This is like using flying robots with smart cameras and sensors to regularly scan corn fields from above, then using an AI model to estimate how much silage you’ll harvest and how nutritious it will be—without cutting and testing plants by hand.
Traditional measurement of maize silage yield and nutritive traits requires destructive, labor-intensive field sampling and lab analysis, which is slow, expensive, and limits breeding trials and management decisions. This system uses UAV imagery and multi-sensor data plus a multi-task learning model to rapidly predict yield and feed quality traits across many plots or fields.
Specialized models and feature fusion tuned to maize silage traits, plus proprietary multi-sensor UAV datasets and agronomic know-how can create a defensible edge in prediction accuracy and robustness across environments.
Classical-ML (Scikit/XGBoost)
Unknown
High (Custom Models/Infra)
Collecting and standardizing large, labeled UAV datasets (yields and nutritive values) across seasons and locations; plus UAV flight logistics and regulatory constraints.
Early Adopters
Compared to generic remote-sensing yield models, this work targets maize silage and simultaneously predicts both yield and multiple nutritive traits using multi-task learning with attention and multi-sensor (multi-spectral/structural) feature fusion, improving both accuracy and phenotyping efficiency for forage-focused breeding and management.