We present new methods for apple detection and counting based on recent deep learning approaches and compare them with state-of-the-art results based on classical methods. Our goal is to quantify performance improvements by neural network-based methods compared to methods based on classical approaches. Additionally, we introduce a complete system for counting apples in an entire row. This task is challenging as it requires tracking fruits in images from both sides of the row. We evaluate the performances of three fruit detection methods and two fruit counting methods on six datasets. Results indicate that the classical detection approach still outperforms the deep learning based methods in the majority of the datasets. For fruit counting though, the deep learning based approach performs better for all of the datasets. Combining the classical detection method together with the neural network based counting approach, we achieve remarkable yield accuracies ranging from 95.56% to 97.83%.

中文翻译：

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苹果园产量测绘的果实检测与计数方法比较研究

我们提出了基于最近的深度学习方法的苹果检测和计数的新方法，并将它们与基于经典方法的最新结果进行比较。我们的目标是量化基于神经网络的方法与基于经典方法的方法相比的性能改进。此外，我们还引入了一个完整的系统来计算整行中的苹果。这项任务具有挑战性，因为它需要从行的两侧跟踪图像中的水果。我们在六个数据集上评估了三种水果检测方法和两种水果计数方法的性能。结果表明，在大多数数据集中，经典检测方法仍然优于基于深度学习的方法。不过，对于水果计数，基于深度学习的方法对所有数据集的表现都更好。