Causality-inspired Framework Boosts Crop Pest Recognition

A team of researchers has developed an innovative Decoupled Feature Learning (DFL) framework to address distribution bias in crop pest recognition. The framework uses causal inference techniques and the Center Triplet Loss to improve the accuracy of deep learning models. Results showed significant improvements in recognition accuracy on three different datasets.