grain-detection
Grain detection refers to computational and imaging techniques used to automatically identify, count, and characterize individual seeds or kernels from images of cereal crop harvests. This capability is essential for plant science because it enables rapid, objective phenotyping of grain traits such as size, shape, and number, which are directly linked to yield potential. By automating what was traditionally a labor-intensive manual process, grain detection accelerates breeding programs and genetic studies aimed at improving crop productivity.
open_in_new WikipediaOpenAlex · 2026-07-10
Researchers built a compact AI model called LGC-Net that can quickly identify defects in five major grain crops using images, achieving nearly 90% accuracy while being small enough to run on low-power devices in the field.
LGC-Net-XXS achieves 89.59% average classification accuracy across five grain types with more than ten defect categories
The model uses only 0.93 million parameters and 0.43 billion floating-point operations, making it suitable for low-power edge devices
A new Fast Channel Additive Attention mechanism reduces computational complexity from quadratic to linear while retaining global pattern recognition
