This study evaluates the effectiveness of fine-tuning a semantic segmentation model to identify and quantify dollar spot in turfgrasses, the most extensively managed and researched disease of turfgrasses worldwide. Using the DeepLabV3+ model, recognized for its capability to segment complex shapes and integrate multi-scale contextual information, the research leveraged a diverse dataset comprising various turfgrass species, disease stages, and lighting conditions to ensure robust model training. The trained model is able to identify and segment disease instances accurately and precisely, and the results indicate the potential for model-based assessment to outperform traditional visual assessment methods in speed, accuracy, and consistency. The development of deep learning models on extensive datasets like ImageNet requires significant computational resources. However, by fine-tuning a pretrained semantic segmentation model, we adapted it for disease segmentation using only a standard personal computer's graphics processing unit. This approach not only conserves resources but also highlights the practicality of deploying advanced deep learning applications in turfgrass pathology with limited computational capacity. The proposed model provides a new tool for turfgrass researchers and professionals to rapidly and accurately quantify this important disease under real-world growing conditions. Additionally, the findings suggest the potential to apply deep learning algorithms to other turfgrass diseases to support data-driven decisions. This could enhance disease management practices and improve decision-making processes for fungicidal treatments, thereby improving the economic and environmental sustainability of turfgrass management.

中文翻译：

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利用深度学习进行草坪中的美元点检测和量化


本研究评估了微调语义分割模型来识别和量化草坪病害的有效性，草坪病害是全球范围内管理和研究最广泛的草坪病害。该研究使用 DeepLabV3+ 模型（因其分割复杂形状和集成多尺度上下文信息的能力而闻名），利用包含各种草坪草物种、疾病阶段和照明条件的多样化数据集来确保稳健的模型训练。经过训练的模型能够准确、精确地识别和分割疾病实例，结果表明基于模型的评估在速度、准确性和一致性方面有可能超越传统的视觉评估方法。在 ImageNet 等广泛数据集上开发深度学习模型需要大量的计算资源。然而，通过微调预训练的语义分割模型，我们仅使用标准个人计算机的图形处理单元将其用于疾病分割。这种方法不仅节省了资源，而且凸显了在计算能力有限的情况下在草坪病理学中部署先进的深度学习应用程序的实用性。所提出的模型为草坪研究人员和专业人员提供了一种新工具，可以在现实世界的生长条件下快速准确地量化这种重要的疾病。此外，研究结果表明，有可能将深度学习算法应用于其他草坪病害，以支持数据驱动的决策。这可以加强疾病管理实践并改善杀菌处理的决策过程，从而提高草坪管理的经济和环境可持续性。