Effective weed control plays a crucial role in optimizing crop yield and
enhancing agricultural product quality. However, the reliance on herbicide
application not only poses a critical threat to the environment but also
promotes the emergence of resistant weeds. Fortunately, recent advances in
precision weed management enabled by ML and DL provide a sustainable
alternative. Despite great progress, existing algorithms are mainly developed
based on supervised learning approaches, which typically demand large-scale
datasets with manual-labeled annotations, which is time-consuming and
labor-intensive. As such, label-efficient learning methods, especially
semi-supervised learning, have gained increased attention in the broader domain
of computer vision and have demonstrated promising performance. These methods
aim to utilize a small number of labeled data samples along with a great number
of unlabeled samples to develop high-performing models comparable to the
supervised learning counterpart trained on a large amount of labeled data
samples. In this study, we assess the effectiveness of a semi-supervised
learning framework for multi-class weed detection, employing two well-known
object detection frameworks, namely FCOS and Faster-RCNN. Specifically, we
evaluate a generalized student-teacher framework with an improved pseudo-label
generation module to produce reliable pseudo-labels for the unlabeled data. To
enhance generalization, an ensemble student network is employed to facilitate
the training process. Experimental results show that the proposed approach is
able to achieve approximately 76\% and 96\% detection accuracy as the
supervised methods with only 10\% of labeled data in CottenWeedDet3 and
CottonWeedDet12, respectively. We offer access to the source code, contributing
a valuable resource for ongoing semi-supervised learning research in weed
detection and beyond.

通过使用半监督学习和集成学生网络的方法，本研究评估了一种用于多类杂草检测的半监督学习框架，以增强模型的泛化能力，在 CottenWeedDet3 和 CottonWeedDet12 数据集上，只使用 10% 的标记数据，能够实现与监督学习方法相近的 76% 和 96% 的检测准确率。该研究成果提供了源代码，为杂草检测及其它半监督学习领域的研究提供了有价值的资源。

多类杂草检测的半监督学习框架性能评估

Performance Evaluation of Semi-supervised Learning Frameworks for  Multi-Class Weed Detection

In weed control, precision agriculture can help to greatly reduce the use of
herbicides, resulting in both economical and ecological benefits. A key element
is the ability to locate and segment all the plants from image data. Modern
instance segmentation techniques can achieve this, however, training such
systems requires large amounts of hand-labelled data which is expensive and
laborious to obtain. Weakly supervised training can help to greatly reduce
labelling efforts and costs. We propose panoptic one-click segmentation, an
efficient and accurate offline tool to produce pseudo-labels from click inputs
which reduces labelling effort. Our approach jointly estimates the pixel-wise
location of all N objects in the scene, compared to traditional approaches
which iterate independently through all N objects; this greatly reduces
training time. Using just 10% of the data to train our panoptic one-click
segmentation approach yields 68.1% and 68.8% mean object intersection over
union (IoU) on challenging sugar beet and corn image data respectively,
providing comparable performance to traditional one-click approaches while
being approximately 12 times faster to train. We demonstrate the applicability
of our system by generating pseudo-labels from clicks on the remaining 90% of
the data. These pseudo-labels are then used to train Mask R-CNN, in a
semi-supervised manner, improving the absolute performance (of mean foreground
IoU) by 9.4 and 7.9 points for sugar beet and corn data respectively. Finally,
we show that our technique can recover missed clicks during annotation
outlining a further benefit over traditional approaches.

本研究提出了一种精准农业方法，采用弱监督学习技术和点击输入，提出一种高效准确的离线工具 —— 全景一键分割法，它可以在降低标注数据成本的同时，有效地定位和分割所有的作物对象。在糖菜和玉米图像数据上的表现表明，我们的方法不仅与传统的一键分割法比拼，而且训练速度更快，使得我们能够将众多的边缘标记转化为面标记，从而有效地提高了模型的性能

Panoptic One-Click 分割：应用于农业数据

Panoptic One-Click Segmentation: Applied to Agricultural Data

Currently, weed control in a corn field is performed by a blanket application
of herbicides that do not consider spatial distribution information of weeds
and also uses an extensive amount of chemical herbicides. To reduce the amount
of chemicals, we used drone-based high-resolution imagery and computer-vision
techniques to perform site-specific weed control in corn.

利用基于无人机的高分辨率图像和计算机视觉技术，进行基于位置的玉米田杂草控制，以减少化学草 herbicides 的使用。