Hyperspectral imaging (HSI) is a non-destructive and contactless technology
that provides valuable information about the structure and composition of an
object. It can capture detailed information about the chemical and physical
properties of agricultural crops. Due to its wide spectral range, compared with
multispectral- or RGB-based imaging methods, HSI can be a more effective tool
for monitoring crop health and productivity. With the advent of this imaging
tool in agrotechnology, researchers can more accurately address issues related
to the detection of diseased and defective crops in the agriculture industry.
This allows to implement the most suitable and accurate farming solutions, such
as irrigation and fertilization before crops enter a damaged and
difficult-to-recover phase of growth in the field. While HSI provides valuable
insights into the object under investigation, the limited number of HSI
datasets for crop evaluation presently poses a bottleneck. Dealing with the
curse of dimensionality presents another challenge due to the abundance of
spectral and spatial information in each hyperspectral cube. State-of-the-art
methods based on 1D- and 2D-CNNs struggle to efficiently extract spectral and
spatial information. On the other hand, 3D-CNN-based models have shown
significant promise in achieving better classification and detection results by
leveraging spectral and spatial features simultaneously. Despite the apparent
benefits of 3D-CNN-based models, their usage for classification purposes in
this area of research has remained limited. This paper seeks to address this
gap by reviewing 3D-CNN-based architectures and the typical deep learning
pipeline, including preprocessing and visualization of results, for the
classification of hyperspectral images of diseased and defective crops.
Furthermore, we discuss open research areas and challenges when utilizing
3D-CNNs with HSI data.

本文综述了使用基于 3D-CNN 的架构与典型的深度学习流程进行检测病害和有缺陷作物的高光谱图像分类的方法，并讨论了使用 HSI 数据时 3D-CNN 存在的挑战及开放性研究领域。

基于 3D 卷积神经网络的农作物病害缺陷分类技术综述，使用非无人机高光谱图像

A comprehensive review of 3D convolutional neural network-based  classification techniques of diseased and defective crops using non-UAV-based  hyperspectral images

The aim of this paper is to map agricultural crops by classifying satellite
image time series. Domain experts in agriculture work with crop type labels
that are organised in a hierarchical tree structure, where coarse classes (like
orchards) are subdivided into finer ones (like apples, pears, vines, etc.). We
develop a crop classification method that exploits this expert knowledge and
significantly improves the mapping of rare crop types. The three-level label
hierarchy is encoded in a convolutional, recurrent neural network (convRNN),
such that for each pixel the model predicts three labels at different level of
granularity. This end-to-end trainable, hierarchical network architecture
allows the model to learn joint feature representations of rare classes (e.g.,
apples, pears) at a coarser level (e.g., orchard), thereby boosting
classification performance at the fine-grained level. Additionally, labelling
at different granularity also makes it possible to adjust the output according
to the classification scores; as coarser labels with high confidence are
sometimes more useful for agricultural practice than fine-grained but very
uncertain labels. We validate the proposed method on a new, large dataset that
we make public. ZueriCrop covers an area of 50 km x 48 km in the Swiss cantons
of Zurich and Thurgau with a total of 116'000 individual fields spanning 48
crop classes, and 28,000 (multi-temporal) image patches from Sentinel-2. We
compare our proposed hierarchical convRNN model with several baselines,
including methods designed for imbalanced class distributions. The hierarchical
approach performs superior by at least 9.9 percentage points in F1-score.

本文旨在通过分类卫星图像时间序列来绘制农作物的地图，开发了一种利用专家知识的农作物分类方法，并将三级标签层次结构编码至卷积循环神经网络中，该神经网络能够同时学习罕见珍贵类别在更宏观层次上的特征表示，提高了细粒度分类的性能，并能够根据置信度和农业实践需求进行输出调整。在新的公共数据集 ZueriCrop 上的实验表明，相比于其他基于不平衡类分布的方法，该分层式卷积循环神经网络模型的分类性能至少提高了 9.9 个百分点。