Abstract
Insect classification is important for agricultural management and ecological research, as it directly affects crop health and production. However, this task remains challenging due to the complex characteristics of insects, class imbalance, and large-scale datasets. To address these issues, we propose BioAutoML-NAS, the first BioAutoML model using multimodal data, including images, and metadata, which applies neural architecture search (NAS) for images to automatically learn the best operations for each connection within each cell. Multiple cells are stacked to form the full network, each extracting detailed image feature representations. A multimodal fusion module combines image embeddings with metadata, allowing the model to use both visual and categorical biological information to classify insects. An alternating bi-level optimization training strategy jointly updates network weights and architecture parameters, while zero operations remove less important connections, producing sparse, efficient, and high-performing architectures. Extensive evaluation on the BIOSCAN-5M dataset demonstrates that BioAutoML-NAS achieves 96.81% accuracy, 97.46% precision, 96.81% recall, and a 97.05% F1 score, outperforming state-of-the-art transfer learning, transformer, AutoML, and NAS methods by approximately 16%, 10%, and 8% respectively. Further validation on the Insects-1M dataset obtains 93.25% accuracy, 93.71% precision, 92.74% recall, and a 93.22% F1 score. These results demonstrate that BioAutoML-NAS provides accurate, confident insect classification that supports modern sustainable farming.
Abstract (translated)
昆虫分类对于农业管理和生态研究至关重要，因为它直接影响作物的健康和产量。然而，由于昆虫具有复杂的特征、类不平衡以及大规模的数据集等问题，这一任务仍然极具挑战性。为了解决这些问题，我们提出了BioAutoML-NAS，这是第一个使用多模态数据（包括图像和元数据）的BioAutoML模型，并应用神经架构搜索（NAS）对图像进行自动学习，以确定每个连接中的最佳操作。多个细胞被堆叠起来形成完整的网络，每个细胞提取详细的图像特征表示。一个多模态融合模块结合了图像嵌入与元数据，使模型能够利用视觉和分类生物信息来分类昆虫。一种交替的双层优化训练策略共同更新网络权重和架构参数，并通过去除不重要的连接以生成稀疏、高效且性能高的架构。 在BIOSCAN-5M数据集上的广泛评估表明，BioAutoML-NAS实现了96.81%的准确率、97.46%的精确度、96.81%的召回率和97.05%的F1值，分别比最先进的迁移学习、Transformer模型、AutoML方法及NAS方法高出约16%、10%和8%。在Insects-1M数据集上的进一步验证获得了93.25%的准确率、93.71%的精确度、92.74%的召回率以及93.22%的F1值。 这些结果表明，BioAutoML-NAS能够提供准确且可靠的昆虫分类，支持现代可持续农业。
URL
https://arxiv.org/abs/2510.05888
