Automatic Fused Multimodal Deep Learning for Plant Identification

TL;DR

This paper presents an innovative deep learning approach that automatically fuses multiple plant data modalities for improved classification accuracy, leveraging architecture search and a new multimodal dataset.

Contribution

It introduces a novel multimodal fusion architecture search method and a new dataset, Multimodal-PlantCLEF, for plant classification, achieving superior accuracy over existing methods.

Findings

Achieved 82.61% accuracy on Multimodal-PlantCLEF

Outperformed late fusion by 10.33%

Demonstrated robustness to missing modalities

Abstract

Plant classification is vital for ecological conservation and agricultural productivity, enhancing our understanding of plant growth dynamics and aiding species preservation. The advent of deep learning (DL) techniques has revolutionized this field by enabling autonomous feature extraction, significantly reducing the dependence on manual expertise. However, conventional DL models often rely solely on single data sources, failing to capture the full biological diversity of plant species comprehensively. Recent research has turned to multimodal learning to overcome this limitation by integrating multiple data types, which enriches the representation of plant characteristics. This shift introduces the challenge of determining the optimal point for modality fusion. In this paper, we introduce a pioneering multimodal DL-based approach for plant classification with automatic modality fusion. Utilizing the multimodal fusion architecture search, our method integrates images from multiple plant organs -- flowers, leaves, fruits, and stems -- into a cohesive model. To address the lack of multimodal datasets, we contributed Multimodal-PlantCLEF, a restructured version of the PlantCLEF2015 dataset tailored for multimodal tasks. Our method achieves 82.61% accuracy on 979 classes of Multimodal-PlantCLEF, surpassing state-of-the-art methods and outperforming late fusion by 10.33%. Through the incorporation of multimodal dropout, our approach demonstrates strong robustness to missing modalities. We validate our model against established benchmarks using standard performance metrics and McNemar's test, further underscoring its superiority.