Prune-FSL: Pruning-Based Lightweight Few-Shot Learning for Plant Disease Identification

Abstract

The high performance of deep learning networks relies on large datasets and powerful computational resources. However, collecting enough diseased training samples is a daunting challenge. In addition, existing few-shot learning models tend to suffer from large size, which makes their deployment on edge devices difficult. To address these issues, this study proposes a pruning-based lightweight few-shot learning (Prune-FSL) approach, which aims to utilize a very small number of labeled samples to identify unknown classes of crop diseases and achieve lightweighting of the model. First, the disease few-shot learning model was built through a metric-based meta-learning framework to address the problem of sample scarcity. Second, a slimming pruning method was used to trim the network channels by the γ coefficients of the BN layer to achieve efficient network compression. Finally, a meta-learning pruning strategy was designed to enhance the generalization ability of the model. The experimental results show that with 80% parameter reduction, the Prune-FSL method reduces the Macs computation from 3.52 G to 0.14 G, and the model achieved an accuracy of 77.97% and 90.70% in 5-way 1-shot and 5-way 5-shot, respectively. The performance of the pruned model was also compared with other representative lightweight models, yielding a result that outperforms those of five mainstream lightweight networks, such as Shufflenet. It also achieves 18-year model performance with one-fifth the number of parameters. In addition, this study demonstrated that pruning after sparse pre-training was superior to the strategy of pruning after meta-learning, and this advantage becomes more significant as the network parameters are reduced. In addition, the experiments also showed that the performance of the model decreases as the number of ways increases and increases as the number of shots increases. Overall, this study presents a few-shot learning method for crop disease recognition for edge devices. The method not only has a lower number of parameters and higher performance but also outperforms existing related studies. It provides a feasible technical route for future small-sample disease recognition under edge device conditions.

Keywords

• meta-learning
• metric learning
• plant protection
• crop disease recognition
• deep learning