An accurate and robust strawberry flower representation and detection scheme is a key step to enable the reliable forecasting of fruit yield for use in precision agricultural applications. A state-of-the-art deep-level object detection framework which processes images through several layers using a region-based convolutional neural network (R-CNN) was developed to visually represent the instances of strawberry flowers in outdoor fields and improve the detection accuracy. A modified version of the visual geometry group 19 (VGG19) architecture, which had 47 layers, was used to represent the multiple scales of strawberry flower image features. The networks were trained entirely on 400 strawberry flower images and tested on another 100 images. Different region-based object detection methods, including the R-CNN, Fast R-CNN and Faster R-CNN, were used to represent the strawberry flower instances. The Faster R-CNN model achieved a better performance than the R-CNN and Fast R-CNN in detecting the instances and had a lower execution time. The detection accuracy of the Faster R-CNN model was 86.1%, which was higher than those of the R-CNN and Fast R-CNN models (63.4% and 76.7%, respectively). The experimental results showed the effectiveness of the deep-level Faster R-CNN framework for representing the strawberry flower instances under various camera view-points, different distances to flowers, overlaps, complex background illumination, blur, etc. The system developed for automatic and accurate strawberry flower detection provides an important and significant solution that enables subsequent applications to estimate the strawberry yield in outdoor fields.

中文翻译：

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一种基于深层次区域的户外草莓花检测视觉表示架构

准确而强大的草莓花表示和检测方案是实现可靠预测果实产量以用于精准农业应用的关键步骤。开发了一种最先进的深层对象检测框架，该框架使用基于区域的卷积神经网络 (R-CNN) 对图像进行多层处理，以直观地表示室外田地草莓花的实例并提高检测效率准确性。视觉几何组 19 (VGG19) 架构的修改版本，具有 47 层，用于表示草莓花图像特征的多个尺度。这些网络完全在 400 张草莓花图像上进行了训练，并在另外 100 张图像上进行了测试。不同的基于区域的物体检测方法，包括 R-CNN、Fast R-CNN 和 Faster R-CNN，用于表示草莓花实例。Faster R-CNN 模型在检测实例方面取得了比 R-CNN 和 Fast R-CNN 更好的性能，并且执行时间更短。Faster R-CNN 模型的检测准确率为 86.1%，高于 R-CNN 和 Fast R-CNN 模型（分别为 63.4% 和 76.7%）。实验结果表明，深层次 Faster R-CNN 框架在表示各种相机视点、不同距离花朵、重叠、复杂背景照明、模糊等情况下的草莓花实例的有效性。准确的草莓花检测提供了一个重要且意义重大的解决方案，使后续应用程序能够估计户外田地的草莓产量。