用自研卷积网络高效识别10类油菜籽，准确率达93%。
A Novel Convolutional Neural Network-Based Framework for Complex Multiclass Brassica Seed Classification
- 设计专用卷积神经网络，解决种子图像纹理相似难题。
- 在自建数据集上达到93%分类准确率，优于主流模型。
- 适合农业质检、种子纯度监测等实际场景应用。
近年来农业研究加速发展，但农民因生产任务繁重，难以开展田间研究。种子分类有助于质量控制、生产效率提升和杂质检测。早期识别种子类型可降低田间出苗成本与风险，避免产量损失或后续流程中断。种子采样帮助种植者监控种子质量，提升纯度判断精度，指导管理调整并优化产量预估。本研究提出一种基于新型卷积神经网络（CNN）的框架，用于高效分类十种常见油菜籽。针对种子图像纹理相似的固有挑战，设计了定制化CNN架构。通过调整层配置优化分类性能，并在自建油菜籽数据集上进行实验验证。结果表明，所提模型在测试集上达到93%的高准确率，优于多个预训练先进模型。
原文摘要 · Abstract (English)
Agricultural research has accelerated in recent years, yet farmers often lack the time and resources for on-farm research due to the demands of crop production and farm operations. Seed classification offers valuable insights into quality control, production efficiency, and impurity detection. Early identification of seed types is critical to reducing the cost and risk associated with field emergence, which can lead to yield losses or disruptions in downstream processes like harvesting. Seed sampling supports growers in monitoring and managing seed quality, improving precision in determining seed purity levels, guiding management adjustments, and enhancing yield estimations. This study proposes a novel convolutional neural network (CNN)-based framework for the efficient classification of ten common Brassica seed types. The approach addresses the inherent challenge of texture similarity in seed images using a custom-designed CNN architecture. The model's performance was evaluated against several pre-trained state-of-the-art architectures, with adjustments to layer configurations for optimized classification. Experimental results using our collected Brassica seed dataset demonstrate that the proposed model achieved a high accuracy rate of 93 percent.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。