基于非无人机高光谱图像的病害与缺陷作物3D卷积神经网络分类技术综述
A comprehensive review of 3D convolutional neural network-based classification techniques of diseased and defective crops using non-UAV-based hyperspectral images
AI总结：
该综述针对作物高光谱图像分类中数据集不足、维度灾难及1D/2D-CNN特征提取局限等问题，梳理了3D-CNN架构及配套深度学习流程，并探讨了相关开放研究方向与挑战。
摘要：
高光谱成像（HSI）是一种非破坏性、非接触式技术，可提供有关物体结构与成分的宝贵信息，能够捕获农作物化学与物理特性的详细信息。与多光谱或RGB成像方法相比，HSI的光谱范围更广，是监测作物健康状况与生产力的更有效工具。随着这种成像工具在农业技术中的应用，研究人员能够更精准地解决农业领域中病害与缺陷作物的检测问题，从而在田间作物进入受损且难以恢复的生长阶段前，实施最合适、最精准的农业解决方案，如灌溉与施肥。尽管HSI能为研究对象提供有价值的见解，但目前用于作物评估的HSI数据集数量有限，构成了发展瓶颈。由于每个高光谱数据立方体中包含丰富的光谱与空间信息，维度灾难是另一项挑战。基于1D-CNN和2D-CNN的现有先进方法难以高效提取光谱与空间信息。另一方面，基于3D-CNN的模型通过同时利用光谱与空间特征，在实现更优的分类与检测结果方面展现出巨大潜力。尽管基于3D-CNN的模型具有显著优势，但它们在该研究领域用于分类的应用仍然有限。本文旨在填补这一空白，综述用于病害与缺陷作物高光谱图像分类的3D-CNN架构，以及包含预处理和结果可视化在内的典型深度学习流程。此外，我们还讨论了将3D-CNN应用于HSI数据时的开放研究方向与挑战。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
