Driven by the compounding pressures of climate change, resource scarcity, and global food insecurity, agricultural R&D requires faster, more precise data than traditional methods can provide. Our Morphological and Phenotypic Analysis of Plant Systems service bridges this gap by combining high-throughput plant phenomics (the study of physical and biochemical traits) with automated computer vision and deep learning.
Instead of relying on slow, destructive harvesting or highly subjective manual scoring in fields and greenhouses, our AI-driven service continuously tracks crops from seed to harvest. We extract thousands of micro-level measurements from digital imaging systems—including 3D scanners, drones, and hyperspectral sensors—empowering seed breeders, crop protection companies, and indoor farming innovators to accelerate genetic gains and optimize crop yields.
The computer vision model establishes a coordinate grid over a high-resolution scan of a leaf to perform precise spatial mapping. The AI defines targeted regions of interest (ROIs)—such as the upper lamina and lower margin—and dynamically zooms in to map micro-structural features. It traces secondary and tertiary venation networks, isolates individual veined domains (areoles), and scans the leaf margins to measure serration density and edge morphology. By converting these fine-scale visual traits into quantitative phenotypic data, the platform allows breeders and AgTech companies to screen for drought resilience, nutrient transport efficiency, and genetic purity with absolute mathematical precision.