency.
3. 3D Plant Structure & Height Mapping (Photogrammetry)
High-resolution imagery from the P3 can be processed using photogrammetry software (e.g. Pheno-Inspect) to create 3D reconstructions of plant canopies. This provides valuable insights into biomass accumulation, plant height variations, and crop uniformity.
3D models can also help assess plant architecture and breeding
4. Lodging Detection (Structural Stability of Plants)
Lodging, the bending or collapsing of plants due to environmental factors, can significantly reduce crop yield. The high spatial resolution of the P3 enables detection of lodged areas and provides researchers with accurate assessments of plant stability. By analyzing structural deformations in crops like wheat, rice, or corn, agronomists can develop more resilient plant varieties.
5. Stress & Disease Identification (Limited to Visible Symptoms)
Although the P3 lacks near-infrared (NIR) capabilities, it can still detect visible plant stress symptoms, such as wilting, discoloration, and necrosis. AI-driven image processing can classify stress patterns in crops based on RGB data. For instance, early detection of nutrient deficiencies and pest damage can be achieved using pattern recognition and colour analysis.
