Nov 5 - Visual AI Agriculture Meetup

Join our virtual meetup to hear talks from experts on cutting-edge topics at the intersection of agriculture and AI. Date, Time and Location Nov 05, 2026 9:00 AM - 11:00 AM PST Online. Register for the Zoom! Talks will include: U-Net Framework for Micro-Scale Surface Damage Segmentation in High-Resolution Soybean Seed Imagery Accurate detection of surface-level seed damage is critical for soybean seed quality assurance, yet automated micro-scale damage detection in high-resolution imagery remains an open challenge due to extreme spatial variability in damage scale, severe class imbalance across damage types, and the computational demands of processing ultra-high resolution agricultural imagery at scale. This research addresses the semantic segmentation of fine-grained soybean seed surface defects, such as wrinkles, dark spots, and general surface damage, in 6048 × 4024 pixel images where target damages can be as small as 18 × 18 pixels, using an augmented dataset of 77,000 individual seed images. To overcome the difficulties of micro-scale detection, spatial sparsity, and class confusion, we propose a two-stage training and dual model inference framework built on an optimized U-Net architecture with a ResNet34 encoder. In the first stage, a damage specialist model is trained using weighted loss functions and a class-balanced approach that prioritizes damage classes. In the second stage, transfer learning is applied to initialize a healthy seed specialist model from Stage 1 weights, with rebalanced class weights and tile probabilities that identify healthy seeds. During inference, a confidence-gated damage filter suppresses low-confidence predictions, and healthy seed labels are assigned only when the specialist model's confidence exceeds that of the damage model. The two specialist models achieve validation accuracies of 94% and 98.53%, respectively, and the combined inference system successfully detects and localizes all three damage categories across unsee