发表机构
Indian Institute of Technology Ropar; University of Regensburg; Khalifa University（印度理工学院罗巴尔校区; 雷根斯堡大学; 哈利法大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出MCLC-NET多模态持续学习框架，结合记忆缓冲策略，并构建MMLC数据集，在叶片计数任务中显著优于现有方法，实现跨作物、时间及混合顺序的稳健性能。
AI 中文摘要
叶片计数是植物表型分析中的一项重要任务，用于监测植物生长和估算作物产量。现有大多数方法依赖RGB图像，但其性能常受遮挡、光照变化及其他现实世界挑战的影响。深度图像和热图像等额外模态可提供有用的补充信息。然而，多模态叶片计数仍未被充分探索。此外，许多现有方法假设所有训练数据可同时获得，这在实际农业场景中不切实际，因为数据是随时间从多个来源收集的。为应对这些挑战，我们提出了MCLC-NET，一种用于叶片计数的多模态持续学习框架。它采用基于记忆的策略顺序学习任务，使用记忆缓冲区保留先前任务中的重要样本。我们还引入了MMLC，一个为持续学习中的领域增量场景（DIS）设计的真实世界多模态叶片计数数据集。该数据集包含在不同环境条件下跨不同作物类型收集的RGB、深度和热图像，并按三种顺序排列：按作物、按时间和混合。实验结果表明，在三个随机种子上的平均值显示，MCLC-NET在所有三种任务顺序上均持续优于现有方法，分别达到最低的AMSE 0.675±0.027、0.542±0.069和0.745±0.057。
英文摘要
Leaf counting is an important task in plant phenotyping for monitoring plant growth and estimating crop yield. Most existing methods rely on RGB images, but their performance is often affected by occlusion, lighting variations, and other real-world challenges. Additional modalities, such as depth and thermal images, can provide useful complementary information. However, multimodal leaf counting remains underexplored. Also, many existing methods assume that all training data are available simultaneously, which is impractical in real agricultural settings, where data is collected over time from multiple sources. To address these challenges, we propose MCLC-NET, a multimodal continual learning framework for leaf counting. It learns tasks sequentially using a memory-based strategy with a memory buffer to retain important samples from previous tasks. We also introduce MMLC, a real-world multimodal leaf-counting dataset designed for a domain incremental scenario (DIS) in CL. It contains RGB, depth, and thermal images collected across different crop types under varying environmental conditions, arranged in three orderings: crop-wise, time-wise, and mixed. Experimental results, averaged over three random seeds, demonstrate that MCLC-NET consistently outperforms existing methods across all three task orderings, achieving the lowest AMSE of 0.675$\pm$0.027, 0.542$\pm$0.069, and 0.745$\pm$0.057, respectively.
