Abstract Apple trees commonly require the removal of excessive flowers by thinning to produce high quality fruit. Machine vision has recently been applied to detect the flower density as the first step in this process. Existing work relying on color thresholding is sensitive to imaging conditions and the most recent published work using deep learning in this context has proven to be exceptionally slow to process. This paper presents an apple flower segmentation method on a pixel level based on a Fully Convolutional Network (FCN) together with a process of generating a map that can be used for a variable rate chemical sprayer. Despite the challenging conditions of an uncontrolled environment, our apple flower detector was able to generate a F1 score at pixel-level up to 85.6%, which is a relatively high accuracy in terms of pixel-level segmentation. Our method has been tested on both daytime and night-time datasets, which strongly validates the ability of our apple flower detector to work under different conditions. The resulting detections are georeferenced and merged into a density map in the format necessary for application by a variable rate chemical sprayer. Finally, this flower density mapping system will benefit farmers by visualising the whole crop and extracting useful information to support their decision making for chemical thinning.

中文翻译：

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使用基于边缘的全卷积网络进行可变速率化学稀释的侧视苹果花映射

摘要 苹果树通常需要通过间伐去除过多的花来生产高质量的果实。最近，机器视觉已被应用于检测花卉密度，作为该过程的第一步。依赖于颜色阈值的现有工作对成像条件很敏感，并且最近发表的在这种情况下使用深度学习的工作已被证明处理起来异常缓慢。本文提出了一种基于全卷积网络 (FCN) 的像素级苹果花分割方法，以及生成可用于可变速率化学喷雾器的地图的过程。尽管不受控制的环境条件具有挑战性，但我们的苹果花检测器能够在像素级生成高达 85.6% 的 F1 分数，这在像素级分割方面具有相对较高的准确度。我们的方法已经在白天和夜间数据集上进行了测试，这有力地验证了我们的苹果花检测器在不同条件下工作的能力。所得检测结果经过地理参考并以可变速率化学喷雾器应用所需的格式合并到密度图中。最后，该花卉密度绘图系统将通过可视化整个作物并提取有用信息来支持他们的化学疏伐决策，从而使农民受益。