Efficient Plant Disease and Pest Recognition Methods with Deep Learning

This thesis presents efficient deep-learning methods for plant disease and pest recognition. It proposes a unique deep metric learning-based architecture for disease recognition and a novel plant family-based approach for disease characterization. Additionally, it explores attention-based lightweight architectures for effective pest identification. These approaches enhance the accuracy and efficiency of plant disease and pest recognition tasks.