■在草莓种植中,表型性状(如冠直径,叶柄长度,植物高度,花,leaf,和水果大小)的测量至关重要,因为它可以作为植物监测和管理的决策工具。迄今为止,草莓植物表型鉴定依赖于传统方法。在这项研究中,使用两种深度学习(DL)架构开发了基于图像的草莓表型工具(SPT),即“YOLOv4”和“U-net”集成到单个系统中。我们旨在创建具有增强鲁棒性的最合适的基于DL的工具,以直接在自然场景中或间接使用捕获和存储的图像来促进数字草莓植物表型。
■我们的SPT主要是通过两个步骤(随后称为版本)使用简单的智能手机相机捕获的具有不同背景的图像数据进行开发的。两个版本(V1和V2)是使用相同的DL网络开发的,但在开发过程中使用的图像数据量和注释方法上有所不同。对于V1,使用单目标非标记方法注释了7116张图像,而对于V2,使用多目标标记方法注释了7,850张图像。
■保留的数据集的结果表明,开发的SPT有助于草莓表型的测量。通过增加数据集大小与多目标标记注释相结合,我们系统的检测精度从V1的60.24%变为V2的82.28%。在验证过程中,使用每个表型的70张图像及其相应的实际值对系统进行了评估.V2的相关系数和检测频率高于V1,证实了V2的优越性。此外,建立了基于图像的回归模型,根据果实大小(R2=0.92)预测草莓的鲜重。
■结果表明,无论草莓植物环境的复杂情况如何,我们的系统在识别上述六种草莓表型性状方面的效率。该工具可以帮助农民和研究人员做出与草莓植物管理相关的准确和有效的决策,可能导致生产率和产量潜力的提高。
UNASSIGNED: In
strawberry farming, phenotypic traits (such as crown diameter, petiole length, plant height, flower, leaf, and fruit size) measurement is essential as it serves as a decision-making tool for plant monitoring and management. To date,
strawberry plant phenotyping has relied on traditional approaches. In this study, an image-based
Strawberry Phenotyping Tool (SPT) was developed using two deep-learning (DL) architectures, namely \"YOLOv4\" and \"U-net\" integrated into a single system. We aimed to create the most suitable DL-based tool with enhanced robustness to facilitate digital
strawberry plant phenotyping directly at the natural scene or indirectly using captured and stored images.
UNASSIGNED: Our SPT was developed primarily through two steps (subsequently called versions) using image data with different backgrounds captured with simple smartphone cameras. The two versions (V1 and V2) were developed using the same DL networks but differed by the amount of image data and annotation method used during their development. For V1, 7,116 images were annotated using the single-target non-labeling method, whereas for V2, 7,850 images were annotated using the multitarget labeling method.
UNASSIGNED: The results of the held-out dataset revealed that the developed SPT facilitates strawberry phenotype measurements. By increasing the dataset size combined with multitarget labeling annotation, the detection accuracy of our system changed from 60.24% in V1 to 82.28% in V2. During the validation process, the system was evaluated using 70 images per phenotype and their corresponding actual values. The correlation coefficients and detection frequencies were higher for V2 than for V1, confirming the superiority of V2. Furthermore, an image-based regression model was developed to predict the fresh weight of strawberries based on the fruit size (R2 = 0.92).
UNASSIGNED: The results demonstrate the efficiency of our system in recognizing the aforementioned six strawberry phenotypic traits regardless of the complex scenario of the environment of the
strawberry plant. This tool could help farmers and researchers make accurate and efficient decisions related to strawberry plant management, possibly causing increased productivity and yield potential.