Lou Lunhong, Lin Jianwu, You Lin, Zhang Xin, Cernava Tomislav, Lu Hanyu, Chen Xiaoyulong
College of Big Data and Information Engineering, Guizhou University, Guiyang, 550025, China.
State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, 550025, China.
Plant Methods. 2025 Jul 31;21(1):105. doi: 10.1186/s13007-025-01423-3.
Deep learning demonstrates strong generalisation capabilities, driving substantial progress in plant disease recognition systems. However, current methods are predominantly optimised for offline implementation. Real-time crop surveillance systems encounter streaming images containing novel disease classes in few-shot conditions, demanding incrementally adaptive models. This capability is called few-shot class-incremental learning (FSCIL). Here, we introduce VCPV-virtual contrastive constraints with prototype vector calibration-enabling sustainable plant disease classification under FSClL conditions. Specifically, our method consists of two phases: the base class training phase and the incremental training phase. During the base class training phase, the virtual contrastive class constraints (VCC) module is utilised to enhance learning from base classes and allocate sufficient embedding space for new plant disease images. In the incremental training phase, the prototype calibration embedding (PCE) module is introduced to distinguish newly arriving plant disease categories from previous ones, thereby optimising the prototype space and enhancing the recognition accuracy of new categories. We evaluated our approach on the PlantVillage dataset, and the experimental results under both 5-way 5-shot and 3-way 5-shot settings demonstrate that our method achieves state-of-the-art accuracy. At the same time, we achieved promising performance on the publicly available CIFAR-100 dataset. Furthermore, the visualisation results validate that our strategy effectively supports fine-grained, sustainable disease recognition, highlighting the potential of our approach to advance FSCIL in the field of plant disease monitoring.
深度学习展现出强大的泛化能力，推动了植物病害识别系统的重大进展。然而，当前方法主要针对离线实现进行优化。实时作物监测系统在少样本情况下会遇到包含新病害类别的流式图像，这就需要具有增量自适应能力的模型。这种能力被称为少样本类别增量学习（FSCIL）。在此，我们引入了带有原型向量校准的VCPV - 虚拟对比约束，以实现FSCIL条件下可持续的植物病害分类。具体而言，我们的方法包括两个阶段：基础类别训练阶段和增量训练阶段。在基础类别训练阶段，利用虚拟对比类别约束（VCC）模块增强对基础类别的学习，并为新的植物病害图像分配足够的嵌入空间。在增量训练阶段，引入原型校准嵌入（PCE）模块，以区分新出现的植物病害类别与先前的类别，从而优化原型空间并提高新类别的识别准确率。我们在PlantVillage数据集上评估了我们的方法，在5路5样本和3路5样本设置下的实验结果表明，我们的方法达到了当前最优的准确率。同时，我们在公开可用的CIFAR - 100数据集上也取得了良好的性能。此外，可视化结果验证了我们的策略有效地支持了细粒度、可持续的病害识别，突出了我们的方法在植物病害监测领域推进FSCIL的潜力。
