Polyculture farming has environmental advantages but requires substantially
more pruning than monoculture farming. We present novel hardware and algorithms
for automated pruning. Using an overhead camera to collect data from a physical
scale garden testbed, the autonomous system utilizes a learned Plant
Phenotyping convolutional neural network and a Bounding Disk Tracking algorithm
to evaluate the individual plant distribution and estimate the state of the
garden each day. From this garden state, AlphaGardenSim selects plants to
autonomously prune. A trained neural network detects and targets specific prune
points on the plant. Two custom-designed pruning tools, compatible with a
FarmBot gantry system, are experimentally evaluated and execute autonomous cuts
through controlled algorithms. We present results for four 60-day garden
cycles. Results suggest the system can autonomously achieve 0.94 normalized
plant diversity with pruning shears while maintaining an average canopy
coverage of 0.84 by the end of the cycles. For code, videos, and datasets, see
this https URL.

该研究提出了一种新型硬件和算法，利用物理规模的实验室和神经网络等技术相结合，实现了对不同作物进行自动化修剪的目标。该系统能够自动评估植物分布，选择要裁剪的部位，并根据训练好的神经网络自动裁剪，维护植物多样性和覆盖率。实验结果证明，系统能够在保持高平均枝叶覆盖率的同时，实现高度规范化的多植物分布。

多种植物自动修剪

Automated Pruning of Polyculture Plants

Deep neural networks (DNNs) have achieved unprecedented success in the field
of artificial intelligence (AI), including computer vision, natural language
processing and speech recognition. However, their superior performance comes at
the considerable cost of computational complexity, which greatly hinders their
applications in many resource-constrained devices, such as mobile phones and
Internet of Things (IoT) devices. Therefore, methods and techniques that are
able to lift the efficiency bottleneck while preserving the high accuracy of
DNNs are in great demand in order to enable numerous edge AI applications. This
paper provides an overview of efficient deep learning methods, systems and
applications. We start from introducing popular model compression methods,
including pruning, factorization, quantization as well as compact model design.
To reduce the large design cost of these manual solutions, we discuss the
AutoML framework for each of them, such as neural architecture search (NAS) and
automated pruning and quantization. We then cover efficient on-device training
to enable user customization based on the local data on mobile devices. Apart
from general acceleration techniques, we also showcase several task-specific
accelerations for point cloud, video and natural language processing by
exploiting their spatial sparsity and temporal/token redundancy. Finally, to
support all these algorithmic advancements, we introduce the efficient deep
learning system design from both software and hardware perspectives.

本文概述了高效深度学习的方法、系统和应用，包括流行的模型压缩方法、自动化模型设计和用户自定义的基于设备的训练，以及各种针对特定任务和空间 - 时间冗余的加速技术和系统设计。