  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. Our method achieves 83.48% accuracy on 956 classes of the
PlantCLEF2015 dataset, surpassing state-of-the-art methods. It outperforms late
fusion by 11.07% and is more robust to missing modalities. We validate our
model against established benchmarks using standard performance metrics and
McNemar's test, further underscoring its superiority.
