The sorghum panicle is an important trait related to grain yield and plant
development. Detecting and counting sorghum panicles can provide significant
information for plant phenotyping. Current deep-learning-based object detection
methods for panicles require a large amount of training data. The data labeling
is time-consuming and not feasible for real application. In this paper, we
present an approach to reduce the amount of training data for sorghum panicle
detection via semi-supervised learning. Results show we can achieve similar
performance as supervised methods for sorghum panicle detection by only using
10\% of original training data.

该研究使用半监督学习方法，在只使用原始训练数据的 10％的情况下，实现了类似监督方法的高精度识别高粱穗的效果。

无人机图像中高粱穗部的半监督物体检测

Semi-Supervised Object Detection for Sorghum Panicles in UAV Imagery

Phenotyping is the process of measuring an organism's observable traits.
Manual phenotyping of crops is a labor-intensive, time-consuming, costly, and
error prone process. Accurate, automated, high-throughput phenotyping can
relieve a huge burden in the crop breeding pipeline. In this paper, we propose
a scalable, high-throughput approach to automatically count and segment
panicles (heads), a key phenotype, from aerial sorghum crop imagery. Our
counting approach uses the image density map obtained from dot or region
annotation as the target with a novel deep convolutional neural network
architecture. We also propose a novel instance segmentation algorithm using the
estimated density map, to identify the individual panicles in the presence of
occlusion. With real Sorghum aerial images, we obtain a mean absolute error
(MAE) of 1.06 for counting which is better than using well-known crowd counting
approaches such as CCNN, MCNN and CSRNet models. The instance segmentation
model also produces respectable results which will be ultimately useful in
reducing the manual annotation workload for future data.

本文提出了一种高通量的自动计数和分割高粱中穗（头）的方法，并使用一种新颖的深度卷积神经网络架构来处理图像密度图，以进行计数。最终得到的实例分割模型也产生了可观的结果。