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

Automated disease classification of radiology images has been emerging as a
promising technique to support clinical diagnosis and treatment planning.
Unlike generic image classification tasks, a real-world radiology image
classification task is significantly more challenging as it is far more
expensive to collect the training data where the labeled data is in nature
multi-label; and more seriously samples from easy classes often dominate;
training data is highly class-imbalanced problem exists in practice as well. To
overcome these challenges, in this paper, we propose a novel scheme of
Cross-Attention Networks (CAN) for automated thoracic disease classification
from chest x-ray images, which can effectively excavate more meaningful
representation from data to boost the performance through cross-attention by
only image-level annotations. We also design a new loss function that beyond
cross-entropy loss to help cross-attention process and is able to overcome the
imbalance between classes and easy-dominated samples within each class. The
proposed method achieves state-of-the-art results.

本研究探讨了在医疗诊断和治疗计划中支持临床的自动疾病分类的技术，并提出了一种新颖的交叉注意力网络方案（Cross-Attention Networks）来分类胸透图像以提高性能，同时结合新的损失函数，克服了数据不平衡的问题，并取得了最先进的结果。