Advancements in machine vision that enable detailed inferences to be made
from images have the potential to transform many sectors including agriculture.
Precision agriculture, where data analysis enables interventions to be
precisely targeted, has many possible applications. Precision spraying, for
example, can limit the application of herbicide only to weeds, or limit the
application of fertiliser only to undernourished crops, instead of spraying the
entire field. The approach promises to maximise yields, whilst minimising
resource use and harms to the surrounding environment. To this end, we propose
a hierarchical panoptic segmentation method to simultaneously identify
indicators of plant growth and locate weeds within an image. We adapt
Mask2Former, a state-of-the-art architecture for panoptic segmentation, to
predict crop, weed and leaf masks. We achieve a PQ{\dag} of 75.99.
Additionally, we explore approaches to make the architecture more compact and
therefore more suitable for time and compute constrained applications. With our
more compact architecture, inference is up to 60% faster and the reduction in
PQ{\dag} is less than 1%.

机器视觉技术在农业等多个领域的精细分析有潜力进行转型。本文提出了一种层次式全景分割方法，用于同时识别植物生长的指标和图像中的杂草，并在此基础上探索了使该架构更紧凑适用于时间和计算受限应用的方法。通过更紧凑的架构，推理速度提升了 60％，PQ 得分降低不到 1％。

Hierarchical Mask2Former: 农作物、杂草和叶片的全景分割

Hierarchical Mask2Former: Panoptic Segmentation of Crops, Weeds and  Leaves

The safe application of reinforcement learning (RL) requires generalization
from limited training data to unseen scenarios. Yet, fulfilling tasks under
changing circumstances is a key challenge in RL. Current state-of-the-art
approaches for generalization apply data augmentation techniques to increase
the diversity of training data. Even though this prevents overfitting to the
training environment(s), it hinders policy optimization. Crafting a suitable
observation, only containing crucial information, has been shown to be a
challenging task itself. To improve data efficiency and generalization
capabilities, we propose Compact Reshaped Observation Processing (CROP) to
reduce the state information used for policy optimization. By providing only
relevant information, overfitting to a specific training layout is precluded
and generalization to unseen environments is improved. We formulate three CROPs
that can be applied to fully observable observation- and action-spaces and
provide methodical foundation. We empirically show the improvements of CROP in
a distributionally shifted safety gridworld. We furthermore provide benchmark
comparisons to full observability and data-augmentation in two different-sized
procedurally generated mazes.

本文提出 Compact Reshaped Observation Processing (CROP) 方法，通过减少用于策略优化的状态信息来提高数据效率和泛化能力，为完全可观测的观察和行动空间制定了三个 CROP，并在分布式安全格网世界中进行了实证展示。同时，与两个不同大小的程序生成的迷宫进行了基准比较，表明了 CROP 的改进。

CROP: 基于紧凑重塑观察处理的分布偏移鲁棒性强化学习

CROP: Towards Distributional-Shift Robust Reinforcement Learning using  Compact Reshaped Observation Processing

Many existing techniques provide automatic estimation of crop damage due to
various diseases. However, early detection can prevent or reduce the extend of
damage itself. The limited performance of existing techniques in early
detection is lack of localized information. We instead propose a dataset with
annotations for each diseased segment in each image. Unlike existing
approaches, instead of classifying images into either healthy or diseased, we
propose to provide localized classification for each segment of an images. Our
method is based on Mask RCNN and provides location as well as extend of
infected regions on the plant. Thus the extend of damage on the crop can be
estimated. Our method has obtained overall 87.6% accuracy on the proposed
dataset as compared to 58.4% obtained without incorporating localized
information.

提出基于 Mask RCNN 的作物病害本地化分类方法，用于早期检测，可提供病变区域的位置和范围估计，相对现有方法，本方法对作物病损区域的准确率达到了 87.6%。