基于尺度交互蒸馏网络的薄壳山核桃品种识别方法

    Identification method for Carya illinoensis varieties based on scale interactive distillation network

    • 摘要: 薄壳山核桃是一种重要的经济坚果,由于品种繁多,对其进行快速科学的鉴定是进行种质资源保护与品种选育的重要基础。为了实现薄壳山核桃品种的快速鉴定,该研究针对品种鉴定提出了基于尺度交互蒸馏网络的薄壳山核桃品种识别方法,通过学习薄壳山核桃的可判别性特征实现品种鉴定。研究选择波尼等12种薄壳山核桃,建立了9 048张实拍图像的品种识别数据集;针对薄壳山核桃图片取样中距离变化导致的目标尺度多样性问题,设计了一种全局-局部特征协同学习方案,用于提取尺度不变特征;与此同时,该研究结合尺度知识蒸馏方案,通过训练提取的不同尺度数据进行预测保证模型训练的有效性。结果表明,通过训练该方法对上述12个品种的薄壳山核桃品种识别准确率均达到了96.98%,显著提高了薄壳山核桃的品种鉴定准确率。该研究开发的薄壳山核桃品种自动识别模型对于未来果实鉴定及产品分选提供了技术手段。

       

      Abstract: Pecan (Carya illinoinensis) is one of the highly valuable nut crops in the global agricultural economy, due to its nutritional and economic advantages. There has ever a rising demand for high-quality cultivars of pecan in recent years. Precise and efficient identification is then required for germplasm resource conservation, breeding programs, and commercial production. However, some challenges still remain in identifying the pecan cultivars, owing to their morphological similarities, and environmental and image acquisition, such as lighting, angle, and distance. Traditional cultivar identification can also rely mainly on manual observation and expert knowledge; These manual approaches cannot fully meet large-scale production, due to the time-consuming susceptibility to subjective errors. It is very necessary to develop automatic, scalable, and accurate recognition of the pecan cultivars. In this study, a Scale-Interactive Knowledge Distillation Network (SIKD-Net) was introduced to enhance the accuracy of classification using deep learning. The discriminative features were then extracted from the pecan images, in order to facilitate the highly precise differentiation of cultivar. 9,048 images were captured from the twelve cultivars of widely cultivated pecan, such as Pawnee. An image dataset was established to support the training and evaluation of the improved model. Among them, the different imaging distances also caused variations in the object scales during data acquisition. The inconsistent representation of features seriously deteriorated the classification of the diverse image samples. Fortunately, the SIKD-Net framework performed better to solve the scale variation and feature inconsistency during image classification. The global-local feature was also incorporated with the scale-aware knowledge distillation. A series of experiments were then conducted to evaluate the performance of the SIKD-Net model. The results indicate that the classification accuracy of 96.98% was achieved in the twelve pecan cultivars, significantly surpassing conventional machine learning and deep learning. A comparison was also made on the baseline models, including the standard convolutional neural networks (CNNs) and transformer-based architectures. Moreover, the scale-interactive knowledge distillation substantially enhanced both the robustness and accuracy of the cultivar recognition. The high accuracy of classification was achieved in the pecan cultivar for agricultural and industrial applications. Automatic recognition can be extended to the rest of tree nuts and fruit crops for precise classification. Furthermore, the improved model can be deployed within smart systems for seedling selection, orchard management, and post-harvesting. Therefore, intelligent sorting can be integrated to enhance efficiency, in order to reduce the reliance on manual inspection in commercial settings. In conclusion, deep-learning identification can be expected to significantly enhance the efficiency and accuracy of pecan cultivar recognition. The hyperspectral imaging and multimodal feature fusion can also be integrated to further enhance the performance and the deployment of the SIKD-Net model for the large-scale classification in real-world orchard environment. The findings can also provide a scientifically robust and practically viable solution to pecan germplasm identification in precision agriculture, intelligent crop classification, and food processing.

       

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