Automated disease, weed and crop classification with computer vision will be
invaluable in the future of agriculture. However, existing model architectures
like ResNet, EfficientNet and ConvNeXt often underperform on smaller,
specialised datasets typical of such projects. We address this gap with
informed data collection and the development of a new CNN architecture,
PhytNet. Utilising a novel dataset of infrared cocoa tree images, we
demonstrate PhytNet's development and compare its performance with existing
architectures. Data collection was informed by analysis of spectroscopy data,
which provided useful insights into the spectral characteristics of cocoa
trees. Such information could inform future data collection and model
development. Cocoa was chosen as a focal species due to the diverse pathology
of its diseases, which pose significant challenges for detection. ResNet18
showed some signs of overfitting, while EfficientNet variants showed distinct
signs of overfitting. By contrast, PhytNet displayed excellent attention to
relevant features, no overfitting, and an exceptionally low computation cost
(1.19 GFLOPS). As such PhytNet is a promising candidate for rapid disease or
plant classification, or precise localisation of disease symptoms for
autonomous systems.

自动疾病分类、杂草分类和作物分类是农业未来中非常重要的一部分，通过计算机视觉进行自动化，然而现有的模型架构如 ResNet、EfficientNet 和 ConvNeXt 在类似项目的小规模专门数据集上常常表现不佳，我们通过数据采集和开发新的 CNN 架构 PhytNet 来填补这一空白，利用红外可可树图像的新数据集，展示 PhytNet 的发展并将其性能与现有架构进行比较，数据采集受到光谱分析数据的启发，该数据提供了有关可可树的光谱特性的有用见解，这些信息可以指导未来的数据采集和模型发展，鉴于可可树病害的多样性，因此选择可可树作为焦点物种是因为其病害存在较大的挑战，ResNet18 显示出一些过拟合迹象，而 EfficientNet 变体则显示出明显的过拟合迹象，相比之下，PhytNet 对相关特征具有良好的关注度，没有过拟合，而且计算成本异常低（1.19 GFLOPS），因此 PhytNet 是快速疾病或植物分类，或疾病症状的精确定位自主系统的有希望的候选方案。

PhytNet：定制植物数据的适应性卷积神经网络

PhytNet -- Tailored Convolutional Neural Networks for Custom Botanical  Data

In this paper, we present an efficient solution for weed classification in
agriculture. We focus on optimizing model performance at inference while
respecting the constraints of the agricultural domain. We propose a Quantized
Deep Neural Network model that classifies a dataset of 9 weed classes using
8-bit integer (int8) quantization, a departure from standard 32-bit floating
point (fp32) models. Recognizing the hardware resource limitations in
agriculture, our model balances model size, inference time, and accuracy,
aligning with practical requirements. We evaluate the approach on ResNet-50 and
InceptionV3 architectures, comparing their performance against their int8
quantized versions. Transfer learning and fine-tuning are applied using the
DeepWeeds dataset. The results show staggering model size and inference time
reductions while maintaining accuracy in real-world production scenarios like
Desktop, Mobile and Raspberry Pi. Our work sheds light on a promising direction
for efficient AI in agriculture, holding potential for broader applications.
Code: this https URL

本文提出了一种有效的农业杂草分类解决方案，该方案在推断时优化了模型性能，同时尊重农业领域的约束。我们通过使用 8 位整数（int8）量化的量化深度神经网络模型对包含 9 个杂草类别的数据集进行分类，这与标准的 32 位浮点（fp32）模型有所不同。通过在深度杂草数据集上应用迁移学习和微调，我们将此方法在 ResNet-50 和 InceptionV3 架构上进行了评估，并将其性能与其 int8 量化版本进行了比较。结果显示，在保持准确性的同时，模型大小和推断时间大幅减少，适用于台式机、移动设备和树莓派等实际生产场景。我们的工作为农业中的高效人工智能指明了一个有前景的方向，具有更广泛的应用潜力。

QWID：量化杂草识别深度神经网络

QWID: Quantized Weed Identification Deep neural network

Image classification is a crucial task in modern weed management and crop
intervention technologies. However, the limited size, diversity, and balance of
existing weed datasets hinder the development of deep learning models for
generalizable weed identification. In addition, the expensive labelling
requirements of mainstream fully-supervised weed classifiers make them cost-
and time-prohibitive to deploy widely, for new weed species, and in
site-specific weed management. This paper proposes a novel method for Weed
Contrastive Learning through visual Representations (WeedCLR), that uses
class-optimized loss with Von Neumann Entropy of deep representation for weed
classification in long-tailed datasets. WeedCLR leverages self-supervised
learning to learn rich and robust visual features without any labels and
applies a class-optimized loss function to address the class imbalance problem
in long-tailed datasets. WeedCLR is evaluated on two public weed datasets:
CottonWeedID15, containing 15 weed species, and DeepWeeds, containing 8 weed
species. WeedCLR achieves an average accuracy improvement of 4.3\% on
CottonWeedID15 and 5.6\% on DeepWeeds over previous methods. It also
demonstrates better generalization ability and robustness to different
environmental conditions than existing methods without the need for expensive
and time-consuming human annotations. These significant improvements make
WeedCLR an effective tool for weed classification in long-tailed datasets and
allows for more rapid and widespread deployment of site-specific weed
management and crop intervention technologies.

使用深度学习模型对长尾数据集中的杂草进行分类，提出了一种基于类优化损失和深层表示的 WeedCLR 方法，在无标签数据上使用自监督学习来学习视觉特征，并通过类优化损失函数解决长尾数据集中的类别不平衡问题，相较于先前的方法在不同环境条件下展现出更好的泛化能力和稳健性，无需昂贵且耗时的人工标注，可广泛应用于场地特定的杂草管理和作物干预技术。