End-to-end deep fusion of hyperspectral imaging and computer vision techniques for rapid detection of wheat seed quality

IF 12.4 Q1 AGRICULTURE, MULTIDISCIPLINARY
Tingting Zhang , Jing Li , Jinpeng Tong , Yihu Song , Li Wang , Renye Wu , Xuan Wei , Yuanyuan Song , Rensen Zeng
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引用次数: 0

Abstract

Seeds are essential to the agri-food industry. However, their quality is vulnerable to biotic and abiotic stresses during production and storage, leading to various types of deterioration. Real-time monitoring and pre-sowing screening offer substantial potential for improved storage management, field performance, and flour quality. This study investigated diverse deterioration patterns in wheat seeds by analyzing 1000 high-quality and 1098 deteriorated seeds encompassing mold, aging, mechanical damage, insect damage, and internal insect infestation. Hyperspectral imaging (HSI) and computer vision (CV) were employed to capture surface data from both the embryo (EM) and endosperm (EN). Internal seed quality was further assessed using scanning electron microscopy, dissection, and standard germination tests. Both conventional machine learning algorithms and deep convolutional neural networks (DCNN) were employed to develop discriminative models using independent datasets. Results revealed that each data source contributed valuable information for seed quality assessment (validation set accuracy: 65.1–89.2 %), with the integration of HSI and CV showing considerable promise. A comparison of early and late fusion strategies led to the development of an end-to-end deep fusion model. The decision fusion-based DCNN model, integrating HSI-EM, HSI-EN, CV-EM, and CV-EN data, achieved the highest accuracy in both training (94.3 %) and validation (93.8 %) sets. Applying this model to seed lot screening increased the proportion of high-quality seeds from 47.7 % to 93.4 %. These findings were further supported by external samples and visualizations. The proposed end-to-end decision fusion DCNN model simplifies the training process compared to traditional two-stage fusion methods. This study presents a potentially efficient alternative for rapid, individual kernel quality detection and control during wheat production.
基于端到端高光谱成像和计算机视觉技术的小麦种子质量快速检测
种子对农业食品工业至关重要。然而,在生产和储存过程中,它们的品质容易受到生物和非生物胁迫,导致各种类型的变质。实时监测和播前筛选为改善储存管理、田间性能和面粉质量提供了巨大的潜力。通过对1000粒优质小麦种子和1098粒优质小麦种子进行霉变、老化、机械损伤、虫蛀和内部虫害等方面的分析,研究了小麦种子的不同变质模式。采用高光谱成像(HSI)和计算机视觉(CV)技术对胚胎(EM)和胚乳(EN)的表面数据进行了采集。内部种子质量通过扫描电子显微镜、解剖和标准发芽试验进一步评估。采用传统的机器学习算法和深度卷积神经网络(DCNN)建立独立数据集的判别模型。结果表明,每个数据源都为种子质量评估提供了有价值的信息(验证集准确率为65.1 - 89.2%),HSI和CV的整合显示出相当大的前景。早期和晚期融合策略的比较导致了端到端深度融合模型的发展。基于决策融合的DCNN模型集成了HSI-EM、HSI-EN、CV-EM和CV-EN数据,在训练集(94.3%)和验证集(93.8%)上都达到了最高的准确率。将该模型应用于种子批次筛选,使优质种子比例由47.7%提高到93.4%。这些发现得到了外部样本和可视化的进一步支持。与传统的两阶段融合方法相比,提出的端到端决策融合DCNN模型简化了训练过程。本研究为小麦生产过程中籽粒质量的快速、个性化检测和控制提供了一种潜在的有效替代方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Artificial Intelligence in Agriculture
Artificial Intelligence in Agriculture Engineering-Engineering (miscellaneous)
CiteScore
21.60
自引率
0.00%
发文量
18
审稿时长
12 weeks
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