The population of plants is a crucial indicator in plant phenotyping and agricultural production, such as growth status monitoring, yield estimation, and grain depot management. To enhance the production efficiency and liberate labor force, many automated counting methods have been proposed, in which computer vision-based approaches show great potentials due to the feasibility of high-throughput processing and low cost. In particular, with the success of deep learning, more and more deeper learning-based approaches are introduced to deal with agriculture automation. Since different detection- and regression-based counting models have distinct characteristics, how to choose an appropriate model given the target task at hand remains unexplored and is important for practitioners. Targeting in-field maize tassels as a representative case study, the goal of this work is to present a comprehensive benchmark of state-of-the-art object detection and object counting methods, including Faster R-CNN, YOLOv3, FaceBoxes, RetinaNet, and the leading counting model of maize tassels—TasselNet. We create a Maize Tassel Detection Counting (MTDC) dataset by supplementing bounding box annotations to the Maize Tassels Counting (MTC) dataset to allow the training of detection models. We investigate key factors effecting the practical applications of the models, such as convergence behavior, scale robustness, speed-accuracy trade-off, as well as parameter sensitivity. Based on our benchmark, we summarise the advantages and limitations of each method and suggest several possible directions to improve current detection- and regression-based counting approaches to benefit next-generation intelligent agriculture. Current state-of-the-art detection- and regression-based counting approaches can all achieve a relatively high degree of accuracy when dealing with in-field maize tassels, with at least 0.85 $$R^2$$ values and 28.2% rRMSE error. While detection-based methods are more robust than regression-based methods in scale variations and can infer extra information (e.g., object positions and sizes), the latter ones have significantly faster convergence behaviors and inference speed. To choose an appropriate in-filed plant counting method, accuracy, robustness, speed and some other algorithm-specific factors should be taken into account with the same priority. This work sheds light on different aspects of existing detection and counting approaches and provides guidance on how to tackle in-field plant counting. The MTDC dataset is made available at https://git.io/MTDC

中文翻译：

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玉米流苏检测：最先进的基准。

植物数量是植物表型分析和农业生产的重要指标，例如生长状态监测、产量估算和粮库管理。为了提高生产效率和解放劳动力，人们提出了许多自动计数方法，其中基于计算机视觉的方法由于高通量处理的可行性和低成本而显示出巨大的潜力。特别是随着深度学习的成功，越来越多的基于深度学习的方法被引入来处理农业自动化。由于不同的基于检测和回归的计数模型具有不同的特征，因此如何根据手头的目标任务选择合适的模型仍然是未知数，对于从业者来说很重要。以田间玉米穗作为代表性案例研究，这项工作的目标是提供最先进的对象检测和对象计数方法的综合基准，包括 Faster R-CNN、YOLOv3、FaceBoxes、RetinaNet 和领先的玉米流苏计数模型 - TasselNet。我们通过向玉米流苏计数 (MTC) 数据集补充边界框注释来创建玉米流苏检测计数 (MTDC) 数据集，以允许训练检测模型。我们研究了影响模型实际应用的关键因素，例如收敛行为、规模鲁棒性、速度-精度权衡以及参数敏感性。根据我们的基准，我们总结了每种方法的优点和局限性，并提出了几个可能的方向来改进当前基于检测和回归的计数方法，以造福于下一代智能农业。当前最先进的基于检测和回归的计数方法在处理田间玉米流苏时都可以达到相对较高的准确度，至少有 0.85 美元的 R^2 美元值和 28.2% 的 rRMSE错误。虽然基于检测的方法在尺度变化方面比基于回归的方法更稳健，并且可以推断出额外的信息（例如，对象位置和大小），但后者具有明显更快的收敛行为和推断速度。选择合适的田间植物计数方法、准确性、稳健性、速度和其他一些算法特定的因素应该以相同的优先级考虑。这项工作揭示了现有检测和计数方法的不同方面，并为如何解决现场植物计数提供了指导。MTDC 数据集可在 https://git.io/MTDC 获得