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