MCLC-NET: Multimodal Continual Learning for Leaf Counting

2026-09-16

AI Summary

为解决叶计数中的遮挡和光照变化等问题，提出MCLC-NET框架，利用多模态持续学习方法，并通过记忆缓冲区保留重要样本。

Abstract

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.

Problem

• Leaf Counting
• Multimodal Continual Learning
• Agricultural Settings

Innovation

• Multimodal Continual Learning
• Memory Buffer
• Leaf Counting
• MMLC Dataset
• Domain Incremental Scenario