Unsupervised pre-training enhances deep learning model for crop stress detection

Overview

Our proposed unsupervised pre-training strategy mitigated the requirement of intensive training data for deep learning models. Leveraging unlabeled UAV-collected RGB images, this approach significantly enhanced performance in rating the severity of soybean iron deficiency chlorosis, surpassing models without pre-training.

Publications

Li, J., Oswald, C., Graef, G. L., & Shi, Y. (2020). Improving model robustness for soybean iron deficiency chlorosis rating by unsupervised pre-training on unmanned aircraft system derived images. Computers and electronics in agriculture, 175, 105557. paper link