Meta-baseline based on deep neuro-fuzzy network for few-shot plant leaf fungal diseases recognition

Abstract

ABSTRACT The emergence and spread of plant diseases can reduce yield. These diseases mainly result from fungi, viruses, and bacteria, of which fungi are the cause of most plant diseases. Deep learning is widely used to identify plant leaf fungal diseases but fails to process fuzzy information alone. A deep neuro-fuzzy network introduces fuzzy sets and fuzzy reasoning rules into deep learning for handling this information. However, since the deep neuro-fuzzy network has limitations, relying on the sufficient training set and requiring retraining when a new task appears, it isn't suitable for few-shot task. While the meta-baseline with the deep neuro-fuzzy network as backbone is an excellent choice, as it combines a deep neuro-fuzzy network and meta-learning to learn from a very few samples and generalize to numerous new samples. Experimental results demonstrate the advantages of the model. This model is an effective method for recognizing few-shot plant leaf fungal diseases.