Plant counting and location are essential for both plant breeding experiments and production agriculture. Stand count indicates the overall emergence of plants compared to the number of seeds that were planted, while location provides information on the associated variability within a plot or geographic area of a field. Deep learning has been successfully applied in various application domains, including plant phenotyping. This article proposes the use of deep learning techniques, more specifically, anchor-free detectors, to identify and count maize plants in RGB images acquired from unmanned aerial vehicles. The results were obtained using a modified CenterNet architecture, with validation performed against manual human annotation. Experimental results demonstrated an overall precision >95% for examples where training and testing were performed on the same field. Few-shot learning was also explored, where the trained network was 1) directly applied to the fields in other geographic areas and 2) updated using small quantities of training data from the other locations.

中文翻译：

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基于少样本学习技术的植物自动计数和定位


植物计数和定位对于植物育种实验和农业生产都至关重要。林分计数表示植物的整体出苗率与所种植种子的数量相比，而位置则提供有关田地或地理区域内相关变异性的信息。深度学习已成功应用于各种应用领域，包括植物表型分析。本文建议使用深度学习技术，更具体地说，使用无锚检测器来识别和计数从无人机获取的 RGB 图像中的玉米植株。结果是使用修改后的 CenterNet 架构获得的，并针对手动人工注释进行了验证。实验结果表明，对于在同一区域进行训练和测试的示例，总体精度为 >95%。还探索了少样本学习，其中训练后的网络 1）直接应用于其他地理区域的领域，2）使用来自其他位置的少量训练数据进行更新。