# Advancing Wheat Crop Analysis: A Survey of Deep Learning Approaches Using Hyperspectral Imaging

arXiv: https://arxiv.org/abs/2505.00805 · HTML: https://mlsanity.com/paper/2505.00805/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 167 references that are in the map.
- First posted on arXiv 1 May 2025.

## Builds on (most cited first, 14 of 14)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification](https://mlsanity.com/paper/1902.06701/) (2019)
- [Going Deeper with Contextual CNN for Hyperspectral Image Classification](https://mlsanity.com/paper/1604.03519/) (2016)
- [MambaHSI: Spatial-Spectral Mamba for Hyperspectral Image Classification](https://mlsanity.com/paper/2501.04944/) (2025)
- [Global Wheat Head Detection (GWHD) dataset: a large and diverse dataset of high resolution RGB labelled images to develop and benchmark wheat head detection methods](https://mlsanity.com/paper/2005.02162/) (2020)
- [Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images](https://mlsanity.com/paper/1812.00645/) (2018)
- [Binary Change Guided Hyperspectral Multiclass Change Detection](https://mlsanity.com/paper/2112.04493/) (2021)
- [Deep Learning Techniques for Hyperspectral Image Analysis in Agriculture: A Review](https://mlsanity.com/paper/2304.13880/) (2023)
- [Dimensionality Reduction of Hyperspectral Imagery Based on Spatial-spectral Manifold Learning](https://mlsanity.com/paper/1812.09530/) (2018)
- [HyperNet: Self-Supervised Hyperspectral Spatial-Spectral Feature Understanding Network for Hyperspectral Change Detection](https://mlsanity.com/paper/2207.09634/) (2022)
- [Adaptive DropBlock Enhanced Generative Adversarial Networks for Hyperspectral Image Classification](https://mlsanity.com/paper/2201.08938/) (2022)
- [Unsupervised Diffusion and Volume Maximization-Based Clustering of Hyperspectral Images](https://mlsanity.com/paper/2203.09992/) (2022)
- [Semi-supervised Superpixel-based Multi-Feature Graph Learning for Hyperspectral Image Data](https://mlsanity.com/paper/2104.13268/) (2021)
- [Aerial hyperspectral imagery and deep neural networks for high-throughput yield phenotyping in wheat](https://mlsanity.com/paper/1906.09666/) (2019)

## Built on by (most cited first, 2 of 2)

- [LoLA-SpecViT: Local Attention SwiGLU Vision Transformer with LoRA for Hyperspectral Imaging](https://mlsanity.com/paper/2506.17759/) (2025)
- [A Comparative Evaluation of Deep Learning Object Detection Models on a Real-World Multi-Plant Dataset from Africa](https://mlsanity.com/paper/2608.11053/) (2026)

## Abstract (arXiv)

As one of the most widely cultivated and consumed crops, wheat is essential to global food security. However, wheat production is increasingly challenged by pests, diseases, climate change, and water scarcity, threatening yields. Traditional crop monitoring methods are labor-intensive and often ineffective for early issue detection. Hyperspectral imaging (HSI) has emerged as a non-destructive and efficient technology for remote crop health assessment. However, the high dimensionality of HSI data and limited availability of labeled samples present notable challenges. In recent years, deep learning has shown great promise in addressing these challenges due to its ability to extract and analysis complex structures. Despite advancements in applying deep learning methods to HSI data for wheat crop analysis, no comprehensive survey currently exists in this field. This review addresses this gap by summarizing benchmark datasets, tracking advancements in deep learning methods, and analyzing key applications such as variety classification, disease detection, and yield estimation. It also highlights the strengths, limitations, and future opportunities in leveraging deep learning methods for HSI-based wheat crop analysis. We have listed the current state-of-the-art papers and will continue tracking updating them in the following https://github.com/fadi-07/Awesome-Wheat-HSI-DeepLearning.