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

arXiv: https://arxiv.org/abs/2306.09418 · HTML: https://mlsanity.com/paper/2306.09418/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 90 references that are in the map.
- First posted on arXiv 15 Jun 2023.

## Builds on (most cited first, 9 of 9)

- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [Squeeze-and-Excitation Networks](https://mlsanity.com/paper/1709.01507/) (2017)
- [Learning Deep Features for Discriminative Localization](https://mlsanity.com/paper/1512.04150/) (2015)
- [Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps](https://mlsanity.com/paper/1312.6034/) (2013)
- [An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture](https://mlsanity.com/paper/2006.01228/) (2020)
- [The TerraByte Client: providing access to terabytes of plant data](https://mlsanity.com/paper/2203.13691/) (2022)
- [Explaining hyperspectral imaging based plant disease identification: 3D CNN and saliency maps](https://mlsanity.com/paper/1804.08831/) (2018)
- [Band selection in RKHS for fast nonlinear unmixing of hyperspectral images](https://mlsanity.com/paper/1503.02090/) (2015)
- [Fusarium head blight detection, spikelet estimation, and severity assessment in wheat using 3D convolutional neural networks](https://mlsanity.com/paper/2303.05634/) (2023)

## Built on by (most cited first, 1 of 1)

- [HyKey: Hyperspectral Keypoint Detection and Matching in Minimally Invasive Surgery](https://mlsanity.com/paper/2604.17446/) (2026)

## Abstract (arXiv)

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.