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Abstract：
Advancements in imaging, computer vision, and automation have revolutionized various fields, including field-based high-throughput plant phenotyping (FHTPP). This integration allows for the rapid and accurate measurement of plant traits. Deep Convolutional Neural Networks (DCNNs) have emerged as a powerful tool in FHTPP, particularly in crop segmentation-identifying crops from the background-crucial for trait analysis. However, the effectiveness of DCNNs often hinges on the availability of large, labeled datasets, which poses a challenge due to the high cost of labeling. In this study, a deep learning with bagging approach is introduced to enhance crop segmentation using high-resolution RGB images, tested on the NU-Spidercam dataset from maize plots. The proposed method outperforms traditional machine learning and deep learning models in prediction accuracy and speed. Remarkably, it achieves up to 40% higher Intersection-over-Union (IoU) than the threshold method and 11% over conventional machine learning, with significantly faster prediction times and manageable training duration. Crucially, it demonstrates that even small labeled datasets can yield high accuracy in semantic segmentation. This approach not only proves effective for FHTPP but also suggests potential for broader application in remote sensing, offering a scalable solution to semantic segmentation challenges. This paper is accompanied by publicly available source code.
摘要：
成像的进步，计算机视觉,自动化彻底改变了各个领域，包括基于田间的高通量植物表型鉴定(FHTPP)。这种整合允许快速准确地测量植物性状。深度卷积神经网络(DCNN)已经成为FHTPP中的一个强大工具，特别是在作物分割中-从背景中识别作物-对于性状分析至关重要。然而,DCNN的有效性通常取决于大型，标记的数据集，由于标签的高成本，这带来了挑战。在这项研究中,引入了一种带套袋的深度学习方法，以使用高分辨率RGB图像增强作物分割，在玉米地块的NU-Spidercam数据集上测试。该方法在预测精度和速度上优于传统的机器学习和深度学习模型。值得注意的是,它比阈值方法实现了高达40%的交叉联合(IoU)，比传统机器学习提高了11%，具有明显更快的预测时间和可管理的训练持续时间。至关重要的是，它表明，即使是小的标记数据集也可以在语义分割中产生很高的准确性。这种方法不仅对FHTPP有效，而且还暗示了在遥感中更广泛应用的潜力，为语义分割挑战提供可扩展的解决方案。本文附有公开可用的源代码。
