Efficient Adaptive Incremental Learning for Fruit and Vegetable Classification

Abstract

Traditional deep learning models for fruit and vegetable classification are usually implemented via training on an unchanged dataset. However, changing fruit and vegetable categories is a very common occurrence in the context of real agricultural sales. When dealing with changes related to variety, deep learning models need to be retrained on the entire updated dataset. The retraining process is time-consuming and inefficient, and it may even cause the ‘catastrophic forgetting’ problem. In response to this challenge, the Adversarial Domain Adaptation Class Incremental Learning (ADA-CIL) method is introduced. This approach employs adversarial domain adaptation techniques combined with core-set selection strategies to effectively extract and integrate cross-domain features. We utilize the ResNet34 architecture as the backbone for feature extraction due to its deep residual learning framework, which is robust in handling the complexities of large and varied image datasets. It achieves a dynamic balance in learning between new and existing categories, significantly enhancing the model’s generalization capabilities and information retention efficiency. The FruVeg dataset, composed of three sub-datasets, includes over 120,000 color images, covering more than 100 different categories of fruits and vegetables collected from various domains and backgrounds. The experimental results on the FruVeg dataset show that the ADA-CIL method achieves an average accuracy of 96.30%, a forgetting rate of 2.96%, a cumulative accuracy of 96.26%, and a current accuracy of 98.60%. The ADA-CIL method improves the average accuracy by 1.65% and 1.82% compared to iCaRL and BiC, respectively, and it reduces the forgetting rate by 2.69% and 2.76%. These performance metrics demonstrate the ADA-CIL method’s impressive ability to handle incremental category and domain changes, highlighting its capability to effectively maintain the intra-class stability and exhibit exceptional adaptability in dynamic learning environments.